Google Cloud

Gartner has recognized Google as a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems for the fifth year in a row. Google is positioned furthest in vision among all vendors evaluated; we believe this is a testament to Google’s innovations that help tens of thousands of Data Cloud customers across all industries build applications faster and unlock deeper insights through a unified data and AI approach.

AI is driving the need for better data management

A Gartner survey finds 61% of organizations are evolving their D&A (data and analytics) operating model because of AI technologies.¹ With AI powering today’s biggest innovations, customers need a simpler way to unify and integrate their data. This is particularly true for businesses with a multi-layered data foundation, including various data models such as graph and document, semi-structured and unstructured data, and a variety of file formats and standards. 

At the same time, organizations need AI-assisted tools and agents, as well as the ability to build these capabilities for their customers and employees. Simply put, they want to be innovators, not just integrators. And lastly, governance across the data estate and AI models is becoming even more critical as gen AI opens up huge opportunities, but also risks when not grounded in truth with their enterprise data.

Driving innovation with Google’s AI-powered Data Cloud

Google provides a unified, intelligent and open Data Cloud that’s designed to address the most pressing data challenges facing customers today. Google’s Data Cloud is built on planet-scale infrastructure with AI at its core, empowering teams to unlock deeper insights and build applications faster. With unmatched reliability, performance, security, and global scale, it enables organizations to operate efficiently and solve their toughest data problems.

Leveraging Google’s deep expertise and decades of innovations, we have built a pre-integrated Data Cloud that seamlessly unifies data services, enabling them to work better together while delivering unique intelligence into the platform. We are committed to being the most open platform, offering unmatched choice and flexibility to meet diverse customer needs. And that’s why we believe the Gartner recognition is based on Google’s completeness of vision and ability to execute.

We’re delivering on three key pillars: end-to-end unified data platform, accelerating innovation with AI-assistive and agentic experiences, and an open data ecosystem for the gen AI era. Let’s learn more. 

End-to-end unified data platform

The unified data platform simplifies data management and enhances business outcomes by unifying data management, governance and analysis. We’ve built deep integration from data to AI, spanning AI infrastructure, foundation models, multiple storage options, and multiple engines. This unified multimodal data foundation enables multiple engines to work seamlessly together for analytics, streaming, machine learning, and AI. 

Some recent advancements include:

• BigQuery Unified Platform: We’re enhancing BigQuery’s multimodal data processing capabilities with support for structured and unstructured data, as well as diverse formats, including fully managed Iceberg tables, Delta, and Hudi. We introduced native BigQuery support for Apache Spark alongside SQL, offering flexibility in engine choice. BigQuery metastore now supports OSS engines such as Apache Spark and Flink. And with BigQuery universal catalog, we’ve simplified governance for the entire BigQuery platform, providing automated data discovery, curation and management at scale.
• Multi-model data support: With the introduction of new Spanner Graph, full-text search and vector search capabilities, we’ve evolved Spanner to a multi-model database with intelligent capabilities that enable customers to deliver a new class of AI-enabled applications — all built on top of a globally consistent, virtually unlimited scale database that offers up to 99.999% availability.
• Unified operational and analytical systems: We’re unifying operational and analytical workloads to drive real-time intelligence. With BigQuery and Spanner Data Boost, customers can now query live operational data directly without compromising performance of their operational systems. And with Reverse ETL through BigQuery and Bigtable integrations, customers can push those insights back into operational systems, closing the loop and driving immediate action. 
• Streaming and real-time intelligence: BigQuery empowers real-time insights with robust streaming and real-time support. We introduced continuous queries to analyze data as it streams into BigQuery, unlocking insights and enabling truly event-driven applications. And, with BigQuery Managed Service for Apache Kafka, organizations can easily ingest and process real-time data streams.

Accelerating innovation with AI-assistive and agentic experiences

The rise of generative AI has fueled a surge in data and machine learning operations as enterprises activate their data to unlock new possibilities. BigQuery, for example, has seen an 80% increase in machine learning operations in six months with customers running tens of millions of prediction and training queries every month, and nearly 7x growth in the use of LLMs for model inference in 2024. For operational data, AlloyDB supercharges PostgreSQL vector search and can scale to over a billion vectors with its ScaNN index, also available in BigQuery, while Spanner supports vector searches scaling to more than 10B vectors. 

A few of the latest innovations include:

• Activating AI on business data: We’re integrating AI deeper into our Data Cloud. Tight integration with Vertex AI provides access to 160+ Google and open-source foundation models, so businesses can perform LLM inferencing, ground, and fine-tune models directly within BigQuery, AlloyDB, Spanner, and Cloud SQL using their enterprise data. Document AI, Vision AI, and speech-to-text APIs enable scalable unstructured data analysis, with governance over multimodal data via object tables within BigQuery.
• Vector search across Google’s Data Cloud: We are turbocharging vector search workloads, helping you to quickly build intelligent, AI applications with your own trusted, enterprise data for any customer or industry use case. Our built-in vector support across all databases including BigQuery, AlloyDB, Cloud SQL, Spanner, and more, letting you store and search vector embeddings with speed and ease without moving data or managing additional systems. We’re also deeply integrating with third-party orchestration frameworks like LangChain and LlamaIndex for building increasingly sophisticated enterprise apps and continue to innovate with Google’s innovative ScaNN algorithm, bringing 12 years of Google research to our Data Cloud customers.
• Gemini across Google’s Data Cloud: BigQuery is advancing automation with Gemini data agents, enabling trusted and governed workflows across data engineering, governance, and analytics. Recently, we launched a conversational analytics agent where customers can ask questions of their BigQuery data in natural language; this capability is also now available as a conversational analytics API for customers to build their own experiences. In addition, Gemini in databases & Gemini in BigQuery allow users to have access to an AI-powered assistant that provides them with fleet management capabilities, workload optimization, data insights & exploration. Additionally administrators get proactive recommendations for managing, securing, governing and optimizing databases, as well as code and schema conversions in Database Migration Service to supercharge legacy database modernization projects.

Open data ecosystem for the gen AI era

We are committed to being the most open cloud provider, empowering data teams to build modern, data-driven applications wherever their workloads reside. By supporting open source and open standards including Iceberg, Delta, Hudi, MySQL, Valkey, and PostgreSQL, Google Cloud enables teams to build Lakehouse architectures with fully managed services compatible with popular open-source engines and models. 

We partner closely with technology providers to deliver choices to our customers across a robust partner ecosystem including solutions such as Oracle Database@Google Cloud, Aiven for AlloyDB Omni and Anthropic API to BigQuery integration. We also continue to support customers on their multicloud journeys with solutions such as BigQuery Omni and AlloyDB Omni. 

Bringing cutting-edge technology to customers faster

As a Leader in this report for the past five consecutive years, we have witnessed remarkable growth with customers. For example: 

• APEX Fintech Solutions leveraged AlloyDB to achieve a 50% reduction in processing time, enabling margin calculations for 100,000 accounts in just one minute — a significant improvement in efficiency and scalability. 
• Bayer migrated to AlloyDB for PostgreSQL to streamline data operations, centralize solutions and improve collaboration across the company, which helped reduce response times by over 50% on average and increased throughput by 5x compared to its previous PostgreSQL solution. 

What’s next

We are excited to work with you on your journey to the cloud in this gen AI era. To learn more about our placement, download the complimentary 2024 Gartner Magic Quadrant for Cloud Database Management Systems report.

^{1. Gartner, Gartner Press Release, “Gartner Survey Finds 61% of Organizations Are Evolving Their D&A Operating Model Because of AI Technologies”, April 29, 2024,}

^{Gartner, Magic Quadrant for Cloud Database Management Systems, Henry Cook, Ramke Ramakrishnan, Xingyu Gu, Aaron Rosenbaum, Masud Miraz, 18 December, 2024.}

^{Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.}

^{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.}

^{This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Google.}

Developers, it’s time to take your skills to the ballpark! Are you ready to step out of the bleachers and onto the field where code collides with my (and America’s) favorite pastime? Get ready to swing for the fences at the Google Cloud X MLB Hackathon — powered by the groundbreaking Gemini family of models. Submit your projects by February 4, 2025 for a chance to win big.

Batter up!

What’s in your equipment bag? Get ready to use:

• Gemini: Google’s most capable AI models, built from the ground up for multimodality. We’re talking text, code, images, audio, and video — all understood and processed seamlessly. Plus, Gemini boasts lightning-fast response times and the power to handle massive datasets, so you can focus on building epic fan experiences.

• Real MLB datasets: You’ll have access to a treasure trove of historical game data, stats and content. This is your chance to mine insights and unlock the hidden stories within America’s pastime.

• Google Cloud: Build, deploy, and scale your project with the power of Google Cloud Platform. We’re talking Vertex AI, BigQuery, Cloud Functions, and the full suite of tools you need to bring your vision to life.

Five challenges. Step up to the plate

Bring innovation to the baseball fan experience in one of five ways:

1. Wild card – fan experience: Think outside the batter’s box! This is your chance to knock our socks off with any project that uses the provided datasets to redefine how fans experience the game we love.

2. Personalized fan highlights: Ditch the one-size-fits-all highlights! Build a system that curates personalized audio, video, and text digests based on a fan’s favorite teams, players, and even preferred language.

3. Real-time “tool tips”: Turn casual viewers into armchair analysts with an interactive application that delivers real-time strategic insights. Explain the “why” behind every steal, strikeout, and home run as it happens.

4. Generate Statcast data from old videos: Give classic games the Statcast treatment! Use computer vision to extract key metrics (pitch speed, exit velocity, etc.) from archival game footage. Think Moneyball meets the digital age.

5. Prospect prediction: Can your code spot the next MLB superstar? Build a platform that analyzes prospect data to project future MLB potential and career impact, leveraging historical comparisons and predictive modeling.

Be an all-star, see the All-Stars

Get ready to cash in on some serious prizes:

• Overall Grand Prize: $20,000, $5,000 in Google Cloud credits, a chance to deliver a keynote demo at Google Cloud Next ’25 (April 9-11, 2025), tickets and travel to the conference, tickets to the 2025 MLB® All-Star Game® presented by Mastercard, a meeting with a Google team member, and social media promotion.

• Best of Challenge Prizes (for each challenge): $5,000, $2,500 in Google Cloud credits, a chance to demo at Google Cloud Next ’25, tickets to the conference, a virtual meeting with a Google team member, and social media promotion.

Ready to code the future of baseball?

Head over to next2025challenge.devpost.com to learn more, review the rules, and register. It’s time to prove that your code is ready for the big leagues. See you on the diamond!

^{NPN. Must be age of majority. Ends 5:00 p.m. ET on 4 Feb 2025 See goo.gle/mlbhackathon. Major League Baseball trademarks and copyrights are used with permission of Major League Baseball. Visit MLB.com.}

If you’re a regular reader of this blog, you know that 2024 was a busy year for Google Cloud. From AI to Zero Trust, and everything in between, here’s a chronological recap of our top blogs of 2024, according to readership. You’ll probably see some favorites on this list, and discover a few that you missed the first time around. 

January

We started the new year strong, removing data transfer fees for anyone moving data off of our platform. Translation: Anyone doing cool things on Google Cloud (like using generative AI to analyze their microservices deployment) is doing it because they want to, not because they have to. And in business news, we shared how to make the most out of your data and AI in the coming year. 

1. Cloud switching just got easier: Removing data transfer fees when moving off Google Cloud

2. Figuring out microservices running on your GKE cluster with help from Duet AI

3. Transform: Five ways your data can thrive with generative AI in 2024

February

From local GPUs, to model libraries, to distributed system design, the second month of 2024 was the first of many to come where AI topics dominated the charts. Our Transform team explored gen AI’s impact on various industries.

1. No GPU? No problem. localllm lets you develop gen AI apps on local CPUs

2. Google Cloud expands access to Gemini models for Vertex AI customers

3. Coming of age in the fifth epoch of distributed computing, accelerated by machine learning

4. Your RAGs powered by Google Search technology, part 1

5. Transform: Putting AI to work: Where gen AI is impacting industries in 2024

March

If it wasn’t already obvious, this month’s top-read blogs showed that our core audience is developers pushing the boundaries of innovation. Business leaders, meanwhile, read about best practices for securely deploying AI on Google Cloud. 

1. Domain-specific AI apps: A three-step design pattern for specializing LLMs

2. Announcing Anthropic’s Claude 3 models in Google Cloud Vertex AI

3. Transform: To securely build AI on Google Cloud, follow these best practices

April

Watch out, here comes Google Cloud Next, where we made a record 218 announcements; the top three are listed here. Readers were also keen to hear about how Citadel Securities built out a research platform on Google Cloud. 

1. Introducing Google Axion Processors, our new Arm-based CPUs

2. Powering Google Cloud with Gemini

3. BigQuery is now your single, unified AI-ready data platform

4. Transform: How Citadel Securities is reimagining quantitative research on the cloud

May

We don’t always get it right, but when there’s a problem, we’re committed to providing you with accurate, timely information with which to make your own assessments. We’re also committed to making you, and customers like McLaren Racing, go really, really fast when developing new AI-based applications.  

1. Sharing details on a recent incident impacting one of our customers

2. RSA ’24: Introducing Google Threat Intelligence: Actionable threat intelligence at Google scale

3. Announcing Trillium, the sixth generation of Google Cloud TPU

4. Improving connectivity and accelerating economic growth across Africa with new investments

5. Transform: AI at the track: How McLaren Racing leverages terabytes of data each race for a winning edge

June

Whether you wanted to modernize your databases, deliver higher system reliability, create really cool AI-powered apps, or learn how legendary companies tackle data management, the Google Cloud blog was your go-to source midway through the year. 

1. Accelerating cloud transformation with Google Cloud and Oracle

2. Free to be SRE, with this systems engineering syllabus

3. Announcing Anthropic’s Claude 3.5 Sonnet on Vertex AI, providing more choice for enterprises

4. Google Cloud expands grounding capabilities on Vertex AI

5. Transform: We are Legend: How Goldman Sachs’ open-source data platform democratizes information

July

We talk a lot about “meeting customers where they are.” Sometimes that means a disaster zone, a remote research station, or a truck cruising down the highway. Over on Transform, you read about the history of our custom Axion and TPU chips. 

1. Bringing cloud and AI capabilities to the tactical edge: Google Distributed Cloud air-gapped appliance is generally available

2. VMware Cloud Foundation on Google Cloud VMware Engine: 20% lower price and up to 40% in migration incentives

3. Transform: Why Google keeps building custom silicon: The story behind Axion

4. Transform: TPU transformation: A look back at 10 years of our AI-specialized chips

August

Just when you thought you knew how to run AI inference, your available graph database options, or the name of Google Cloud’s event-driven programming service, we went and changed things up. We like to keep you on your toes 😉 And business readers got a first look at AI agents — more to come on this. 

1. Run your AI inference applications on Cloud Run with NVIDIA GPUs

2. GenOps: learning from the world of microservices and traditional DevOps

3. Real-time in no time: Introducing BigQuery continuous queries for up-to-the-minute insights

4. Introducing Spanner Graph: Graph databases reimagined

5. Cloud Functions is now Cloud Run functions — event-driven programming in one unified serverless platform

6. Transform: So much more than gen AI: Meet all the other AI making AI agents possible

September

You’ve been generating (and storing) business data for years. Now, we’re making it easier for you to make sense of, and actually use, that data. Speaking of using data, the Transform team compiled a jaw-dropping list of the real-world ways customers are using gen AI in their organizations. 

1. Chat with your business data – Conversational Analytics comes to Gemini in Looker

2. Find key insights faster with a new contribution analysis model in BigQuery ML

3. Transform: 185 real-world gen AI use cases from the world’s leading organizations

October

According to this month’s most-read blog, 75% of you rely on AI for at least one daily professional responsibility, including code writing, information summarization, and code explanation, and experience “moderate” to “extreme” productivity gains. So it was no big surprise that business leaders wanted to read about how to develop an AI strategy. 

1. Highlights from the 10th DORA report

2. Gemini models are coming to GitHub Copilot

3. Revolutionizing SQL with pipe syntax, now available in BigQuery and Cloud Logging

4. BigQuery tables for Apache Iceberg: optimized storage for the open lakehouse

5. Transform: How to build an effective AI strategy

November

Not content to hold the existing record for most nodes in a Kubernetes cluster (15,000), we went ahead and more than quadrupled it, to the delight of AI unicorns. But whether you work for an AI unicorn, or just a plain old zebra, all Google Cloud users needs to start using multi-factor authentication next year, as well as learn how to avoid comman AI pitfalls. 

1. 65,000 nodes and counting: Google Kubernetes Engine is ready for trillion-parameter AI models

2. Mandatory MFA is coming to Google Cloud. Here’s what you need to know

3. Transform: Unlocking gen AI success: Five pitfalls every executive should know

December

We’re closing out the year on an AI highnote, with the availability of amazing new image and video generation models, as well as the new Trillium TPU, which Google used to train Gemini 2.0, our most capable AI model… yet. Be on the lookout for how these technologies — and many others — will reshape your industry and how you work in the coming year. 

1. Veo and Imagen 3: Announcing new video and image generation models on Vertex AI

2. Announcing the general availability of Trillium, our sixth-generation TPU

3. Introducing Google Agentspace: Bringing AI agents and AI-powered search to enterprises

4. Transform: AI’s impact on industries in 2025

To all our loyal readers and customers, thanks for an amazing 2024, and here’s to an even more exciting and productive 2025!

Spanner is Google’s always-on, virtually unlimited database that powers planet-scale applications like Gmail, YouTube, and Google Photos. Outside of Google, Spanner powers demanding workloads for household brands like Yahoo!, The Home Depot, Wayfair, and Pokémon Go. Today, Spanner handles over 4 billion queries per second at peak and more than 15 exabytes of data, with five 9s of availability, plus global consistency.

Since we first discussed it in 2012, Spanner has evolved from a groundbreaking distributed SQL database into a versatile, intelligent innovation platform. 2024 was a big year for Spanner, with multiple launches that expanded its functional capabilities, pushed the envelope on price-performance, re-architected it for best-in-class reliability and security, and enhanced the developer experience. Here is a recap of Spanner’s biggest innovations of the year and how you can benefit from them.

1. Multi-model — one database, many possibilities

With the launch of Spanner Graph, full-text search and vector search, Spanner went from being a highly available, globally consistent and scalable database, to a multi-model database with intelligent, interoperable capabilities with which you can build AI-enabled applications. Unlike other multi-model databases on the market, Spanner offers a true multi-model experience that allows interoperability between different data models without downtime. 

Spanner’s multi-model support lets you consolidate databases, saving on costs and reducing operational overhead, governance, and security touchpoints, while its interoperability eliminates data movement for a “true ZeroETL” experience with consistent data across all models. This helps enable use cases like near-real-time fraud detection, supply chain optimization, or product recommendations. 

2. Improving price-performance  

Spanner’s price-performance lets you dream big, start small (for as little as $65/mo), and scale linearly with no cliffs. In 2022, we increased the storage per node from 2T to 4T, and in 2023 we built on this with a 50% increase in throughput and a 2.5X increase in storage at no additional cost. 

This year, with the launch of new multi-model capabilities, we wanted to make it simple and cost effective for you to use these capabilities without charging incrementally for every new feature. The result was Spanner editions, an intuitive, tier-based pricing approach that offers different capabilities at various price points to fit you diverse needs and budgets, all while providing flexibility, cost transparency and additional cost saving opportunities.

3. A new home for your Cassandra workloads

The Cassandra NoSQL database is prized for its speed and scalability. It also has limitations, such as limited support for complex queries and difficulty modeling intricate relationships. Spanner combines the scalability and availability of NoSQL with the strong consistency and relational model of traditional databases, for the best of both worlds. 

This year, we launched the Cassandra to Spanner Proxy Adapter, an open-source, plug-and-play tool that makes it easier than ever to move your Cassandra workload to Spanner with near-zero changes to your application logic. Customers like Yahoo! and Reltio are loving the ease of use of the Cassandra proxy adapter, and we’re excited to help customers be more successful with Cassandra on Spanner. 

4. Generative AI and the Spanner ecosystem

Over the past year, we’ve witnessed a remarkable shift in how organizations leverage generative AI. But gen AI comes with risk of hallucinations. We believe that transactional and analytical databases can help reduce these, bridging the gap between foundation models and enterprise gen AI apps. Here’s how: 

• Vector support: With vector support for Spanner, developers can perform similarity searches on vector embeddings stored in the database. Spanner vector search supports both exact KNN and approximate ANN searches, providing flexibilit for different workloads that leverage Google’s scalable nearest neighbor (ScaNN) algorithm, providing fast and accurate results, even on large datasets. Spanner now supports vector searches scaling to more than 10 billion vectors. Developers can combine vector searches with regular SQL and graph GQL queries to power use-cases like RAG applications. 

• BigQuery and Spanner better together: New, groundbreaking integrations between Spanner and BigQuery help businesses connect operational and analytical workloads, to unlock valuable insights and drive better decision-making. Spanner external datasets in BigQuery allows you to query transactional data residing in Spanner directly within BigQuery, without needing to move or duplicate data. Spanner now also supports reverse ETL from BigQuery to export data from BigQuery to Spanner, so you can operationalize the analytical insights that BigQuery enables. 

5. Reliability, availability, security, and governance 

Spanner customers expect the highest levels of reliability, availability, security, and governance controls for their mission-critical workloads. This year, we launched support for dual-region configurations and geo-partitioning to help you improve your availability SLAs, improve application performance for multi-region workloads, and meet governance requirements.

• Dual-region support: Spanner dual-region configurations help meet local residency requirements while providing five 9s of availability and zero recovery-point objective (RPO) guarantees in geographies with only two regions. 

• Geo-partitioning: You can partition your table data at the row-level across the globe, to serve data closer to your users. With geo-partitioning, Spanner customers across industries like gaming, e-commerce, and financial services can provide their users reduced application latency, optimized costs, and data residency benefits such as storing sensitive user data within geographic jurisdictions.

Lastly, we introduced support for incremental and scheduled backups to make it easier and cheaper to back up your Spanner data. 

6.  The developer experience

At Google Cloud, we strive to make it ridiculously simple to build and manage applications built on our databases, including Spanner. 

• Protobuf improvements: Protocol Buffers, or protobuf, is a language-neutral way to encode and decode data structures for efficient transport and storage. You can now manage protobuf values in Spanner and access their fields using the dot operator in SQL, e.g., dimensions.size.width, without having to normalize into tables upfront. This dramatically simplifies writing queries that need to filter, group, or order by specific values within a protobuf. 

• Troubleshooting and Database Center support: Database Center is an AI-powered unified database management solution to monitor and manage diverse database services. This year, customers started to be able to use Database Center to manage their Spanner databases. We also added support for end-to-end tracing and client tracing to make it easier to troubleshoot performance issues. 

We are proud of what we have delivered for customers in 2024, and are excited to see the innovative solutions you are building on Spanner. Needless to say, we are just getting started and we have a lot more exciting capabilities lined up for 2025. 

Get started

Want to learn more about what makes Spanner unique and how it’s being used today? Try it yourself for free for 90-days or for as little as $65 USD/month for a production-ready instance that grows with your business without downtime or disruptive re-architecture.

Google Cloud’s Database Center provides a unified fleet management solution to help manage your databases at scale. In October 2024, Database Center was made available to all Google Cloud customers, with support for Cloud SQL, AlloyDB and Spanner engines.

Today we are expanding Database Center’s capabilities with the addition of Bigtable, Memorystore, and Firestore databases in preview. You now have a single, unified view where you can: 

• Gain a comprehensive view of your entire database fleet across all Google Cloud managed databases. No more silos of information or hunting through bespoke tools and spreadsheets.

• Proactively de-risk your database fleet with intelligent and actionable availability and data protection recommendations for Bigtable and Firestore databases.

• Optimize your database fleet with AI-powered assistance using a natural language interface to answer questions about all your Google Cloud databases, and quickly resolve fleet issues through optimized recommendations.

Let’s take a deeper look at the new Database Center capabilities for Bigtable, Memorystore and FIrestore.

Gain a comprehensive view of your database fleet 

Database Center simplifies database management with a single, unified view of all your Google Cloud managed database services, including Bigtable, Memorystore and Firestore. You can monitor these database resources across your entire organization, spanning multiple engines, versions, regions, projects, and environments or applications using labels. Specifically, Database Center now lets you:

• Identify out-of-date database versions to ensure proper support and reliability 

• Track version upgrades, e.g., if Memorystore Redis 6.x to Memorystore Redis 7.0/ 7.2 is updating at an expected pace

• Ensure database resources are appropriately distributed, e.g., identify the number of Bigtable, Firestore, Memorystore databases powering the critical production applications vs. non-critical dev/test environments

Proactively de-risk your database fleet with intelligent recommendations

We’ve expanded Database Center’s proactive monitoring and issue-resolution capabilities to support Bigtable and Firestore, helping to ensure optimal availability and data protection for your existing database fleet. For instance, Database Center:

• Proactively monitors Bigtable instances, detecting and helping to resolve failover gaps to minimize downtime and prevent service disruption

• Publishes recommendations related to any unsuccessful backup attempts, no automated back-up policies and short back-up retention for your Bigtable instances. It’s important to address these issues quickly to ensure data can be recovered.

• Enhances data availability and durability by protecting against system failures and regional outages for your Bigtable instances.

• Helps safeguard your critical data in Firestore by detecting if any tables lack an automated backup policy, so you can prevent data loss from accidents or corruption

In short, when issues arise, Database Center guides you through intuitive troubleshooting steps, streamlining resolution and minimizing downtime for your Bigtable and Firestore deployments. It goes beyond problem identification to provide clear, actionable solutions. Recommendations for Memorystore are coming to Database Center soon along with additional recommendations for other engines!

Optimize your database fleet with AI-powered assistance

With Gemini enabled, Database Center makes optimizing your database fleet incredibly intuitive. Chat with the AI-powered interface to get precise answers, uncover issues within your database fleet, troubleshoot problems, and implement solutions. AI-powered chat in the Database Center now includes support for Bigtable, Memorystore and Firestore. For example, Gemini can help you quickly identify Firestore resources that do not have automated back-up policies.

Get started with Database Center today

With today’s launch, Database Center now provides you a single, unified view across all your Google Cloud managed databases. You can access the Database Center within the Google Cloud console and begin monitoring and managing your entire database fleet. To learn more about Database Center’s capabilities, check out the documentation.

As the year winds down and we gather around the (digital) fireplace, the excitement around Google Cloud databases is ramping up! It’s been a busy season of innovation and customer success, and we’ve got a sack full of updates to share. Join us for a festive feast of news, including key announcements in the databases space, cool product updates and feature notes, inspiring customer stories, and a calendar of events to ring in the new year. So grab a cup of hot cocoa, settle in, and let’s unwrap the latest happenings from Google Cloud databases.

Key database announcements 

• [Forrester Wave] Google was named a Leader in The Forrester Wave™: Translytical Data Platforms, Q4 2024 report, earning the highest score possible in 11 criteria, including vision, innovation, gen AI/LLM, real-time analytics, and data security. We believe this recognition solidifies AlloyDB as Google Cloud’s fastest-growing database and a top choice for supporting transactional and analytical workloads with exceptional performance, availability, and scale.

• [New Generative AI Whitepaper] As a developer, you know the power of data. But are you harnessing the full potential of generative AI? Gen AI can help revolutionize your database applications, enabling hyper-personalization, increased productivity, and deeper insights. Read our new whitepaper today to discover the four pillars of successful gen AI database apps.

Database product updates

Cloud SQL

• Private Service Connect (PSC) Automation capability is in Preview – Customers can automate the creation of Cloud SQL PSC endpoints in their VPCs and simplify the deployment of Cloud SQL at scale.

• Cloud SQL integration with Certificate Authority Service (CAS) is in Preview – Now customers can create Cloud SQL instances with a shared CA (Certificate Authority) option to simplify TLS certificate management

• Advanced DR for PostgreSQL and MySQL now in GA – Advanced DR, a combination of switchover and replica failover along with a write Eendpoint (in Preview), provides seamless disaster recovery testing and execution without requiring application changes.

• Cloud SQL Enterprise Plus for PostgreSQL and MySQL got two new features that improve availability:

• Data cache enable/disable – Allows users to enable or disable data cache with near-zero downtime for MySQL and PostgreSQL primary instances.

• Instance scaledown – Near-zero downtime is now available for infrequent instance scaledowns on Enterprise Plus for MySQL and PostgreSQL primary instances.

AlloyDB for PostgreSQL

• Model endpoint management allows customers to access third-party and custom hosted models directly from their database. The extension enabling AI model predictions and embedding generation is now installed by default in new databases. Vertex AI’s latest models are available with no setup.

• AlloyDB now supports up to 128 TiB storage per cluster in all regions where AlloyDB is available.

• You can set up AlloyDB clusters using a copy of your Cloud SQL for PostgreSQL backup. This feature is in Preview. 

• AlloyDB now supports in-place major version upgrade in Preview – Customers can upgrade their AlloyDB cluster from PostgreSQL version 14 to version 15.

• AlloyDB Omni version 15.7.0 is now GA.

• The AlloyDB Omni Kubernetes operator version 1.2.0 is now GA.

Memorystore

• Single shard clusters – Memorystore for Redis Cluster is now GA. This enables customers to create Memorystore instances with a single shard, and dynamically scale the instance size up or down while maintaining their 99.99% SLA with multi-zone instances having replicas.

• Node-level metrics – Memorystore for Redis Cluster is now GA. Node-level metrics empower Memorystore customers with the advanced monitoring capabilities to better manage their clusters.

• OSS Autoscaler – Memorystore Cluster now in GA – OSS autoscaler ensures that customer clusters are automatically optimized for performance, capacity, and budget. 

Spanner

• Cassandra to Spanner Proxy Adapter is now GA – This  open-source tool migrates Cassandra workloads to Spanner without application changes. Read our blog post on how the new Cassandra to Spanner adapter simplifies Yahoo’s migration journey.

• Directed reads feature is now GA – Spanner provides the flexibility to route read traffic (except in RW transactions) to a specific replica type or region within a multi-region instance configuration or a custom regional configuration with optional read-only region(s).

• Query Optimizer version 7 is now in GA – Spanner’s query optimizer now rewrites OR operators on indexed columns as a UNION MERGE, improving efficiency by avoiding full table scans

• Usage statistics and insights dashboard for database splits is now GA – Spanner’s new observability feature helps identify and address performance hotspots by showing how data is distributed across splits (slide).

• Spanner now supports using multiple, single-region customer-managed encryption keys in Cloud Key Management (KMS) to protect databases in custom, dual-region, and multi-region instance configurations.

Bigtable

• Bigtable is now available in the northamerica-south1 (Mexico) region. Check out all the Bigtable locations.

• Data Boost for Bigtable is in Preview – You can now create a Data Boost app profile and view Data Boost metrics in the Google Cloud console (doc)

Firestore

• Firestore Custom Organization Policies is now GA – It allows customers to set custom restrictions on Firestore database operations.

• Firestore Key Access Justifications (KAJ) is now GA – Key access justifications provide customers with more control over data access and the ability to manage key access requests.

• Monitoring page on Firestore console allows customers to view available metrics, create a custom dashboard, and set alerts directly from the Firestore console.

• Datastore v1 Java Client Side Tracing with OpenTelemetry allows customers to collect and view distributed traces from Datastore v1 API in Cloud Trace for performance monitoring and debugging. 

• Reliability Recommender feature is now GA – It uses Active Assist to provide recommendations for improving database reliability and disaster recovery.

Businesses transforming with Google Cloud databases

Discover how Google Cloud database solutions are driving innovation and success:

• Ford Pro Intelligence leverages Google Cloud’s Bigtable to harness the power of connected vehicle data, providing real-time insights and predictive maintenance for fleets of all sizes.

• Unity Ads unlocked incredible performance by migrating to Memorystore for Redis Cluster. Their infrastructure now handles a staggering 10 million Redis operations per second on a single instance, delivering incredible speed and scalability. This move also freed up their team to focus on high-value activities, all while reducing infrastructure costs.

• Tricent Security Group boosted database performance and scalability with AlloyDB for PostgreSQL. They now handle 250 million daily transactions with faster query response times, solving performance bottlenecks and positioning themselves for future growth.

• Palo Alto Networks boosted performance and slashed costs by switching their Advanced WildFire product from Cassandra to Bigtable, resulting in 5x lower latency and cutting their total cost of ownership in half.

• Flipkart boosted its streaming analytics performance by migrating from Apache HBase to Bigtable. This move increased developer productivity, enhanced database performance, and enabled them to handle fluctuating customer demand with ease.

Database webinars and events

Product webinars 

• Fire up your innovation engine with Spanner: Today’s applications demand more than traditional databases can deliver. In this webinar, we explore Spanner, the ‘always-on’ globally available database that scales effortlessly to power your next-generation innovations.

• Maximize performance with Cloud SQL and Memorystore: In this webinar, we’ll show you the combined power of CloudSQL and Memorystore in order to reduce costs, improve efficiency, and deliver exceptional application experiences.

Cloud Wars with Bob Evans
In this episode of Cloud Wars, Bobby Brauer, head of geospatial data engineering at Bayer, discusses their innovative use of AlloyDB. Bayer is using AlloyDB to overcome data challenges in agriculture. AlloyDB provides low-latency data processing and high availability, crucial for handling spikes in data flow during harvest season. Learn how Bayer is transforming geospatial data processing to enhance efficiency and drive data-driven decision-making.

Merv Adrian x Apex Fintech | AlloyDB
In the rapidly evolving PostgreSQL DBMS landscape, AlloyDB is emerging as a top choice for organizations with demanding workloads. Join Merv Adrian for an interview with Apex Fintech, a leading provider of brokerage and wealth management services, as they share their experience with AlloyDB and how it helps them achieve high availability and robust security.

Get started

Google Cloud offers the only suite of industry-leading databases built on planet-scale infrastructure and for AI. Learn more and start a free trial today:

• Get started with a 30-day AlloyDB free trial instance.

• Create a 90-day Spanner free trial instance with 10 GB of storage at no cost.

• New customers get $300 in free credits to try Cloud SQL.

With the European Commission’s adoption of the Network and Information Systems Directive 2.0, or NIS2, Europe is taking an essential step forward in its strategy to protect consumers, businesses, and government organizations from escalating threats in cyberspace. NIS2 will help to drive a consistent high level of security and resilience across key sectors of the European economy. 

NIS2 also represents a seismic shift in the expectations and obligations of private entities to adhere to cybersecurity best practices, and to factor security into everyday business decisions. For European organizations, including Google Cloud customers, NIS2 may require new investments in security tools, talent, and processes to achieve a higher overall security baseline. Customers should consider this an opportunity to use the cloud as a platform for managing risk and for streamlining compliance. 

Google Cloud and Google Workspace offer a range of tools and resources to help customers meet their NIS2 compliance goals through building secure and resilient applications, managing cyber risks, responding to incidents, and enabling new modes of business built atop a secure foundation.

NIS2: Compliance challenge or opportunity?

NIS2 outlines new security requirements for tens of thousands of essential and important entities in critical sectors, including energy, transportation, healthcare, financial services, and digital infrastructure. Covered entities must adopt risk management practices, establish business continuity plans, enhance supply chain security practices, develop cyber hygiene and training programs, and implement more rigorous minimum security controls. 

In addition, entities must report “significant” cyber incidents to the relevant national authorities. Failure to comply could lead to enforcement measures including costly fines and reputational damage.

NIS2 and other regulations are already translating into higher cybersecurity spending. European organizations expect to allocate an average of 9% of their IT budgets on security in 2024 – up from 7.1% in 2023, according to recent research by ENISA, the European Agency for Cybersecurity. 

Still, organisations continue to struggle to hire and retain sufficient numbers of skilled cybersecurity professionals. As a result, organisations that succeed in driving down the complexity and cost of meeting their NIS2 requirements will be at a competitive advantage over those who do not.

At Google Cloud, we believe in shared fate and in our commitment to support customers as a trusted partner in their security and compliance journeys. We provide customers industry-leading tools to enhance visibility into their online assets and to address risks. 

We can help customers achieve a higher security baseline through strong inherited controls, such as encryption and multi-factor authentication (MFA). For example, Google Workspace customers experienced three times fewer incidents than those using Microsoft 365, according to a study by cyber-insurer At Bay. 

We also simplify IT lifecycle management and enable customers to focus on managing their businesses, rather than managing technical debt. We also partner closely with regulators to build trust and to support customer regulatory compliance needs on-demand. 

For these reasons, European customers should strongly consider partnering with Google Cloud to meet their compliance goals.

How Google Cloud can help our customers achieve their NIS2 goals

Google Cloud offers a set of solutions and resources to help guide customers through their NIS2 compliance journey. 

Risk management: Under NIS2, covered organizations must take appropriate information risk management measures based on international and European standards, including performing risk assessments and implementing a risk treatment plan. 

First, with management now accountable for managing cyber risks under NIS2, we offer a range of free educational resources on the topic of risk governance, including best practice guides and our Insights Hubs for CISOs and for boards of directors. Even before moving to the cloud, organizations can use our Risk Assessment and Critical Asset Discovery solution to evaluate their current IT risks, identify where critical assets reside, and view recommendations for improving their security posture and resilience. 

Once in the cloud, customers can take advantage of Google Cloud Monitoring to view the real-time performance, availability, and health of their applications and infrastructure. Security Command Center Enterprise provides organizations visibility into their  security posture and empowers them to surface and remediate vulnerabilities and misconfigurations.

Partnering with Google Cloud also allows customers to take advantage of our extensive compliance offerings, best practices, and easy access to documentation. Our products routinely undergo independent verification of security, privacy, and compliance controls captured in frameworks such as ISO/IEC 27001, which is aligned to NIS2 requirements referenced in the draft ENISA Implementing Guidance.

Organizations can also take advantage of risk management services offered by Mandiant Consulting, including security program assessments, penetration testing, and cybersecurity due diligence — even if they’re not a Google Cloud customer.

Incident handling: NIS2 requires covered entities to monitor for cyber threats and notify national authorities of significant incidents within 24 hours, file a detailed incident report within 72 hours, and issue a comprehensive final report within one month.

These requirements will test the ability of even the most sophisticated security teams to detect incidents, gather evidence, and submit reports by the deadline — while also containing and eliminating the threat. Google Cloud offers tools and resources to assist security teams in designing collaborative incident management processes, strengthening preparedness planning, and accelerating incident response efforts. 

For incident management in the cloud, Google Security Operations (SecOps) offers security practitioners a modern platform for threat monitoring, detection, investigation, and response. The SecOps platform provides organizations the ability to tap into insights from Google Threat Intelligence to track novel threats, as well as analyze petabytes of telemetry and collaborate on cases using a single platform. Built-in generative AI unlocks the ability to create custom incident playbooks on-demand to accelerate response and reporting. 

Google Cloud customers and non-customers alike can tap into Mandiant Incident Response services for in-depth forensic analysis, crisis management support, and recovery operations.

Google Cloud will notify customers of service disruptions impacting the underlying products and services they rely on using programmatic alerts via our Personalized Service Health Dashboard and via public dashboards. Google Cloud also notifies customers of security and privacy incidents via Advisory Notifications.

Business continuity: NIS2 requires organizations to implement tools and processes to ensure business continuity in the event of significant cyber incidents, including establishing an incident response team or creating backups.

There is growing recognition among customers and policymakers that cloud-based tools offer advantages in sustaining a high level of operational resilience. We perform rigorous disaster recovery testing to ensure our infrastructure will continue to run despite a range of disaster scenarios. Our data centers are certified as ISO/IEC 22301 compliant after undergoing an independent audit. 

Customers can leverage the Google Cloud Architecture Framework as a starting point for guidance on building reliable applications and robust disaster recovery capabilities. We offer a range of managed backup and disaster recovery solutions to protect customer workloads against threats such as ransomware. Google Workspace is not only a highly-reliable, highly-secure email and collaboration platform in its own right; organizations can also configure Workspace as a backup in the event their primary email platform becomes compromised.

Minimum security requirements: NIS2 requires covered organizations to adopt basic security controls and technologies, such as encryption, MFA, and identity and access management.

Customers can review our whitepaper on how we design security into Google Cloud infrastructure from the ground up. Our security model starts with overlapping physical security measures to protect Google data centers and network infrastructure. We use specially-designed Titan hardware security chips to authenticate legitimate Google devices and to verify the integrity of the software components. 

Zero trust is at the core of Google Cloud’s security model: our infrastructure continuously authenticates and authorizes every identity, device, and service to prevent lateral movement on the network. We encrypt customer data at-rest and communications in-transit between data centers. 

Further, we’re adding an additional layer of protection by making MFA mandatory for all Google Cloud customers worldwide in 2025. Customers can also refer back to the Google Cloud Architecture Framework for guidance on building secure-by-default applications in the cloud.

How Google Cloud is approaching NIS2 compliance

As a covered entity, Google Cloud will be responsible for meeting cyber risk management and incident handling requirements under NIS2 while supporting our customers along their compliance journeys. We are working closely with national authorities to demonstrate the strength of our security model and our compliance with all NIS2 requirements. 

NIS2 requires covered entities to implement strong supply chain risk management measures, including the need to codify minimum security requirements in supplier contracts and service level agreements. Customers can learn more about Google Cloud’s contractual responsibilities with respect to physical security, vulnerability management, incident notification, personnel skills and training, and subprocessor security by reviewing our Cloud Data Processing Addendum. Please contact your Google Cloud representative for further details.

Looking ahead

Google Cloud is committed to supporting a strong and secure European digital ecosystem. Under our Cloud on Europe’s Terms initiative, we are delivering a portfolio of trusted cloud solutions and sovereign controls to support customers in achieving digital transformation and unlocking innovation from AI. 

To help European organizations train and hire the next generation of cybersecurity professionals, we’ve awarded thousands of scholarships for the Google Cybersecurity Certificate program. In addition, Google.org will award $15 million over the next year to support hands-on cybersecurity education at universities across Europe, Africa, and the Middle East as part of our Cybersecurity Seminars program. 

We’re also proud to support the European Union and EU member states in efforts to combat malicious cyber activity targeting European businesses, governments, and individuals. Our cybersecurity teams already collaborate closely with more than a dozen European governments to share intelligence and disrupt threats. Customers and partners of all sizes can take advantage of free threat analysis resources we publish on our Threat Intelligence blog.

NIS2 represents an essential step forward in strengthening Europe’s collective cyber resilience. As a technology provider and security innovator, Google Cloud will continue to support our customers as we work together to build a safer Internet for all.

Today, generative AI is giving organizations new ways to process and analyze data, discover hidden insights, increase productivity and build new applications. However, data sovereignty, regulatory compliance, and low-latency requirements can be a challenge. The need to keep sensitive data in certain locations, adhere to strict regulations, and respond swiftly can make it difficult to capitalize on the cloud’s innovation, scalability, and cost-efficiency advantages.

Google Distributed Cloud (GDC) brings Google’s AI services anywhere you need them — in your own data center or at the edge. Designed with AI and data-intensive workloads in mind, GDC is a fully managed hardware and software solution featuring a rich set of services. It comes in a range of extensible hardware form factors, with leading industry independent software vendor (ISV) solutions integrated via GDC Marketplace, and your choice of whether to run it connected to Google Cloud’s systems or air-gapped from the public internet.

In this blog post, we dive into the details of how GDC’s new AI-optimized servers with NVIDIA H100 Tensor Core GPUs and our gen AI search packaged solution — now available in preview — allow you to bring increasingly popular retrieval-augmented generation (RAG) to your on-premises environment, and unlock multimodal and multilingual natural-language search experiences across your text, image, voice, and video data.

Gen AI-optimized infrastructure

GDC air-gapped now incorporates new servers with NVIDIA H100 GPUs, powered by the advanced NVIDIA Hopper architecture and the 5th Gen Intel Xeon Scalable processors. The new servers introduce the new GPU-optimized A3 VM family optimized for NVIDIA NVLink interconnect to GDC, enabling faster shared compute and memory for AI workloads using large language models (LLMs) with up to 100 billion parameters. It also extends the set of NVIDIA Multi-Instance GPU (MIG) profiles, supporting a variety of new GPU slicing schemes (both uniform and mixed-mode) and dynamic allocation of GPU resources to serve the needs of AI services with better ownership costs.

Ready-to-deploy on-prem conversational search

With GDC’s new gen AI Search solution, you get a ready-to-deploy, on-prem conversational search solution based on the Gemma 2 LLM with 9 billion parameters. You can easily ingest your sensitive on-prem data into the search solution and quickly find the most relevant information and content via natural language search, boosting employee productivity and knowledge sharing, while helping ensure that the search queries and data remain on-prem.

Responses also include citation links to your original documents so you can easily verify all answers to reduce hallucinations. Watch the demo below to see the solution in action:

For more accurate responses, the GDC gen AI search solution relies on a RAG architecture to combine the benefits of traditional search and generative AI, and user queries are augmented with relevant on-prem data before they’re sent to the LLM to generate responses. Other core integrations available out-of-box include Vertex AI pre-trained APIs (translation for 105 languages, speech-to-text for 13 languages, and optical character recognition for 46 supported and 24 experimental languages) for multimodal and multilingual data ingestion across text, images, and audio. It also includes the AlloyDB Omni database service for embeddings storage and semantic search across ingested data.

GDC’s open cloud approach also allows you to customize this solution according to your needs and swap any components as you see fit, including for other database services like Elasticsearch, other open-source models and LLMs, or your own proprietary models.

Get started on your GDC development journey

To join GDC’s gen AI search solution preview and experience how on-prem gen AI search can transform how your organization retrieves information, contact your Google account representative. Note that you will need a GDC deployment where you can deploy and run the preview.

Retrieval-augmented generation (RAG) supercharges large language models (LLMs) by connecting them to real-time, proprietary, and specialized data. This helps LLMs deliver more accurate, relevant, and contextually aware responses, minimizing hallucinations and building trust in AI applications.

But RAG can be a double-edged sword: while the concept is straightforward – find relevant information and feed it to the LLM – its implementation is difficult to master. Done incorrectly, it can impact user trust in your AI’s reliability. The culprit is often a lack of thorough evaluation. RAG systems that are not thoroughly evaluated lead to ‘silent failures’ which can undermine the reliability and trustworthiness of the system as a whole.

In this blog post, we’ll equip you with a series of best practices to identify issues within your RAG system and fix them with a transparent, automated evaluation framework.

Step 1. Create a testing framework

Testing a RAG system consists of running a set of queries against the tool and evaluating the output. A key prerequisite for rapid testing and iteration is to decide on a set of metrics as the definition of success, and calculate them in a rigorous, automated, and repeatable fashion. Below are some guidelines:

• Assemble a test dataset of high-quality questions

• Ensure that your test set covers a broad subset of the underlying data, and includes variations in phrasing and question complexity that match real-world use cases.

• Pro tip: It’s a good idea to consult with stakeholders and end users here to ensure the quality and relevance of this dataset.

• Although some metrics can be calculated without a reference dataset, having a set of known-good outputs allows us to produce a more comprehensive and nuanced range of evaluation metrics.

• There are many features of a RAG pipeline that can make a difference – by changing them one at a time, we can be sure that a change in evaluation scores is attributable to a single feature alone. 

• Similarly, we must ensure that between test runs we do not change the evaluation questions being used, the reference answers, or any system-wide parameters and settings.

The basic process here is to change one aspect of the RAG system, run the battery of tests, adapt the feature, run the exact same battery of tests again and then see how the test results have changed. Once you are satisfied that a feature cannot be improved, freeze the configuration and move on to testing a separate part of the process. 

This testing framework can be visualized as three components: 

1. Reference questions and answers: 

• The set of queries to be evaluated. Depending on which metrics are being calculated we may include corresponding reference answers.

• The retrieval and summarization techniques being changing and evaluated

• The evaluation outputs as scored by the testing framework

Choosing appropriate metrics

Establishing the best metrics to assess your system involves trial and error. Predefined testing frameworks exist that have been designed to speed up the process by providing prebuilt metrics that can also be adapted to your specific use case. This allows you to quickly generate baseline scores for the evaluation and refinement of your RAG system. From this baseline, you can then systematically modify retrieval and generation capabilities and measure any improvements.

Common RAG evaluation frameworks include:

Ragas

Ragas is an open-source tool for evaluating RAG systems. It measures key aspects like factual accuracy, answer relevance, and how well retrieved content matches the question. Ragas also helps generate test data, making it easier for developers to improve RAG systems for accuracy and usefulness.

Vertex AI gen AI evaluation service

The Vertex AI gen AI evaluation service helps users test and compare generative models or applications based on custom metrics. It supports model selection, prompt engineering, and fine-tuning and allows users to define metrics, prepare data, run evaluations, and review results. The service works with Google’s models, third-party models, and open models across various languages, using both model-based and computation-based assessment methods.

Example metrics

Model-based metrics utilize a proprietary Google model to assess the output of a candidate model. Functioning as an evaluator, this model scores responses based on predefined criteria.

• Pointwise metrics: The judge model assigns a numerical score (e.g., on a scale of 0-5) to the candidate model’s output, indicating its alignment with the evaluation criteria. A higher score signifies a better fit.

• Pairwise metrics: The judge model compares the responses of two models and identifies the superior one. This approach is frequently employed to benchmark a candidate model against a baseline model.

Computation-based metrics: These metrics utilize mathematical formulas to compare the model’s output against a ground truth or reference. Popular examples include ROUGE and BLEU.

Opinionated tiger team actions

1. Collaborate with stakeholders to develop a set of “golden” question inputs. These questions should accurately reflect the main use cases the RAG system is intended to address. It’s crucial to include a diverse range of query types, such as simple, complex, multi-part, and misspelled queries to ensure comprehensive testing.

2. Make use of the Vertex AI generative AI evaluation framework. This framework allows developers to quickly implement multiple test metrics, and run multiple tests on a model’s performance with minimal setup. It offers a fast feedback loop, so improvements can be made rapidly.

3. Conduct a pointwise evaluation of the RAG retrieval system.

4. Generate model scores based on the following criteria:

1. Response groundedness: The extent to which the generated text aligns with the factual information retrieved from the source documents.

2. Verbosity: The length and detail of the response. While beneficial for providing comprehensive understanding, excessive verbosity may indicate difficulty in concisely and accurately answering the question. You may wish to tune this metric based on your use case.

3. Instruction following: The system’s ability to generate text that accurately and comprehensively adheres to given instructions, ensuring the output is relevant and aligned with user intent.

4. Question answer quality as related to instructions: The ability of the RAG system to generate text that correctly answers a user’s question with a high level of detail and coherence.

Step 2. Root cause analysis and iterative testing 

The goal of setting up a repeatable testing framework is ideally understanding the root cause of issues. RAG is fundamentally based on two components: (1) the retrieval accuracy of your nearest neighbor matches and (2) the context that you provide to the LLM that generates your responses.

Identifying and isolating these components individually allows you to determine the specific areas that may be causing problems and formulating testable hypotheses that can be performed as experiments and run in Vertex AI using the Gen AI evaluation framework.

Typically when performing a root cause analysis exercise, the user will execute a testing run as a baseline, modify the implementation of one of the RAG components, and re-execute the testing run. The delta between the output scores of the testing metrics is the influence of the RAG component that was altered. The goal in this phase is to modify and document the components carefully, aiming to optimize towards a maximum score for each of the chosen metrics. Often the temptation is to make multiple modifications between testing runs which can mask the impact of a specific process and whether it was successful in creating a measurable change in your RAG system.

Examples of RAG experiments to run

Example RAG components to experiment with:

• What is the ideal number of neighbors for a document chunk that gets passed into an LLM to improve answer generation?

• How does embedding model choice affect retrieval accuracy?

• How do different chunking strategies affect quality? For example, adjusting variables like chunk size or overlap, or exploring strategies such as pre-processing chunks to summarize or paraphrase them with a language model.

• When it comes to generation, simply comparing Model A vs. Model B or Prompt A vs. Prompt B is particularly useful for fine-tuning prompt design or adjusting model configurations, helping developers to optimize models and prompts for specific use cases.

• What happens when you enrich documents with metadata like title, author, and tags for better retrieval signals?

Opinionated tiger team actions

1. Test model A vs model B for generation tasks (simple and can produce measurable results)

2. Test chunking strategies for retrieval within a single embedding model (400 chars, 600 chars, 1200 chars, Full document text)

3. Test pre-processing of long chunks to summarize them to smaller chunk sizes.

4. Test what data is passed to the LLM as context. For example, do we pass the matched chunks themselves, or use these as a lookup to find the source document and pass the whole document text to the LLM, making use of long context windows.

Step 3. Human evaluation

Although quantitative metrics created by your testing framework provide valuable data, qualitative feedback from real users is also crucial. Automated testing tools are efficient for scalability and rapid iteration, but they cannot replicate human judgment in ensuring high-quality output. Human testers can evaluate subtle aspects like the tone of responses, the clarity of explanations, and potential ambiguity. Combining qualitative and quantitative testing provides a more holistic understanding of your RAG system’s performance.

Human tests are typically run after you’ve achieved a solid level of baseline answer quality by optimizing evaluation metrics through the automated testing framework. You may wish to include human response evaluation as part of your broader user-testing motions for the system as a whole, such as performance, UX, etc. Similar to previous experiments, human testers can focus on specific system features following structured steps, or they can assess the overall application and provide comprehensive qualitative feedback. 

Because human testing is time consuming and repetitive, it is essential to identify users who are engaged and willing to provide meaningful feedback.

Opinionated tiger team actions

• Identify key personas based on the RAG system’s target users

• Recruit a representative sample of participants that matches these personas to ensure realistic feedback.

• If possible, include both technical and non-technical user groups for testing

• Sit with the user (if possible) to ask follow-up questions and dig into the detail of their responses

Conclusion

To begin your own evaluation, explore Google Cloud’s generative AI evaluation service, where you can create both prebuilt and custom evaluation methodologies to enhance your RAG system.

As the 2027 end of support for SAP Business Suite 7 approaches, SAP customers need to decide where to deploy as they upgrade to cloud-based S/4HANA and RISE with SAP. This represents a great opportunity to get more value out of your enterprise data, and take advantage of the longstanding partnership between Google Cloud and SAP. RISE with SAP on Google Cloud lets you accelerate innovation and reduce costs through a secure, reliable, and scalable high-performance infrastructure. By deploying SAP on Google Cloud, you can accelerate your ERP transformation with a unique, unified data experience that offers leading integration, analytics accelerators, and AI innovations to bring clarity from data to decisions.

Two data powerhouses unite

Key to the Google Cloud-SAP partnership is the integration between BigQuery, a fully managed, AI-ready data analytics platform that helps you maximize value from your data, and SAP Datasphere. BigQuery is designed to be multi-engine, multi-format, and multi-cloud. This integration lets you access your most critical data in real time without duplication thanks to co-engineered data replication and federation technologies. This joint capability can unify data from SAP software systems, such as SAP S/4HANA and SAP HANA Cloud, providing organizations with a comprehensive view of their most important data on Google Cloud and letting you:

• Simplify data landscapes. Federate queries across SAP Datasphere and BigQuery to blend data from SAP and non-SAP software. This minimizes common data silos from sources that span marketing, sales, purchasing, finance, supply chain, manufacturing and more. 

• Improve decision-making. Gain a more holistic view of your data and leverage powerful analytics to drive better decision-making. Now, you can plan with a single, comprehensive view of your businesses by connecting SAP data to powerful data and analytics tools such as Vertex AI to analyze financial and business outcomes while improving the accuracy of models. 

• Add rich context to your analytics. BigQuery makes it easy to bring in Google and third-party data sources such as Trends, Weather, Maps, world events and Enterprise application data with Google Cloud Cortex Framework’s pre-built accelerators.

• Use SAP Business Technology Platform (SAP BTP) globally on Google Cloud. SAP is advancing its multi-cloud offerings by expanding regional support of SAP BTP and SAP HANA Cloud on Google Cloud, which includes support for SAP Analytics Cloud and SAP Datasphere. SAP and Google Cloud intend to launch SAP BTP in five new regions, building to a total of eight regions supported by 2025.

Transform your business with AI

Google Cloud has powerful, easy-to-use tools to help you incorporate your SAP data into predictive and generative AI initiatives. Vertex AI provides a single, integrated development platform where you can build sophisticated predictive and generative AI agents and experiences faster — without any manual ETL.

• Innovate freely with Model Garden, a curated collection of over 150 machine learning models. Google is the only cloud provider to offer widely used first-party, third-party, and open-source models, so you can easily discover and choose foundation models based on modality, size, performance, latency, and cost. 

• Leverage Google’s most powerful model yet. Gemini is Google’s most capable and general family of LLMs, offering four models each built for its own set of use cases. Google Cloud recently announced multiple Gemini updates that enable SAP users to bring more gen AI capabilities to SAP workloads and access Google Cloud’s large language models through SAP GenAI Hub.

• Get more from your models by augmenting and grounding them with SAP data. Leverage Vertex AI Model Builder’s managed tooling for extensions, function calling, and grounding. Customize Gemini models in an efficient, lower-cost way with supervised tuning.

• Easily deploy, manage, and monitor agents with Vertex AI Agent Builder, which lets you quickly create a range of generative AI agents grounded with Google Search and your SAP data with the convenience of a no-code agent builder console. 

Run SAP today on tomorrow’s infrastructure

Google Cloud helps SAP customers build quickly, securely, and cost effectively with the next generation of infrastructure designed to meet specific workload and industry needs. Google Cloud infrastructure is optimized for AI, cloud-first, enterprise workloads with an emphasis on security, scalability, and data sovereignty. 

• Leverage superior scale-up machine types. Google Cloud is the sole provider of 32TB SAP-certified machines, simplifying architecture, deployments, and administration. More recently, Google Cloud introduced memory-optimized X4 instances, which provide the largest SAP-certified compute instances in the cloud market, supporting up to 32TB SAP HANA workloads.

• Accelerate time to value. Google Cortex for SAP Framework delivers pre-built analytical and AI models based on SAP data models and with out-of-the-box integration with SAP. Cortex comes with pre-built dashboards for visualizing SAP data and enriches data with AI/ML services and external data sets such as weather, search trends, ads impressions, and more.

• Increase agility and scalability. Unlike on-premises infrastructure, Google Cloud offers on-demand scalability to massive scale if needed, allowing you to easily adjust resources based on changing business needs. This agility can be crucial for responding to market fluctuations or unexpected growth.

• Improve performance and efficiency. Google Cloud’s infrastructure is designed for high performance and reliability. This can lead to faster processing times and reduced latency for your SAP applications, improving overall efficiency and the user experience.

• A highly secure cloud. While customers are growing concerned about the recent increase in DDoS attacks, threat vulnerability, and security-born outages, Google Cloud’s customers have been appreciative of our consistency in availability. With robust security features and compliance certifications, Google Cloud can help give you peace of mind knowing your data is secure and your business meets regulatory requirements.

• Meet sustainability goals. SAP and Google Cloud are exploring ways to combine SAP Datasphere with broader ESG data sets and insights powered by Google Cloud to accelerate sustainability journeys with actionable insights.

Real-world results with RISE on Google Cloud

SAP customers are already getting more out of their technology investments by migrating to Google Cloud.

Hunkemöller
Hunkemöller, a leading European lingerie brand with over 850 stores in 22 countries, aimed to enhance its customer experience and support its omnichannel growth strategy. The company migrated its SAP environment and IT infrastructure to Google Cloud using RISE with SAP. This move allowed Hunkemöller to leverage Google Cloud’s data and analytics capabilities, enabling more precise customer segmentation and personalized recommendations, leading to improved customer experiences and reduced returns. 

“By moving our on-premise data to the cloud and leveraging Google Cloud technologies, we can provide our customers with the best possible customer experience—digitally driven and supported by data. Developments happen at lightspeed in retail, and we want to be a market leader when it comes to digital applications and use of data for a better customer experience and operation of the business. We worked intensively with experts from Google Cloud and partners to realize the blueprint for cloud migration.” – Gordon Smit, Global IT Director, Hunkemöller

Cementos Pacasmayo
A leading producer of construction solutions in Northern Peru, Cementos Pacasmayo built a data ecosystem using the Cortex Framework, SAP, and Vertex AI on Google Cloud to provide stakeholders across all departments with fast, broad, and deep visibility into data. 

“Before, three people had to work for eight hours to generate a business analysis. Now business users don’t have to wait for computer scientists or data analysts. They can act autonomously to immediately gain valuable insights thanks to automation and rich, centralized information.” – Franz Zárate, Technical Tribe Lead Data & Analytics, Cementos Pacasmayo 

This is just the beginning

Google Cloud and SAP continue to collaborate, with new co-innovations coming online all the time, making Google Cloud a compelling choice for your SAP modernization initiative. Together, Google Cloud and SAP have made it easy to activate your SAP data with Cloud Cortex Framework and Vertex AI. Now you can enable advanced analytics and build cutting-edge AI/ML and generative AI applications, retain end-to-end business context for enterprise AI, and improve time-to-AI enterprise realization with instant access to your SAP data, all without thousands of integration and development hours. 

Learn more about SAP on Google Cloud here.

With the rapid advancement and adoption of AI, organizations face increasing pressure to ensure their AI systems are developed and used responsibly. This includes considerations around bias, fairness, transparency, privacy, and security. 

A comprehensive framework for managing the risks and opportunities associated with AI can help by offering a structured approach to building trust and mitigating potential harm. The ISO/IEC 42001:2023 standard provides a framework for addressing the unique challenges AI poses, and we’re excited to announce that Google Cloud has achieved an accredited ISO/IEC 42001:2023 certification for our AI management system.

This certification helps demonstrate our commitment to developing and deploying AI responsibly. It underscores our dedication to building trust and transparency in the AI ecosystem, and provides our customers with further assurance that our AI services meet the industry standards for quality, safety, and ethical considerations. 

In a landscape increasingly shaped by the advent of AI regulations, such as the EU AI Act, this certification is foundational upon which we continue to build and expand our responsible AI efforts. As AI continues to transform industries, this certification reinforces our position as a leader in providing responsible, compliant, and innovative AI solutions.

Our journey to certification

Achieving ISO/IEC 42001:2023 certification was a significant undertaking, reflecting our long-standing commitment to responsible AI as we continue to align our processes with industry standards. This independent validation reinforces our commitment to AI risk management and continuous improvement across our AI lifecycle.

This certification offers our customers several key benefits:

• Enhanced trust and transparency: Independent validation of our AI management system provides increased confidence in the responsible development and operation of our AI products and services.

• Compliance support: This certification enables our customers to use services supported by a certified AI management system, supporting their own compliance efforts, and demonstrating a commitment to using responsibly-built AI technology.

• Risk management: The certification demonstrates our dedication to managing the risks inherent in AI development and deployment, such as bias, fairness, security, and privacy.

• Access to innovative and responsible AI: Customers can use our certified AI services to build and deploy their own AI solutions with confidence, knowing they are built on a foundation of responsible AI principles.

What’s next

We are committed to maintaining high standards and continually improving our AI management system. We will continue to work closely with standards organizations, regulators, and our customers to shape the future of responsible AI.  

To help customers get started, last year we introduced the Secure AI Framework (SAIF). We also recently published the SAIF Risk Assessment tool, which is an interactive way for AI developers and organizations to take stock of their security posture, assess risks and implement stronger security practices. 

Of course, operationalizing an industry framework requires close partnership and collaboration, and above all, a forum to make that happen. This is why we introduced the Coalition for Secure AI (CoSAI), comprising a forum of industry peers to advance comprehensive security measures addressing the risks that come with AI.

Google Cloud is committed to sharing our learnings, strategies, and guidance so we can collectively build and deliver responsible, secure, compliant and trustworthy AI systems.

Editor’s note: FanCode needed to deliver low-latency, personalized sports content to millions of fans while scaling rapidly. By migrating to Google Cloud and adopting Memorystore for Redis Cluster, FanCode built a fully integrated, scalable backend infrastructure that supports real-time features and high-traffic live events. Today, FanCode streams 2x more live events annually with consistent performance and reliability, enhancing fan engagement and meeting the demands of modern sports enthusiasts.

At FanCode, India’s premier sports destination, we bring the thrill of sports to fans across India. With a diverse catalog of content, FanCode satisfies every fan’s craving, whether it’s cricket in Mumbai or football in Goa. And for fans of speed, we offer something truly special: As the exclusive streaming home for Formula One racing in India, we deliver all the on-track action with unparalleled coverage, bringing you closer to one of the world’s most exciting motorsports.

Our goal at FanCode is to deliver content reliably and in real time, with features like live scores, player stats, and tailored recommendations based on fan preferences, viewing history, and regional interests. But as our platform grew to serve millions, managing this level of content delivery became increasingly complex.

Redis plays a critical role in our backend infrastructure, providing the essential caching layer to support fast data retrieval, low-latency streaming, and real-time processing. Originally, we hosted Redis on AWS, but as we migrated to Google Cloud, maintaining Redis separately introduced latency issues that impacted our service’s speed and reliability. Other self-hosted Redis providers had unsustainable pricing models and would have required extensive changes to our core adapter SDKs, stretching our small team’s resources. We needed Redis to align with our Google Cloud infrastructure for a more flexible, scalable, and fully integrated solution — and Memorystore for Redis Cluster was our answer.

Scoring low latency with Memorystore for Redis Cluster

FanCode’s application infrastructure, hosted entirely on Google Cloud, uses a load balancer to direct traffic to an edge service that routes requests to a gateway managing multiple microservices. Each microservice relies on a dedicated database and Memorystore for Redis Cluster to maintain fault tolerance, performance, and scalability. Memorystore’s flexibility lets us start with smaller clusters for lighter workloads and expand as needed — without requiring major adjustments to our architecture. 

As we migrated from AWS, we separated Redis for minimal disruption. Non-persistent workloads were transitioned with zero downtime as cache keys refreshed automatically. For persistent workloads, we used Redis Input/Output tools (RIOT) to carefully migrate data, redirecting requests through a proxy to maintain continuity. Google Cloud’s support throughout this process allowed us to maintain business continuity and optimize performance.

With Memorystore for Redis Cluster sitting at the core of our infrastructure, our scalable content delivery pipeline can support thousands of live sports streams annually and uninterrupted viewing across web, mobile, and TV platforms, even during peak traffic. This fully managed service integrates seamlessly with our Google Cloud environment, so we can scale easily as demand increases. During live events, where large spikes in concurrent viewers require both high performance and reliability, this caching layer is especially crucial.

Furthermore, we’re able run advanced analytics within this pipeline to provide personalized recommendations based on fan preferences. Real-time data processing powers interactive features like live scores, match statistics, and player analytics, transforming fan engagement into a more immersive experience. Memorystore for Redis Cluster supports these backend functions with fast data caching and retrieval, enabling us to connect fans with real-time insights. 

Streaming millions of moments with consistent performance

With Google Cloud, FanCode is not just scaling but elevating the fan experience. We’re breaking new ground in sports media, creating a platform that’s as interactive and dynamic as our fans. Today, FanCode serves over 160 million users and streams billions of minutes of live sports content annually, with sub-second latency that ensures smooth viewing. 

Managed services have lightened our operational load, as our small team can focus on innovation rather than database maintenance. The ability to scale Redis clusters based on real-time demand optimizes both performance and costs while strong SLAs provide high availability, which is critical during high-stakes events. Downtime during these moments can mean lost revenue and disappointed fans, so Google Cloud’s stability has been key in supporting a seamless experience. 

Our infrastructure now allows us to support over 15,000 live events each year — more than double the previous year’s volume. The reliability and scalability of Google Cloud have been essential to this growth, delivering uninterrupted, high-quality streaming that keeps fans coming back for more. With this robust, integrated infrastructure, FanCode is meeting the demands of modern sports enthusiasts and achieving our business goals.

Ready to get started with your own Memorystore implementation? 

Check out our product and documentation pages to learn more about Memorystore for Redis Cluster.

In today’s rapidly evolving digital landscape, organizations need to leverage real-time data for actionable insights and improved decision-making. Availability of real-time data is emerging as a key element to evolve and grow the business. Pub/Sub is Google Cloud’s simple, reliable, and scalable messaging service that serves as a versatile entry point to ingest streaming data into Google Cloud’s ecosystem, and is integrated with products like BigQuery, Cloud Storage, Dataflow, and more. You can then use this data for downstream analytics, visualization, and AI applications. This year we launched several new features and enhancements to help meet the demands of modern streaming workloads, across three key data analytics patterns:

1. Streaming ingestion – Stream data directly into BigQuery and Cloud Storage for downstream use cases such as analytics and ML with BigQuery or for backup in Cloud Storage.

2. Streaming analytics – Process and analyze real-time event streams and take business decisions on high-value, real-time insights with Dataflow or BigQuery Engine for Apache Flink, or BigQuery continuous queries.

3. Stream sharing and export – Curate, share and monetize your valuable streaming data through data exchanges with your internal teams and/or external customers.

Let’s take a closer look at the Pub/Sub highlights of 2024 across these three areas.

Streaming ingestion

Many customers have some workloads on one public cloud and other workloads (e.g. analytical) on another. Pub/Sub has traditionally supported streaming ingestion into BigQuery and Cloud Storage through export subscriptions. This year, we simplified import into Pub/Sub from various sources, starting with AWS Kinesis Data Streams. Pub/Sub import topics is a new no-code, one-click way to ingest streaming data from AWS Kinesis Data Streams into Pub/Sub. This helps simplify streaming data ingestion pipelines without the overhead of maintaining and running a custom connector.

Another typical streaming ingestion use case is to ingest batch data into Pub/Sub. To ingest data from Cloud Storage into Pub/Sub, you used to have to either configure, deploy, run, manage and scale a custom connector, or use a Dataflow template. Now you can enable the ingestion property to create a Cloud Storage import topic to ingest batch data from Cloud Storage into a Pub/Sub topic. Once the data is flowing into an import topic, you can create a subscription (Pull, Push, BigQuery or Cloud Storage) to get the data to your choice of sink for downstream processing. 

There are two key use cases for Cloud Storage import topics:

1. Batch to streaming – To leverage batch data for streaming analytics use cases like predictions and activations, you must first transform it into a streaming format. With Cloud Storage import topics, you can perform this ingestion in a fully managed way.

2. Streaming archive data – Many customers need to store historical data; using Pub/Sub with Cloud Storage subscriptions makes it easier to build their archive. From there, Cloud Storage import topics make it easy to ingest historical data into a Pub/Sub topic for streaming analytics use cases.

This year we launched BigQuery tables for Apache Iceberg in preview, a fully managed, Apache Iceberg-compatible storage engine from BigQuery with features such as autonomous storage, optimizations, clustering and high-throughput streaming ingestion. Pub/Sub BigQuery subscriptions integrates with BigQuery tables for Apache Iceberg for high-throughput streaming ingestion that durably stores the ingested tuples in a row-oriented format, and periodically converts them to Parquet stored in a customer-owned Cloud Storage bucket. BigQuery tables for Apache Iceberg can also be used with Pub/Sub to store streaming data in Cloud Storage in Parquet format.

Streaming analytics

Customers use Pub/Sub in conjunction with stream processing engines to power streaming-analytics use cases such as anomaly detection, personalization, etc. With Pub/Sub already natively integrated with Dataflow, in 2024, we focused on supporting Apache Flink, an open-source stream-processing framework that is seeing growing adoption across enterprises. You can now use Apache Flink with Pub/Sub in two ways:

1. BigQuery Engine for Apache Flink
We recently launched BigQuery Engine for Apache Flink in preview, which lets you use the familiar Apache Flink API and ecosystem for stateful stream processing with Java, Python and SQL. It’s also a serverless offering with fully managed deployments, autoscaling, transparent upgrades and pay-as-you-go billing, and is natively integrated into our unified data and AI platform. Pub/Sub is also integrated with BigQuery Engine for Apache Flink.

2. Pub/Sub Apache Flink connector
To support streaming analytics with existing Apache Flink deployments, we launched a new version of the Pub/Sub Flink connector. Now generally available, the connector lets you connect your existing Apache Flink deployment to Pub/Sub in just a few steps. The connector also allows you to publish an Apache Flink output into Pub/Sub topics or use Pub/Sub subscriptions as a source in Apache Flink applications.

Stream sharing & export

BigQuery Analytics Hub lets businesses share batch data assets across organizations efficiently and securely. However, many organizations also need to share real-time streaming data with partners and customers, as well as with internal teams. To help, Pub/Sub Topics sharing in Analytics Hub in preview provides:

• Real-time data sharing, allowing data providers to share data updates instantly, facilitating timely access to the freshest data.
• Enhanced data discovery: By listing Pub/Sub topics as data products, producers can help increase the visibility and discoverability of their data streams.
• Simplified data access, with an integrated experience for centrally managing accessibility to your organization’s streaming data.

To simplify streaming real-time data from BigQuery to external systems and vendors, you can use BigQuery continuous queries with Pub/Sub, extending new streaming SQL capabilities within BigQuery in the form of SQL jobs that can run indefinitely and process real-time data the moment it arrives. BigQuery continuous queries lets you analyze streaming data in real-time, and act on those insights immediately.

You can even leverage Pub/Sub as both an input and output for real-time data processing: Use BigQuery subscriptions to ingest streaming data into BigQuery, with a BigQuery continuous query to process, analyze, and develop event-driven data pipelines for communicating insights to downstream applications by exporting the query results to a separate Pub/Sub topic. Multiple Google Cloud ISV partners already support Pub/Sub messages generated from a continuous query, including (but not limited to) Aiven, Census, Confluent, Estuary, Hightouch, Keboola, Lytics, Nexla, Qlik, and Redpanda.

Observability

New support for OpenTelemetry in Pub/Sub lets you see a detailed trace of your message lifecycle, including the ability to see a distributed trace from the moment a message is published to when it’s received and processed. Analyzing these traces can decrease troubleshooting time by allowing you to quickly identify bottlenecks, misconfigurations, and other failures in your Pub/Sub applications.

Looking ahead

As we look ahead to 2025, we have planned innovation across following key areas:

• Simplified Kafka ingestion – Oftentimes customers migrate from Kafka to Pub/Sub to simplify their messaging infrastructure and enjoy Pub/Sub’s key benefits of simplicity, reliability and auto-scalability. To make this migration journey simpler, we will be launching cross-cloud Kafka sources with Import Topics in early 2025.

• Single message transforms – Almost all streaming data pipelines need some form of transformations. Some customers prefer to transform the data after it has landed into a data lake or data warehouse (ELT pattern), while others prefer to transform the data before landing it in the sink (data lake, data warehouse). In 2025, we plan to further simplify streaming analytics architectures by providing native, lightweight, single-message transformations. Pub/Sub Single Message Transforms (SMT) will help you perform simple, lightweight modifications to the message attributes and/or data with JavaScript User-Defined Function (UDFs). 

Thanks for reading this far. We are excited to get these capabilities to you. Get started with Pub/Sub today and start exploring these new features to solve your hardest business challenges.

Here at the Kubernetes Podcast, we’ve been bringing you news and interviews from the Kubernetes and Cloud-Native technology space since 2019. And this year, Kubernetes turned 10 years old. We celebrated the milestone by focusing on open-source maintainers, including four Kubernetes 10-year anniversary special episodes, the usual release episodes (1.30, 1.31, and 1.32), and interviews with industry experts and end users.

In case you missed it, check out our most-listened-to episodes of 2024.

1. Cilium and eBPF, with Bill Mulligan
One of our first episodes of 2024 was also one of our most popular. In this episode, Bill Mulligan of Isovalent, the company behind Cilium and eBPF, gives an approachable intro to these kernel technologies. eBPF gives users the ability to run code in protected environments at the kernel level. Cilium is an eBPF-based technology for networking, observability, and security. It’s also fitting that Google Kubernetes Engine runs Cilium with its Dataplane V2 add-on.

2. API Machinery, chaos and dishwashers, with Lucas Käldström
If you’ve been involved with Kubernetes for any period of time, you’ve likely heard of API Machinery. We were curious about it too so we brought in Lucas Käldström, one of the maintainers. What we did not expect was a deep dive into the promise theory, entropy, dishwashers and chaos engineering. Believe us, we covered all these topics, but we don’t want to spoil anything for you. You will have to listen to the episode for yourself.

3. Creating Envoy, with Matt Klein
Matt is the brain behind Envoy, one of the most popular proxies on the market today. Envoy is at the core of the popular service mesh, Istio, but is also used as a standalone proxy by many organizations. We talk to Matt about the story of the creation of Envoy and its popularity.

4. Postgres on Kubernetes, with Álvaro Hernández
The popularity of stateful apps on Kubernetes has grown immensely in its first decade. Databases in particular have been a point of contention, namely, how and why to run them on Kubernetes. To teach us how to make databases on Kubernetes work, we invited an expert. With Álvaro, we discussed the state of databases on Kubernetes, why Operators matter, and why you should use them. Plus, we take a bonus look at some cool stuff that Álvaro and his team are working on.

5. Kubernetes v1.30: “Uwubernetes”, with Kat Cosgrove
For our first episode of 2024, we continued the Kubernetes Podcast of interviewing release leads. If you are wondering what Uwubernetes means, it was the theme of this particular release: Kubernetes v1.30 makes your clusters cuter. Kat walked us through the choice behind this particular theme and the most important features in the release, including deprecations or removals.

6. Kubernetes stale reads, with Madhav Jivrajani
etcd is at the core of Kubernetes. The NoSQL database acts as the source of truth for everything happening inside the system. In a blog post earlier this year, maintainers explored a curious stale read issue that users were encountering. So we invited Madhav to talk about it and learn more about what maintainers were doing to solve this issue.

7. AI/ML in Kubernetes
If we were to summarize 2024 in a few words, “Artificial Intelligence” would be very high on that list. As a solution for distributed systems, Kubernetes is well-positioned to tackle the new technical challenges posed by these resource-hungry workloads. Maciej Szulik, Clayton Coleman, and Dawn Chen are prominent leaders who are driving new AI initiatives in the open source community. And in the case of Clayton and Dawn, they’ve been around since the very beginning. As part of our 10 year anniversary coverage, we asked these Kubernetes maintainers for their thoughts on what AI means for Kubernetes.

8. Observability & Engineering Management, with Charity Majors
Charity is a major name in observability engineering; she even wrote the book on it! From working on distributed systems at large companies, to founding Honeycomb.io and the idea of “observability 2.0,” Charity is a pre-eminent expert with a lot to share. In this episode we discuss observability, managing engineering teams, and blogging best practices.

9. Container Security, with Michele Chubirka
Are containers more secure than virtual machines? To explore this seemingly endless debate, we brought security expert Michele Chubirka to the show — plus some extra special security-focused guest hosts! Michele explored a wide variety of container security topics, from the basic to the advanced, while expertly managing a panel of curious podcast hosts.

10. Falco Graduation, with Mike Coleman
Falco is a very popular Kubernetes runtime security tool that became a Cloud Native Compute Foundation (CNCF) Graduated project this year. To explore what Falco does and celebrate its graduation, we welcomed Mike Coleman to the show. Mike’s storied history with container technology leads into his involvement with the Falco community. In this episode, he explains the basics of the technology and what graduation means for the community.

We recently published our 2024 recap episode, where co-hosts Abdel Sghiouar and Kaslin Fields and guest host Mofi Rahman reflected on how 2024 has been for the cloud-native and Kubernetes space.

If you’re looking for a podcast to enjoy over the holiday season, we invite you to subscribe to the show and catch up on these conversations, as well as many others we’ve had over the years. You can also visit our website to find links, notes, and transcripts.

Editor’s note: Virgin Media O2 provides internet and communications services to more than 48.5 million subscribers, and teams are also responsible for supporting more than 16,000 employees. Virgin Media O2 is committed to empowering customers with outstanding customer-centered products and essential connectivity — all powered behind the scenes by Google Cloud, the backbone of the company’s daily operations. In this guest blog, Henry Tze shares his DevOps insights.

From virtual machines to applications to storage assets, at Virgin Media O2 we have a huge cloud footprint, all hosted in Google Cloud. Google Cloud enables us to process vast amounts of data at scale and deliver cutting-edge customer services, using an array of cloud-first services, including BigQuery, Google Kubernetes Engine, and Compute Engine. 

All told, we’re managing more than 11 million resources, including nearly 1 million BigQuery tables. Ensuring adherence to stringent security and compliance requirements is central to our mission — and no small feat.

Reducing identity sprawl to safeguard our cloud environment

We have a large, international team, and even though all of our employees are vital to our operations, they don’t all need access to the same information. To protect both our internal and customer data, we have to carefully configure our Identity and Access Management (IAM) infrastructure to ensure we’re compliant with the U.K.’s stringent data management regulations.

Part of our security journey has involved both our migration to the cloud and fully embracing Infrastructure as Code (IaC). We now refer to our infrastructure as “everything as code,” using GitOps to centralize our source of truth. Doing so helps us create a clear audit trail and version history record. 

This centralization has helped us align developers, engineers, and compliance teams toward the same goals. Unfortunately, ensuring that these teams have access to the tools they need to accomplish those goals is complicated.

Since taking a GitOps approach means we’re focusing more on what we want our infrastructure to look like than how we get there, we have less insight into the specific tools or paths our teams need to take to get the job done. However, this led to permission sprawl as users gained access to applications or datasets for a specific purpose and retained that access after they were finished with their work.

As that sprawl expanded, it was difficult to track who had access to what which ultimately posed a challenge to our compliance and security goals. Balancing ease of access with security compliance is a complicated challenge, but we needed to find a simple solution. 

With Google Cloud, we already had access to IAM conditions, which helped us customize time-based access controls. However, for us to scale we needed to automate provisioning and permissions management based on roles and responsibilities, and now we can with Google Cloud’s built-in Privileged Access Manager (PAM). 

Improving compliance by centralizing and automating identity controls with PAM

Using PAM, our team can ensure we adhere to least-privilege standards to protect our assets but still automate access for appropriate users, all without needing an external agent or additional software tool. By providing just-in-time (JIT), time-bound, and approval-based access, our admin team can rest easy knowing that the only users who are actively using an application or other asset have access. 

Our administrators can review access permissions in a single place and still customize permission controls from asset to asset. For example, developers may have predefined, role-based permissions for their day-to-day applications. However, if they’re assigned an ad hoc data project, they’ll need to request temporary access, and the approver is notified immediately. 

This extra level of caution helps us mitigate risk by reducing the number of users who have blanket permissions and prevents access sprawl as we grow. Our compliance auditors are also satisfied because we have clear access permission histories which simplifies and centralizes the audit process. 

Using Google Cloud’s PAM we created a self-service model at Virgin Media O2, that has helped us streamline access, boost productivity, and address compliance. Here are core tenets of our model:

Delegated approvals: Our Cloud Platform Security Team can delegate approval authority to Department and Product Administrators, which can enable them to manage privileges for their areas of expertise.

Built-in guardrails: Our Cloud Platform Security Team relies on predefined templates with embedded security controls, which can ensure that privileged access is granted securely, even with delegated approvals.

Self-sufficient teams: Our Department and Product administrators gain the autonomy to manage entitlements independently, so they can make changes swiftly and without the need to involve the security team.

On-demand access: Our customers can request permissions at any time, and adapt to evolving work requirements.

Compliance audits: Our Compliance Auditors have complete visibility into every action related to privileged access, from entitlement creation and grant requests to activation and termination.

Using PAM to drive success

Since adopting PAM, we’ve successfully reduced longstanding IAM permissions that created potential vulnerabilities in our cloud and simplified access management across the organization. Most importantly, PAM has helped us achieve and maintain compliance with the stringent requirements imposed on our regulated organization. 

We’ve also built a more sustainable and scalable access system that will grow alongside Virgin Media O2 as we continue to supercharge more customers across the UK.

One of the biggest areas of promise for generative AI is coding assistance — leveraging the power of large language models to help developers create or update application code with amazing speed and accuracy, dramatically boosting productivity. For Gemini Code Assist, 2024 has been a year of tremendous growth and innovation, as we help companies build, run, and operate apps across the software development lifecycle (SDLC). Just last week, we announced support for Gemini 2.0 Flash. This new large language model (LLM) will help Gemini Code Assist users be more productive with higher quality responses and lower latency so they can stay in an uninterrupted flow state for longer. 

Gemini Code Assist tools: A connected development ecosystem

Building on these advancements, today we’re launching Gemini Code Assist tools, a new way for developers to get the information they need to build and manage apps. As of today, you can sign up to preview Gemini Code Assist tools and start testing them. Or, sign up now to build your own extension for Gemini Code Assist.

Tools enable developers to retrieve information from, or act on any part of their engineering system, which is especially helpful for services outside the IDE. For example, you might summarize recent comments from a Jira issue, find the last person who merged changes to a file in git, or show the most recent live site issue from Sentry. Tools can do this because they leverage that most basic building block of the internet: the API. Tools effectively translate any natural language command into a parametrized API call as defined by the OpenAPI standard or a YAML file you provide.

Here are a few ways Gemini Code Assist tools can enhance your development experience:

• Uninterrupted flow: Accessing crucial information and tools from applications without ever leaving your IDE. This minimizes distractions and maximizes focus, boosting your productivity and code quality.

• Data-driven decisions: With real-time access to data and insights from integrated partner tools, you can make informed decisions faster and more consistently. This leads to more efficient development cycles, reduced errors, and ultimately, better software.

• A comprehensive toolkit: We are constantly expanding our ecosystem by onboarding new partners and open-source technologies, creating a rich and diverse toolkit within Code Assist, ensuring you have everything you need to succeed at every stage of development.

Getting scalable, secure applications into production requires more than just writing great code — developers need solutions for productivity, observability, security, databases, and more. We’re excited to partner with industry leaders who are eager to improve the developer experience. 

Starting today, in our private preview, you can use the new tools feature in Gemini Code Assist from our launch partners including Atlassian (Rovo), GitHub, GitLab, Google Docs, Sentry, and Snyk to provide the first look into how your software development experience is better when you have information available right in the IDE.

“Gemini Code Assist is a major step forward in keeping developers in the flow by reducing the need to context switch. By connecting Atlassian Rovo to Gemini Code Assist, engineering organizations will be able to leverage the rich context already captured in tools like Jira and Confluence — where teams ideate, plan, and document technical requirements. This integration ensures every developer can instantly access technical specifications, tasks in progress, blockers, or even identify the right person to ask for help, all without leaving their coding environment.” – Josh Devenny, Head of Product for Agile and DevOps AI, Atlassian

“By leveraging GitLab’s open API, Google has seamlessly integrated GitLab with Gemini Code Assist, empowering joint customers to boost efficiency and reduce context-switching by accessing GitLab functionality directly within their IDE. This integration offers another flexible and seamless way to leverage GitLab’s industry-leading DevSecOps platform to streamline workflows and accelerate secure software delivery.” – Emilio Salvador, GitLab, VP, Strategy and Developer Relations, GitLab

“We’re thrilled to partner with Google Cloud on this initiative. Integrating Sentry’s error monitoring and performance insights directly into the developer workflow through Gemini Code Assist tools empowers developers to build and ship higher-quality software faster than ever before.” – Dave Rosenthal, CTO, Sentry

“The transformative potential of generative AI is reshaping how developers create software, and at Snyk, we’re committed to ensuring this innovation happens securely. By collaborating with Google Cloud to build a dedicated Gemini tool, we’re empowering developers to use Gemini to write code and Snyk to test and fix it — all within their IDE. This integration ensures developers can stay in their creative flow, leveraging AI to accelerate innovation while maintaining the highest standards of security.” – Danny Allan, Chief Technology Officer, Snyk

A broad ecosystem of partners

Recognizing the diverse tools developers use, we’re collaborating with many partners to integrate their technologies directly into Gemini Code Assist for a more comprehensive and streamlined development experience. These partners, and more, help developers stay in their coding flow while accessing information through tools that enhance the SDLC.

Observability

“Dynatrace is thrilled to be a launch partner for Gemini Code Assist. This collaboration with Google Cloud will further empower our customers to harness the full potential of AI, enhancing their coding efficiency and accelerating innovation. Together with Google Cloud, we’re committed to helping organizations achieve greater resilience and stay ahead in a rapidly evolving digital landscape.” – Raj Ramanujam, Regional Vice President of Global Alliances, Dynatrace

“We are excited to partner with Google Cloud and join the Tools Partner Ecosystem for Gemini Code Assist. This collaboration deepens our shared commitment to improving developer productivity — a core value at Harness — by delivering innovative AI-native solutions to accelerate and enhance software delivery.” – Brad Rydzewski, Vice President of Engineering, Harness

“Switching tools and contexts is a barrier to productivity and can result in software outages that impact business. The New Relic Intelligent Observability platform is uniquely built to enable agentic orchestration, which allows us to directly integrate with partners like Google to deliver real-time, understandable observability insights and intelligent recommendations wherever practitioners want them. Together, Google and New Relic enable anyone supporting software to act faster and smarter to prevent business-impacting incidents.” – Manav Khurana, Chief Product Officer, New Relic

Security in the SDLC

“AI is transforming the way developers work, streamlining processes and reducing the toil associated with writing code. We are excited to partner with Google on the Gemini Code Assist tools program to further advance the adoption of AI in software development. As AI becomes more integrated into the SDLC, maintaining trust in your codebase requires new strategies and tools. Through this partnership, Gemini Code Assist and SonarQube work in tandem to help developers validate AI-generated code at scale while leaders can maintain the confidence that their code remains secure, reliable, and trustworthy.” – Harry Wang, VP of Growth & New Ventures, Sonar (SonarQube)

“The opportunity to join the Gemini Code Assist tools ecosystem allows Black Duck to broaden how we empower developers to build secure applications with greater speed and confidence. By integrating our leading application security solutions with Google Cloud’s advanced AI, developers can proactively identify and mitigate vulnerabilities within their code, ensuring the integrity of their software supply chain while streamlining development workflows.” – Scott Johnson, Vice President of Product Management, Black Duck

Databases

“Aiven is thrilled to join the Gemini Code Assist Partner Ecosystem. We believe the collaboration between Google Cloud and Aiven will empower customers with seamless access to Aiven’s fully managed data platform directly within their coding environment. This integration will unlock new levels of productivity and efficiency for developers building cutting-edge applications on Google Cloud.” – John Kennedy, Head of Databases, Aiven

”We’re excited to bring together DataStax Astra DB, Langflow, and Google Gemini Code Assist. This integration empowers developers to seamlessly access their database structures, create data models, get query assistance, and design flows — all within their IDE. By embedding these capabilities directly into the coding environment, we’re significantly streamlining development workflows and enhancing productivity for developers who rely on Astra DB and Langflow.” – Ed Anuff, CPO, DataStax

“We look forward to exploring the use of Google Code Assist tools with Elastic AI Assistant. We are optimistic about the potential this integration has in optimizing development workflows, making it easier for our joint customers to build intelligent search experiences, proactively address performance issues, and strengthen application security.” – Steve Kearns, General Manager of Search, Elastic

“Gemini Code Assist is a powerful tool that helps customers and partners build applications faster. MongoDB is proud to be a launch partner with Google Cloud on the Gemini Code Assist tools ecosystem, to significantly boost development productivity at the most crucial layer of their application — the data layer. This integration will supercharge Gemini Code Assist with MongoDB domain knowledge, allowing customers to connect and interact with their MongoDB clusters using natural language.” – Andrew Davidson, SVP of Product, MongoDB

“Neo4j customers currently use Gemini to automate the creation of knowledge graphs and then to query those graphs with a GraphRAG pattern. The Neo4j integration with Gemini Code Assist tools  will simplify this architecture, reducing the cost and time for our customers to get value from these systems. Graphs allow our customers to get value from the connections in their data. We see application across verticals, including improving fraud ring detection, optimizing supply chains and improving drug discovery.” – Sudhir Hasbe, Chief Product Officer, Neo4j

“Redis is known to be loved by developers as we always strived for the best developer experience. We’re excited to partner with Google to create the Gemini Code Assist tool for Redis that enables developers to seamlessly query and validate data, as well as generate code that follows best practices and avoids anti-patterns, all without disrupting their development flow.” – Pieter Cailliau, Head of Developer Experience, Redis

“A seamless developer experience means staying in the flow without interruptions or context switches. Today, this is more achievable than ever, thanks to gen AI-driven query generation and code completion that integrate database development directly into the coding environment. With Gemini Code Assist, developers can now handle all their database tasks within the same interface they use to write code, supercharging their productivity and making the entire coding experience up to 10X faster and more efficient. We’re thrilled to empower developers to work smarter and faster with this all-in-one solution!” – Rahul Rastogi, Chief Innovation Officer, SingleStore

Partnering with the developer and application tools ecosystem

Bringing tools for Gemini Code Assist is not just beneficial for developers, it also provides immense value to our partners, offering exciting new opportunities for growth and engagement. This new feature will help Google Cloud’s partners:

• Foster direct developer connections: Bring the power of your tools directly into the hands of developers, providing unparalleled access and visibility within their daily workflow.

• Expand reach: Tap into the vast and growing user base of Gemini Code Assist, extending your reach and brand awareness within the developer community. We also proactively co-market with our partners to maximize exposure and tell a cohesive story.

Code Assist currently provides developers with a natural language interface to both traditional APIs and AI Agent APIs. Partners can quickly and easily integrate to Code Assist by onboarding to our partner program. The onboarding process is as simple as providing an OpenAPI schema, a Tool config definition file, and a set of quality evals prompts used to validate and tune the integration. Code Assist currently supports authentication to partner APIs via OAuth 2.0 Authorization Code grant type, also known as a 3LO (3-Legged OAuth) flow. In the future, we plan to add support for API Key auth as well. 

For more information or to request access to the partner program, please fill out the signup form.

Join the future of software development

The future of coding is collaborative, connected, and AI-powered. If you’re a developer who is ready to experience the power of connected development, please sign up to access the private preview of Gemini Code Assist tools. Developers can also sign up to use Gemini 2.0 Flash in Gemini Code Assist, for enhanced coding assistance capabilities in popular IDEs such as Visual Studio Code, IntelliJ, PyCharm and more. And if you are interested in building an extension for Gemini Code Assist, please use our signup form to get in contact with us.

Looker, Google Cloud’s complete AI for BI platform, is now available on the AWS Marketplace, allowing AWS customers to benefit from Looker’s powerful analytics and reporting capabilities in their environment. As the next step in Looker’s commitment to an open multicloud strategy, this milestone simplifies procurement for businesses that use AWS cloud and databases such as Amazon Redshift, empowering them to quickly and easily integrate Looker’s rich AI-driven visualizations and insights into their existing infrastructure.

Today’s businesses leverage data as a competitive advantage — all the more so with the accelerating adoption of generative AI. To unlock the full potential of your data, you need a business intelligence platform that’s:

• Built for self-service, so all employees can access and benefit from data

• Delivers instantaneous AI-powered insights to any part of the organization

• Backed by a semantic layer, helping ensure that decisions are based on the foundational truth underpinning your business

Looker is the engine that enables organizations to imagine new AI-powered capabilities and showcase their data insights in a highly visual and easily understandable way. With Gemini in Looker, including Conversational Analytics, you can have a natural language conversation with your data, and produce rich data visualizations with Studio in Looker. This complete AI-for-BI solution lets teams connect to multiple diverse data sources and create a unified view of their information, uncovering trends and embedding analytic-driven decision-making in applications and throughout the enterprise.

Your employees rely on the data and tools they use every day to be accurate and work as anticipated. With this update, Looker brings its pioneering semantic layer to AWS environments, delivering consistent, governed and trusted data definitions to the organization. This allows AI models to work against high-quality data, ensuring its insights are accurate and trustworthy.

Looker on the AWS Marketplace delivers:

• Multicloud flexibility: Looker lets organizations leverage powerful AI-for-BI capabilities across multiple clouds, including AWS, helping them gain insights within their preferred environment, avoid vendor lock-in, and easily adapt to evolving cloud strategies as needs change.

• Simplified procurement: You can streamline procurement and consolidate billing, maximizing AWS investments by purchasing Looker directly through the AWS Marketplace. This simplifies cloud investments so you can increase the value of your AWS environment.

• Universal semantic layer for trustworthy data: Looker for the AWS Marketplace customers can now build a reliable, consistent foundation for AI-driven insights. Standardizing data definitions and governance across platforms minimizes data silos and enhances data accessibility, helping organizations to make smarter, accurate, data-driven decisions.

• Access to Google AI-powered BI on AWS: Harness the power of Google Cloud’s advanced AI capabilities within your existing AWS environment, including powerful machine learning, predictive analytics, and AI-driven insights including through natural language interactions driven by Conversational Analytics.

Looker is the BI engine for your AI-driven future. To experience the power of this multi-cloud BI solution, start with Looker on AWS Marketplace today, or learn more about Looker at cloud.google.com/looker.

AI is rapidly transforming industries and redefining the future of work. However, many organizations face a significant hurdle: bridging the knowledge gap and acquiring the necessary skills to effectively harness the power of AI. 

Recognizing this challenge, Google Cloud is set to launch the AI Playground in Shoreditch, Central London, in the first quarter of 2025. This innovative space will serve as a dynamic hub for businesses and individuals to demystify AI, explore its potential, and develop practical expertise.

More than a showcase — dive deep into AI

Google’s powerful Gemini model family — now in its supercharged second generation — takes center stage at the AI Playground, featuring several interactive demos that put its multimodal and agentic capabilities on display. Among the capabilities guests can experience and experiment with first hand are Gemini’s ability to analyze complex data, generate creative formats, and power innovative solutions.

The AI Playground is much more than a technology showcase. 

We’ve built the space to serve as an immersive learning environment where visitors can actively engage with AI, participate in hands-on workshops and hackathons, and connect with Google Cloud AI experts. This unique approach fosters a deeper understanding of AI concepts and encourages experimentation with cutting-edge tools and techniques.

Addressing the AI skills gap

The AI Playground directly addresses the growing need for AI skills development in today’s rapidly evolving technological landscape, providing a dedicated space for:

• Hands-on experimentation: Gain practical experience with cutting-edge AI tools and techniques, moving beyond theoretical knowledge to real-world application.

• Skills development: Build on-demand AI skills through interactive workshops and hackathons led by Google Cloud experts, equipping individuals and teams with the expertise needed to thrive in the AI era.

• Community building: Connect with fellow AI enthusiasts, share knowledge, and facilitate collaboration, creating a vibrant ecosystem for learning and innovation.

• Real-world inspiration: Explore AI applications across diverse industries and discover new possibilities for your organization, sparking creativity and driving the development of novel AI solutions.

Empowering the future of AI

By providing accessible learning opportunities and fostering a thriving AI community, the AI Playground empowers individuals and businesses to embrace the transformative power of AI and contribute to shaping a better future. It’s a place where curiosity meets innovation, learning is hands-on, and  the potential of AI is unlocked. Mark your calendars for Q1 2025 and prepare to embark on your AI journey at Google Cloud’s AI Playground.

Enterprise workloads like SAP S/4HANA present unique challenges when migrating to a public cloud, making the choice of a cloud provider critically important. As an in-memory database for large SAP deployments, SAP HANA can have massive memory and CPU processing requirements, and that can just be the beginning of the challenges. Businesses are generating, processing, and making business decisions on more data than ever before, and they require increasingly complex data analysis, algorithms, and reporting. SAP provides the real-time operational and historical analytics and insights businesses need to make informed decisions quickly. However, this requires machines that can process and analyze large volumes of data in real time, demanding high levels of memory and compute power. 

To meet these demands, we recently introduced X4 machine types. The Compute Engine X4 machine family is purpose-built to handle the demanding requirements of SAP HANA Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads. These machines deliver strong performance, scalability, and reliability, empowering businesses to unlock the full potential of their SAP S/4HANA, SAP Business Suite (ECC) on SAP HANA deployments, and SAP’s Industry Solutions. X4 is also built to support OLAP workloads such as SAP BW/4HANA and SAP BW on HANA.

Uncompromising performance in a scalable solution

As businesses grow, whether organically or via acquisitions, they often risk hitting CPU and memory limitations of their SAP HANA system. This forces them to upgrade to larger scale-up machines earlier or using multiple machines in a scale-out configuration. Scaling out is a complex custom process that requires application data redesign and approvals from SAP, as well as being costly from an operational and resource perspective. In addition, the planned downtime required for these migration operations can be unacceptable for organizations that rely on SAP for mission-critical operations.

X4 solves these challenges by allowing businesses to grow further in scale-up mode before hitting performance bottlenecks. X4 is available in 16TB, 24TB, and 32TB memory configurations and 960, 1440, 1920 vCPU cores respectively with “standard sizing” SAP certification for both SAP HANA OLTP and OLAP capable of running the most demanding enterprise SAP HANA workloads. X4 machines also boast scale-out OLTP certifications for up to four nodes the size of the servers, for a total of 128 terabytes for S/4HANA use cases — larger than any other cloud provider’s. The X4 16TB machines achieved an SAP Benchmark IaaS SAPS result that is over 8% higher than the closest IaaS cloud’s. (Source: SAP note 2456432 – Logon required). 

As the only cloud IaaS provider offering a 32TB SAP-certified machine, Google Cloud helps ensure that your SAP HANA environment grows seamlessly with your business needs while providing a cost-effective and simple cloud-native experience. Here’s how:

• Industry-leading compute and storage: The Google Cloud X4 machine family gains its power and scale through a combination of powerful hardware and software optimizations. Integration with Google Cloud Hyperdisk provides high performance, scalability, and resilient block storage. Hyperdisk provides dedicated, dynamically tunable read/write operations per second (IOPS) and throughput with each volume up to 5GB per second, delivering substantially higher performance compared to the previous generation of Persistent Disk. This level of performance is crucial for applications that require rapid data access, like databases, real-time analytics, and reduces SAP HANA rehydration times. Additionally, X4 machines run on one of the world’s largest privately managed networks, offering low-latency performance. 

• High availability and reliability: Minimizing downtime for mission-critical SAP applications is a top priority for customers. X4 machines have an industry-leading 99.95% Compute Engine Memory-Optimized Single Instance SLA. Paired with Hyperdisk, X4 enables rapid SAP HANA rehydration capabilities (<45 min full rehydration for 32TB) that minimize application business process downtime, so your mission-critical SAP applications stay up and running. 

• Performance-boosting innovation: Titanium is a hardware- and software-based technology designed to significantly enhance workload performance, efficiency, and security while also simplifying management by automating many infrastructure-level tasks. Titanium shifts networking and storage functions away from the machine’s CPU to specialized hardware, allowing the machine’s CPU to focus on running SAP HANA applications. Titanium’s tight integration with Google Cloud’s Hyperdisk block storage service allows for best-in-class storage I/O performance (up to 400k IOPS per instance), surpassing what’s typically possible with traditional architectures. 

• Enhanced security and compliance: X4 machines inherit Google Cloud’s robust security features, including encryption of data in transit and at rest as well as compliance with industry standards and regulations with support for Google Cloud’s Assured Workloads. Titanium enhances security feature performance by offloading security-sensitive tasks like encryption and key management to dedicated hardware with enhanced protections. This helps improve the security posture of workloads, reduces the risk of vulnerabilities, and enables customers to meet their compliance and regulatory requirements.

• Simplified cloud-native experience: X4 machines offer the benefits of a cloud-native architecture for SAP S/4 HANA, integrating seamlessly with Google Cloud’s ecosystem and providing access to a wide range of cloud services. Compute Engine also offers a seamless migration experience for X4. You can easily migrate your SAP instance from M2 servers (up to 12 terabytes) to X4. The migration process from other servers or on-prem environments to X4 is the same as the standard process for migrating to any other Google Cloud server. 

• A choice of service model: When migrating to Google Cloud, SAP customers have two choices. You can choose a direct (BYOL) deployment model, or you can take advantage of RISE with SAP, which brings solutions and expertise from SAP and technology ecosystem partners into one single-contract subscription offering.

  “In the past few years, our SAP HANA systems have seen significant data growth with an increasing need for higher performance. With the 24TB X4 machines and Hyperdisk storage, we have been able to raise the ceiling for our future data growth and are also looking to see improvements in our performance. Added to this, Google’s X4 machines are cloud native, giving us opportunities to automate system management and operations.” – Shawn Lund, US Chief Technology Officer, Deloitte

Google Cloud’s X4 machines represent a significant advancement in SAP HANA infrastructure, offering high performance, scalability, and cost-efficiency. By integrating with Google Cloud’s powerful ecosystem and providing a cloud-native experience, X4 machines empower businesses to unlock the full potential of their SAP S/4HANA deployments and drive digital transformation. 

Learn more about X4 machines and RISE with SAP on Google Cloud. View SAP Datasphere for Google BigQuery on Google Cloud Marketplace.