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.