Cloud

Your business data sets you apart from the competition. It fuels your innovations, your culture, and provides all your employees a foundation from which to build and explore. Since 2022, enterprises in all industries have turned to Looker Studio Pro to empower their businesses with self-service dashboards and AI-driven visualizations and insights, complete with advanced enterprise capabilities and Google Cloud technical support.

As the Looker community has grown, we’ve gotten more requests for guidance on how users can make their Looker Studio Pro environments even stronger, and tap into more sophisticated features. Those requests have only increased, accelerated by the debut of Studio in Looker, which brings Looker Studio Pro to the broader Looker platform. To help, today we are debuting a new on-demand training course: Looker Studio Pro Essentials.

Looker Studio Pro connects businesses’ need to govern data access with individual employees’ needs to explore, build and ask questions. This Google Cloud Skills Boost course helps users go beyond the basics of setting up reports and visualizations, and provides a deep dive into Looker Studio Pro’s more powerful features and capabilities.

Here’s what you can expect to get from this course:

• Gain a comprehensive understanding of Looker Studio Pro: Explore its key features and functionality, and discover how it elevates your data analysis capabilities.

• Enhance collaboration: Learn how to create and manage collaborative workspaces, streamline report sharing, and automate report delivery.

• Schedule and share reports: Learn how to customize scheduling options to your business, including delivery of reports to multiple recipients via Google Chat and email, based on your sharing preferences.

• Ensure data security and control: Become an expert in user management, audit log monitoring, and other essential administrative tasks that can help you maintain data integrity.

• Leverage Google Cloud customer care: Learn how to use Google Cloud Customer Care resources to find solutions, report issues, and provide feedback.

From your focus, to your employees, to your customers, your business is unique. That’s why we designed this course to bring value to everyone — from sales and marketing professionals, to data analysts, to product innovators — providing them with the knowledge and skills they need to fully leverage Looker Studio Pro in their own environments. Because in the gen AI era, how you leverage your data and invigorate your employees to do more is the true opportunity. Accelerate that opportunity with the new Looker Studio Pro Essentials course today.

For developers and businesses that run applications on Google Kubernetes Engine (GKE), scaling deployments down to zero when they are idle can offer significant financial savings. GKE’s Cluster Autoscaler efficiently manages node pool sizes, but for applications that require complete shutdown and startup (scaling the node pool all the way to and from zero), you need an alternative, as GKE doesn’t natively offer scale-to-zero functionality. This is important for applications with intermittent workloads or varying traffic patterns. 

In this blog post, we demonstrate how to integrate the open-source Kubernetes Event-driven Autoscaler (KEDA) to achieve this. With KEDA, you can align your costs directly with your needs, paying only for the resources consumed.

Why scale to zero?

Minimizing costs is a primary driver for scaling to zero, and applies to a wide variety of scenarios. For technical experts, this is particularly crucial when dealing with:

• GPU-intensive workloads: AI/ML workloads often require powerful GPUs, which can be expensive to keep running even when idle.

• Applications with predictable downtime: Internal tools with specific usage hours — scale down resources for applications used only during business hours or specific days of the week.

• Seasonal applications: Scale to zero during the off-season for applications with predictable periods of low activity.

• On-demand staging environments: Replicate production environments for testing and validation, scaling them to zero after testing is complete.

• Development, demo and proof-of-concept environments:

  • • Short-term demonstrations: Showcase applications or features to clients or stakeholders, scaling down resources after the demonstration.

    • Temporary proof-of-concept deployments: Test new ideas or technologies in a live environment, scaling to zero after evaluation.

    • Development environment: Spin up resources for testing, code reviews, or feature branches and scale them down to zero when not needed, optimizing costs for temporary workloads.

• Event-driven applications:

• Microservices with sporadic traffic: Scale individual services to zero when they are idle and automatically scale them up when requests arrive, optimizing resource utilization for unpredictable traffic patterns.

• Serverless functions: Execute code in response to events without managing servers, automatically scaling to zero when inactive.

Introducing KEDA for GKE

KEDA is an open-source, Kubernetes-native solution that enables you to scale deployments based on a variety of metrics and events. KEDA can trigger scaling actions based on external events such as message queue depth or incoming HTTP requests. And unlike the current implementation of Horizontal Pod Autoscaler (HPA), KEDA supports scaling workloads to zero, making it a strong choice for handling intermittent jobs or applications with fluctuating demand.

Use cases

Let’s explore two common scenarios where KEDA’s scale-to-zero capabilities are beneficial:

1. Scaling a Pub/Sub worker

• Scenario: A deployment processes messages from a Pub/Sub topic. When no messages are available, scaling down to zero saves resources and costs.

• Solution: KEDA’s Pub/Sub scaler monitors the message queue and triggers scaling actions accordingly. By configuring a ScaledObject resource, you can specify that the deployment scales down to zero replicas when the queue is empty.

2. Scaling a GPU-dependent workload, such as an Ollama deployment for LLM serving

• Scenario: An Ollama-based large language model (LLM) performs inference tasks. To minimize GPU usage and costs, the deployment needs to scale down to zero when there are no inference requests.

• Solution: Combining HTTP-KEDA (a beta feature of KEDA) with Ollama enables scale-to-zero functionality. HTTP-KEDA scales deployments based on HTTP request metrics, while Ollama serves the LLM.

Get started with KEDA on GKE

KEDA offers a powerful and flexible solution for achieving scale-to-zero functionality on GKE. By leveraging KEDA’s event-driven scaling capabilities, you can optimize resource utilization, minimize costs, and improve the efficiency of your Kubernetes deployments. Please remember to validate usage scenarios as scale to zero mechanism can influence workload performance. Scaling to zero can increase latency due to cold starts. When an application scales to zero, it means there are no running instances. When a request comes in, a new instance has to be started, increasing latency. 

There are also considerations about state management. When instances are terminated, any in-memory state is lost.

To help you get started quickly, we’ve published a guide for scaling GKE to zero with KEDA, with specific steps for scaling a Pub/Sub worker to zero with KEDA on GKE and scaling a Ollama to zero with KEDA on GKE. 

To learn more about KEDA and its various scalers, refer to the official KEDA documentation at https://keda.sh/docs/latest.

Dun & Bradstreet, a leading global provider of business data and analytics, is committed to maintaining its position at the forefront of innovation. For the past two years, this commitment has included the company’s deliberate approach to improving its software development lifecycle by infusing AI solutions. 

While development velocity and security were the company’s most pressing considerations, Dun & Bradstreet was also inundated with productivity and operational challenges common to many global enterprises which included: 

• Significant time onboarding new team members 

• Siloed knowledge of legacy codebases 

• Low test coverage

• Application modernization challenges

To achieve its goal of accelerating software development, Dun & Bradstreet knew it had to take a holistic “people, process, and tools” approach to solve the traditional development lifecycle issues that most enterprise engineering teams face. They looked to AI-assistance to anchor this new effort.

Finding a partner for the future of the software development lifecycle

As a provider of information that can move markets and drive economies, Dun & Bradstreet had a high bar for any technology tools, with demanding expectations as high as the financial professionals and government leaders they serve. 

Dun & Bradstreet executed a thorough evaluation process to identify the best partner and coding assistance tool, considering both open-source and commercial options. The company ultimately selected Gemini Code Assist due to the Gemini model’s performance, seamless integration with their existing development environment, and robust security features.

The implementation of Gemini Code Assist was a collaborative effort between Dun & Bradstreet’s development teams and the Google Cloud team. The developers who were part of the team were actively involved in the configuration and customization of the tool to ensure that it met their specific needs and workflows.

A key focus area for Dun & Bradstreet was Google’s security stance. Incorporating AI into the development process required both top-grade protection of private data and guardrails to ensure the safety of machine-generated code. Google’s security expertise and guidance allowed Dun & Bradstreet to move forward with confidence due to the following factors:

• Gemini models are built in-house, allowing Google to fully validate and filter all source code samples used in model training.

• Trust and verify: Integration into a company’s existing coding and review lifecycles allows developers to guide the model outputs with human oversight, without learning a whole new system.

• Google’s partnership with Snyk provides additional options for automated security scanning, covering both AI-generated and human-written code.

• Google’s AI Principles underpin the architecture and design decisions for Gemini Code Assist. Privacy and security protections include single-tenant storage of customer code references, encrypted logging, and fine-grained administrative controls to prevent accidental data leakage.

• Google’s indemnification policies.

“AI-assisted code creation is not just a leap forward in efficiency — it’s a testament to how innovation and security can work hand-in-hand to drive business success,” said Jay DePaul, chief cybersecurity technology risk officer at Dun & Bradstreet. “By embedding robust guardrails, we’re enabling our teams to build faster, safer, and smarter.”

Transformation in action

Dun & Bradstreet decided to move forward with Code Assist in October 2024. The solution is now starting to roll out to more teams across the organization. Adoption has been smooth, aided by Code Assist’s intuitive interface and comprehensive documentation.

Having a program for incubation at large organizations helps to iron out both technical and potential adoption blockers. For example, the Dun & Bradstreet team identified the need to educate teams on how coding assistants are there to help developers as a partner, not as replacements.

Now that the rollout is underway, Dun & Bradstreet is sharing the factors that drove their adoption of Gemini Code Assist.

• Increased developer productivity: Gemini Code Assist’s AI-powered code suggestions and completions have significantly reduced the time developers spend writing code. The tool’s ability to automate repetitive tasks has freed up time for the developers so they can focus on more complex and creative aspects of their work.

• Improved code quality: The automated code review and linting capabilities of Gemini Code Assist helped Dun & Bradstreet’s developers detect errors and potential issues early in the development process. This has led to a significant reduction in bugs and improved overall code quality.

• Easier application modernization: A significant amount of time was saved when converting Spring apps to Kotlin.

• Increased developer efficiency: Early internal indicators show a 30% increase in developer productivity.

• Developer onboarding: New developers at Dun & Bradstreet have been able to ramp up quicker due to the real-time guidance and support provided by Gemini Code Assist. 

• Enhanced knowledge sharing: Gemini Code Assist has fostered a culture of knowledge sharing within Dun & Bradstreet’s development teams. The tool’s ability to provide code examples and best practices made it easier for developers to learn from each other and collaborate effectively.

Leading the way with AI

Gemini Code Assist has proven to be a valuable solution for Dun & Bradstreet as it has empowered their developers with advanced AI capabilities and intelligent code assistance. 

“AI-assisted code creation is a game changer for everyone involved in the solution-delivery business,” said Adam Fayne, vice president for Enterprise Engineering at Dun & Bradstreet. “It enables our teams to innovate, test, and deploy faster, without having to risk security or quality.”

The company has been able to accelerate velocity, improve software quality, and maintain its competitive edge in the market. Companies like Dun & Bradstreet trust Google Cloud and Gemini to greatly enhance their software developer lifecycles. In fact, Google Cloud was recently named a Leader in the 2024 Gartner Magic Quadrant for AI Code Assistants for its Completeness of Vision and Ability to Execute.

Visit the Gemini Code Assist website for more information on use cases and how to start using it today.

Ford Pro Intelligence is a cloud-based platform that is used for managing and supporting fleet operations of its commercial customers. Ford commercial customers range from small businesses, large enterprises like United Postal Service and Pepsi where fleets can be thousands of vehicles, and government groups and municipalities like the City of Dallas. The Ford Pro Intelligence platform collects connected vehicle data from fleet vehicles to help fleet operators streamline operations, increase productivity, reduce cost of ownership, and improve their fleet’s performance and overall uptime through the alerts on vehicle health and maintenance. 

Telemetry data from vehicles provides a wealth of opportunity, but it also presents a challenge: planning for the future as cars and services evolve. We needed a platform that could support the volume, variety and velocity of vehicle data as automotive innovations emerge, including new types of car sensors, more advanced vehicles, and increased integration with augmented data sources like driver information, local weather, road conditions, maps, and more.

In this blog, we’ll discuss our technical requirements, the decision-making process, and how building our platform with Bigtable, Google Cloud’s flexible NoSQL database for high throughput and low-latency applications at scale, unlocked powerful features for our customers like real-time vehicle health notifications, AI-powered predictive maintenance, and in-depth fleet monitoring dashboards.

Scoping the problem

We wanted to set some goals for our platform based on our connected vehicle data. One of our primary goals is to provide real-time information for fleet managers. For example, we want to inform our fleet partners immediately if tire pressure is low, a vehicle requires brake maintenance, or there is an airbag activation, so they can take action. 

Connected vehicle data can be extremely complex and variable. When Ford Pro set out to build its vehicle telemetry platform, we knew we needed a database that could handle some unique challenges. Here’s what we had to consider:

• A diverse and growing vehicle ecosystem: We handle telemetry data from dozens of car and truck models, with new sensors added every year to support different requirements. Plus, we support non-Ford vehicles too!

• Connectivity isn’t a guarantee: A “connected” car isn’t always connected. Vehicles go offline due to spotty service or even just driving through a tunnel. Our platform needs to handle unpredictable or duplicated streams of time-series data.

• Vehicles are constantly evolving: Manufacturers frequently push over-the-air updates that change how vehicles operate and the telemetry data they generate. This means our data is highly dynamic, and our database needs to support a flexible, ever-evolving schema.

• Security is paramount: At Ford, we are committed to our customer’s data privacy and security. It’s imperative to our technology. We serve customers around the world and must ensure we can easily incorporate privacy and security measures while maintaining regulatory compliance, such as GDPR, in every country we operate.

These challenges, along with the application feature requirements, we knew that we needed an operational data store that can support low-latency access for both real-time and historical data with a flexible schema. 

Where we started

The Ford Pro Intelligence platform offers a diverse range of features and services that cater to the diverse needs of our customers. To ensure flexibility in data access, we prioritize real-time reporting of vehicle status, event-based notifications, location services, and historical journey reconstruction. These capabilities necessitate a variety of data access methods to support both real-time and historical data access — all while maintaining low latency and high throughput to meet the demands of Ford customers.

Our starting point was an Apache Druid-based data warehouse that contained valuable historical data. While Apache Druid could handle high-throughput write traffic and generate reports, it was not able to support our low-latency API requirements or high data volumes. As a result, we started working with Google Cloud to explore our options. 

We began our search with BigQuery. We already used BigQuery for reporting, so this option would have given us a serverless, managed version of what we already had. While BigQuery was able to perform the queries we wanted, our API team raised concerns about latency and scale — we required single-digit millisecond latency with high throughput. We discussed putting a cache layer in front of BigQuery for faster service of the latest data but soon discovered that it wouldn’t scale for the volume and variety of requests we wanted to offer our customers.

From there, we considered several alternative options, including Memorystore and PostgreSQL. While each of these solutions offered certain advantages, they did not meet some of our specific requirements in several key areas. We prioritized low-latency performance to ensure real-time processing of data and seamless user experiences. Flexibility, in terms of schema design, to accommodate our evolving data structures and wide column requirements was also a must. Scalability was another crucial factor as we anticipated significant growth in data volume and traffic over time. 

When we looked at Bigtable, its core features of scalable throughput and low latency made it a strong contender. NoSQL is an ideal option for creating a flexible schema, and Bigtable doesn’t store empty values, which is great for our sparse data and cost optimization. Time-series data is also inherent to Bigtable’s design; all data written is versioned with a timestamp, making it a naturally good fit for use cases with vehicle telemetry data. Bigtable also met our needs for an operational data store and analytics data source, allowing us to handle both of these workloads at scale on a single platform. In addition, Bigtable’s data lifecycle management features are specifically geared toward handling the time-oriented nature of vehicle telemetry data. The automated garbage collection policies use time and version as criteria for purging obsolete data effectively, enabling us to manage storage costs and reduce operational overhead. 

In the end, the choice was obvious, and we decided to use Bigtable as our central vehicle telemetry data repository.

Ford Pro Telematics and Bigtable

We receive vehicle telemetry data as a protocol buffer to a passthrough service hosted on Compute Engine. We then push that data to Pub/Sub for Google-scale processing by a streaming Dataflow job that writes to Bigtable. Ford Pro customers can access data through our dashboard or an API for both historical lookback for things like journey construction and real-time access to see fleet status, position, and activity.

With Bigtable helping to power Ford Pro Telematics, we have been able to provide a number of benefits for our customers, including:

• Enabling the API service to access telematics data 

• Improving data quality with Bigtable’s built-in time series data management features

• Reducing operational overhead with a fully managed service

• Delivering robust data regulation compliance tooling across regions 

The platform provides interactive dashboards that display relevant information, such as real-time vehicle locations, trip history, detailed trip information, live map tracking and EV charging status. Customers can also set up real-time notifications about vehicle health and other important events, such accidents, delays, or EV charging faults. For example, a fleet manager can use the dashboard to track the location of a vehicle and dispatch assistance if an accident occurs.

We leverage BigQuery alongside Bigtable to generate reports. BigQuery is used for long-running reports and analysis, while Bigtable is used for real-time reporting, and direct access to vehicle telemetry. Regular reports are available for fleet managers, including vehicle consumption, driver reimbursement reports, and monthly trip wrap ups. Our customers can also leverage and integrate this data into their own tooling using our APIs, which enable them to query vehicle status and access up to one year of historical data.

Looking to the future

The automotive industry is constantly evolving, and with the advent of connected vehicles, there are more opportunities than ever before to improve the Ford commercial customer experience. Adopting a fully managed service like Bigtable allows us to spend less time maintaining our own infrastructure and more time innovating and adding new features to our platform. Our company is excited to be at the forefront of this innovation, and we see many ways that we can use our platform to help our customers.

One of the most exciting possibilities is the use of machine learning to predict vehicle maintenance and create service schedules. By collecting data from vehicle diagnostics over time, we can feed this information into machine learning models that can identify patterns and trends. This will allow us to alert customers to potential problems before they even occur, and to schedule service appointments at the most convenient times.

Another area where we can help our customers is in improving efficiency. By providing insights about charging patterns, EV range, and fuel consumption, we can help fleet managers optimize their operations. For example, if a fleet manager knows that there are some shorter routes for their cars, they can let those cars hit the road without a full charge. This can save time and money, and it can also reduce emissions.

In addition to helping our customers save time and money, we are also committed to improving their safety and security. Our platform can provide alerts for warning lights, oil life, and model recalls. This information can help customers stay safe on the road, and it can also help them avoid costly repairs.

We are already getting great feedback from customers about our platform, and we are looking forward to further increasing their safety, security, and productivity. We believe that our platform has the potential to revolutionize the automotive industry, and we are excited to be a part of this journey.

Get started

• Learn more about Bigtable and why it is a great solution for automotive telemetry and time series data. 

• Read more on how others like Palo Alto Networks, Flipkart, and Globo are reducing cloud spend while improving service performance, scalability and reliability by moving to Bigtable.

• Ready to unlock the power of Bigtable? Start a free trial today!

Tis the season for learning new skills! Get ready for 12 Days of Learning, a festive digital advent calendar packed with courses, hands-on labs, videos, and community opportunities—all designed to boost your generative AI expertise. Discover a new learning resource on Google Cloud’s social channels every day for twelve days this December. 

Before you start: Get no-cost access to generative AI courses and labs

Join the Innovators community to activate 35 monthly learning credits in Google Cloud Skills Boost at no cost. Use these credits to access courses and labs throughout the month of December—and beyond! 

Ready to get started? Review all of the resources below.

Get festive with generative AI foundations

Learn how to use gen AI in your day-to-day work. These resources are designed for developers looking to gain foundational knowledge in gen AI.

1. A Developer’s Guide to LLMs: In this 10-minute video, explore the exciting world of large language models (LLMs). Discover different AI model options, analyze pricing structures, and delve into essential features.

2. Responsible AI: Fairness & Bias: This course introduces the concepts of responsible AI and shares practical methods to help you implement best practices using Google Cloud products and open source tools.

3. Gemini for end-to-end SDLC: This course explores how Google Cloud’s Gemini AI can assist in all stages of the software development lifecycle, from building and debugging web applications to testing and data querying. The course ends with a hands-on lab where you can build practical experience with Gemini.

4. Responsible AI for Developers: Interpretability & Transparency: This course introduces AI interpretability and transparency concepts. Learn how to train a classification model on image data and deploy it to Vertex AI to serve predictions with explanations.

5. Introduction to Security in the World of AI: This course equips security and data protection leaders with strategies to securely manage AI within their organizations. Bring these concepts to life with real-world scenarios from four different industries.

Cozy up with gen AI use cases

Launch these courses and labs to get more in-depth, hands-on experience with generative AI, from working with Gemini models to building agents and applications. 

1. Build Generative AI Agents with Vertex AI and Flutter: Learn how to develop an app using Flutter and then integrate the app with Gemini. Then use Vertex AI Agent Builder to build and manage AI agents and applications.

2. Machine Learning Ops (MLOps) for Generative AI: In this course, learn how to overcome MLOps challenges and use generative AI to streamline processes.

3. Boost productivity with Gemini in BigQuery: This course provides an overview of features to assist in the data-to-AI workflow, including data exploration and preparation, code generation, workflow discovery, and visualization. 

4. Build Generative AI Apps with Firebase Genkit: Learn how to integrate gen AI features into your applications using Firebase Genkit—from prototyping to production. 

5. Website Modernization with Generative AI on Google Cloud: Transform your website experiences with gen AI with this hands-on course. Learn how to build a generative search experience in an interactive lab.

6. Work with Gemini models in BigQuery: Through a practical use case involving customer relationship management, learn how to solve a real-world business problem with Gemini models. Plus, receive step-by-step guidance through coding solutions using SQL queries and Python notebooks.

Get a jump-start on your New Year’s resolutions with AI Skills Quest

Get an early start on your learning goals by signing up for AI Skills Quest, a monthly learning challenge that puts gen AI on your resume with verifiable skill badges. When you sign up, choose your path based on your level of knowledge:

• Beginner/Intermediate cohort: Learn fundamental AI concepts, prompt design, and Gemini app development techniques in Vertex AI, plus other Gemini integrations in various technical workflows.

• Advanced cohort: Already know the basics, but want to add breadth and depth to your AI skills? Sign up for the Advanced path to learn advanced AI concepts like RAG and MLOps.

Ready to ring in the new year with new skills? Find more generative AI learning content on Google Cloud Skills Boost.