Today at Google Cloud Next we are announcing innovations that will enable data teams to simplify how they work with data and derive value from it faster. These new solutions will help organizations build modern data architectures with real-time analytics to power innovative, mission-critical, data-driven applications. 

Too often, even the best minds in data are constrained by ineffective systems and technologies. A recent study showed that only 32% of companies surveyed gained value from their data investments. Previous approaches have resulted in difficult to access, slow, unreliable, complex, and fragmented systems. 

At Google Cloud, we are committed to changing this reality by helping customers simplify their approach to data to build their data clouds. Google Cloud’s data platform is simply unmatched for speed, scale, security, and reliability for any size organization with built-in, industry-leading machine learning (ML) and artificial intelligence (AI), and an open standards-based approach.

Vertex AI and data platform services unlock rapid ML modeling 

With the launch of Vertex AI in May 2021, we empowered data scientists and engineers to build reliable, standardized AI pipelines that take advantage of the power of Google Cloud’s data pipelines. Today, we are taking this a step further with the launch of Vertex AI Workbench, a unified user experience to build and deploy ML models faster, accelerating time-to-value for data scientists and their organizations. We’ve integrated data engineering capabilities directly into the data science environment, which lets you ingest and analyze data, and deploy and manage ML models, all from a single interface.

Data scientists can now build and train models 5X faster on Vertex AI than on traditional notebooks. This is primarily enabled by integrations across data services (like Dataproc, BigQuery, Dataplex, and Looker), which significantly reduce context switching. The unified experience of Vertex AI let’s data scientists coordinate, transform, secure and monitor Machine Learning Operations (MLOps) from within a single interface, for their long-running, self-improving, and safely-managed AI services.

“As per IDC’s AI StrategiesView 2021, model development duration, scalable deployment, and model management are three of the top five challenges in scaling AI initiatives,” said Ritu Jyoti, Group Vice President, AI and Automation Research Practice at IDC. “Vertex AI Workbench provides a collaborative development environment for the entire ML workflow – connecting data services such as BigQuery and Spark on Google Cloud, to Vertex AI and MLOps services. As such, data scientists and engineers will be able to deploy and manage more models, more easily and quickly, from within one interface.”

Ecommerce company, Wayfair, has transformed its merchandising capabilities with data and AI services. “At Wayfair, data is at the center of our business. With more than 22 million products from more than 16,000 suppliers, the process of helping customers find the exact right item for their needs across our vast ecosystem presents exciting challenges,” said Matt Ferrari, Head of Ad Tech, Customer Intelligence, and Machine Learning; Engineering and Product at Wayfair. “From managing our online catalog and inventory, to building a strong logistics network, to making it easier to share product data with suppliers, we rely on services including BigQuery to ensure that we are able to access high-performance, low-maintenance data at scale. Vertex AI Workbench and Vertex AI Training accelerate our adoption of highly scalable model development and training capabilities.”

BigQuery Omni: Breaking data silos with cross-cloud analytics and governance

Businesses across a variety of industries are choosing Google Cloud to develop their data cloud strategies and better predict business outcomes — BigQuery is a key part of that solution portfolio. To address complex data management across hybrid and multicloud environments, this month we are announcing the general availability of BigQuery Omni, which allows customers to analyze data across Google Cloud, AWS, and Azure. Healthcare provider, Johnson and Johnson was able to combine data in Google Cloud and AWS S3 with BigQuery Omni without needing data to migrate. 

This flexible, fully-managed, cross-cloud analytics solution allows you to cost-effectively and securely answer questions and share results from a single pane of glass across your datasets, wherever you are. In addition to these multicloud capabilities, Dataplex will be generally available this quarter to provide an intelligent data fabric that enables you to keep your data distributed while making it securely accessible to all your analytics tools.

Spark on Google Cloud simplifies data engineering 

To help make data engineering even easier, we are announcing the general availability of Spark on Google Cloud, the world’s first autoscaling and serverless Spark service for the Google Cloud data platform. This allows data engineers, data scientists, and data analysts to use Spark from their preferred interfaces without data replication or custom integrations. Using this capability, developers can write applications and pipelines that autoscale without any manual infrastructure provisioning or tuning. This new service makes Spark a first class citizen on Google Cloud, and enables customers to get started in seconds and scale infinitely, regardless if you start in BigQuery, Dataproc, Dataplex, or Vertex AI.

Spanner meets PostgreSQL: global, relational scale with a popular interface

We’re continuing to make Cloud Spanner, our fully managed, globally scalable, relational database, available to more customers now with a PostgreSQL interface, now in preview. With this new PostgreSQL interface, enterprises can take advantage of Spanner’s unmatched global scale, 99.999% availability, and strong consistency using skills and tools from the popular PostgreSQL ecosystem. 

This interface supports Spanner’s rich feature set that uses the most popular PostgreSQL data types and SQL features to reduce the barrier to entry for building transformational applications. Using the tools and skills they already have, developer teams gain flexibility and peace of mind because the schemas and queries they build against the PostgreSQL interface can be easily ported to another Postgres environment. Complete this form to request access to the preview.

Our commitment to the PostgreSQL ecosystem has been long standing. Customers choose Cloud SQL for the flexibility to run PostgreSQL, MySQL and SQL Server workloads. Cloud SQL provides a rich extension collection, configuration flags, and open ecosystem, without the hassle of database provisioning, storage capacity management, or other time-consuming tasks.

Auto Trader has migrated approximately 65% of their Oracle footprint to Cloud SQL, which remains a strategic priority for the company. Using Cloud SQL, BigQuery, and Looker to facilitate access to data for their users, and with Cloud SQL’s fully managed services, Auto Trader’s release cadence has improved by over 140% (year-over-year), enabling an impressive peak of 458 releases to production in a single day.

Looker integrations make augmented analytics a reality

We are announcing a new integration between Tableau and Looker that will allow customers to operationalize analytics and more effectively scale their deployments with trusted, real-time data, and less maintenance for developers and administrators. Tableau customers will soon be able to leverage Looker’s semantic model, enabling new levels of data governance while democratizing access to data. They will also be able to pair their enterprise semantic layer with Tableau’s leading analytics platform. The future might be uncertain, but together with our partners we can help you plan for it. 

We remain committed to developing new ways to help organizations go beyond traditional business intelligence with Looker. In addition to innovating within Looker, we’re continuing to integrate within other parts of Google Cloud. Today, we are sharing new ways to help customers deliver trusted data experiences and leverage augmented analytics to take intelligent action. 

First, we’re enabling you to democratize access to trusted data in tools where you are already familiar. Connected Sheets already allows you to interactively explore BigQuery data in a familiar spreadsheet interface and will soon be able to leverage the governed data and business metrics in Looker’s semantic model. It will be available in preview by the end of this year. 

Another integration we’re announcing is Looker’s Solution for Contact Center AI, which helps you gain a deeper understanding and appreciation of your customers’ full journey by unlocking insights from all of your company’s first-party data, such as contextualizing support calls to make sure your most valuable customers receive the best service. 

We’re also sharing the new Looker Block for Healthcare NLP API, which provides simplified access to intelligent insights from unstructured medical text. Compatible with Fast Healthcare Interoperability Resources (FHIR), healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, and in turn, can begin to link this to other clinical data sources for additional AI and ML actions. 

Bringing the best of Google together with Google Earth Engine and Google Cloud

We are thrilled to announce the preview of Google Earth Engine on Google Cloud. This launch makes Google Earth Engine’s 50+ petabyte catalog of satellite imagery and geospatial data sets available for planetary-scale analysis. Google Cloud customers will be able to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and Google Maps Platform. This gives data teams a way to better understand how the world is changing and what actions they can take — from sustainable sourcing, to saving energy and materials costs, to understanding business risks, to serving new customer needs. 

For over a decade, Earth Engine has supported the work of researchers and NGOs from around the world, and this new integration brings the best of Google and Google Cloud together to empower enterprises to create a sustainable future for our planet and for your business.

At Google Cloud, we are deeply grateful to work with companies of all sizes, and across industries, to build their data clouds. Join my keynote session to hear how organizations are leveraging the full power of data, from databases to analytics that support decision making to AI and ML that predict and automate the future. We’ll also highlight our latest product innovations for BigQuery, Spanner, Looker, and Vertex AI.

I can’t wait to hear how you will turn data into intelligence and look forward to connecting with you.

Over one third of organizations believe that data analytics and machine learning have the most potential to significantly alter the way they run business over the next 3 to 5 years. However, only 26% of organizations are data driven. One of the biggest reasons for this gap is that a major portion of the data generated today is unstructured, which includes images, documents, and videos. It is estimated to cover roughly up to 80% of all data, which has so far remained untapped by organizations.

One of the goals of Google’s data cloud is to help customers realize value from data of all types and formats. Earlier this year, we announced BigLake, which unifies data lakes and warehouses under a single management framework, enabling you to analyze, search, secure, govern and share unstructured data using BigQuery.

At Next ‘22, we announced the preview of object tables, a new table type in BigQuery that provides a structured record interface for unstructured data stored in Google Cloud Storage. This enables you to directly run analytics and machine learning on images, audio, documents and other file types using existing frameworks like SQL and remote functions natively in BigQuery itself. Object tables also extend our best practices of securing, sharing and governing structured data to unstructured, without needing to learn or deploy new tools.

Directly process unstructured data using BigQuery ML

Object tables contain metadata such as URI (Uniform Resource Identifier), content type, and size that can be queried just like other BigQuery tables. You can then derive inferences using machine learning models on unstructured data with BigQuery ML. As part of preview, you can import open source TensorFlow Hub image models, or your own custom models to annotate the images. Very soon, we plan to enable this for audio, video, text and many other formats, and pre-trained models to enable out-of-the box analysis. Check out this video to learn more and watch a demo.

Create an object table

CREATE EXTERNAL TABLE my_dataset.object_table
WITH CONNECTION us.my_connection
OPTIONS(uris=["gs://mybucket/images/*.jpg"],
object_metadata="SIMPLE", metadata_cache_mode="AUTOMATIC");
​
# Generate inferences with BQML
SELECT * FROM ML.PREDICT(
MODEL my_dataset.vision_model,
(SELECT ML.DECODE_IMAGE(data) AS img FROM my_dataset.object_table)
);

By analyzing unstructured data natively in BigQuery, businesses can

• Eliminate manual effort as pre-processing steps such as tuning image sizes to model requirements are automated
• Leverage the simple and familiar SQL interface to quickly gain insights
• Save costs by utilizing existing BigQuery slots without needing to provision new forms of compute

Adswerve is a leading Google Marketing, Analytics and Cloud partner on a mission to humanize data. Twiddy & Co. is Adswerve’s client – a vacation rental company in North Carolina. By combining structured and unstructured data, Twiddy and Adswerve used BigQuery ML to analyze images of rental listings and predict the click-through rate, enabling data-driven photo editorial decisions.

“Twiddy now has the capability to use advanced image analysis to stay competitive in an ever changing landscape of vacation rental providers – and can do this using their in-house SQL skills.” said Pat Grady, Technology Evangelist, Adswerve

Process unstructured data using remote functions

Customers today use remote functions (UDFs) to process structured data for languages and libraries that are not supported in BigQuery. We are extending this capability to process unstructured data using object tables.

Object tables provide signed URLs to allow remote UDFs running on Cloud Functions or Cloud Run to process the object table content. This is particularly useful for running Google’s pre-trained AI models, including Vision AI, Speech-to-Text, Document AI, open source libraries such as Apache Tika, or deploying your own custom models where performance SLAs are important.

Here’s an example of an object table being created over PDF files that are parsed using an open source library running as a remote UDF.

SELECT uri, extract_title(samples.parse_tika(signed_url)) AS title<br>FROM EXTERNAL_OBJECT_TRANSFORM(TABLE pdf_files_object_table,<br>["SIGNED_URL"]);

Extending more BigQuery capabilities to unstructured data

Business intelligence – The results of analyzing unstructured data either directly in BigQuery ML or via UDFs can be combined with your structured data to build unified reports using Looker Studio (at no charge), Looker or any of your preferred BI solutions. This allows you to gain more comprehensive business insights. For example, online retailers can analyze product return rates by correlating them with the images of defective products. Similarly, digital advertisers can correlate ad performance with various attributes of ad creatives to make more informed decisions.

BigQuery search index – Customers are increasingly using the search functionality of BigQuery to power search use cases. These capabilities now extend to unstructured data analytics as well. Whether you use BigQueryML to produce inference on images or use remote UDFs with Doc AI to produce document extraction, the results can now be search indexed and used to support search access patterns.

Here’s an example of search index on data that is parsed from PDF files:

CREATE SEARCH INDEX my_index ON pdf_text_extract(ALL COLUMNS);
​
SELECT * FROM pdf_text_extract WHERE SEARCH(pdf_text, "Google");

Security and governance – We are extending BigQuery’s row-level security capabilities to help you secure objects in Google Cloud Storage. By securing specific rows in an object table, you can restrict the ability of end users to retrieve the signed URLs of corresponding URIs present in the table. This is a shared responsibility security model, for which administrators need to ensure that end users don’t have direct access to Google Cloud Storage, and use signed URLs from object tables as the only access mechanism.

Here’s an example of a policy for PII images that are secured to be first processed through a blur pipeline:

CREATE ROW ACCESS POLICY pii_data ON object_table_images
GRANT TO ("group:admin@example.com")
FILTER USING (ARRAY_LENGTH(metadata)=1 AND
metadata[OFFSET(0)].name="face_detected")

Soon, Dataplex will support object tables, allowing you to automatically create object tables in BigQuery and manage and govern unstructured data at scale.

Data sharing – You can now use Analytics Hub to share unstructured data with partners, customers and suppliers while not compromising on security and governance. Subscribers can consume the rows of object tables that are shared with them, and use signed URLs for unstructured data objects.

Getting Started

Submit this form to try these new capabilities that unlock the power of your unstructured data in BigQuery. Watch this demo to learn more about these new capabilities.

Special thanks to engineering leaders Amir Hormati, Justin Levandoski and Yuri Volobuev for contributing to this post.

97 zettabytes was the estimated volume of data generated worldwide in 2022¹. This sort of explosion in data volume is happening in every enterprise. Now imagine being able to access all this data you own from anywhere, at any time, analyze it, and leverage its insights to innovate your products and services? One of the biggest barriers to fulfilling this vision is the complexity inherent in dealing with data trapped across silos in an enterprise. Google Data Cloud offers a unified, open, and intelligent platform for innovation, allowing you to integrate your data on a common architectural platform. Across industries, organizations are able to reimagine their data possibilities in entirely new ways and quickly build applications that delight their customers.

Siloed data: The barrier to speed and innovation

Digital technologies ranging from transaction processing to analytics and AI/ML use data to help enterprises understand their customers better. And the pace of innovation has naturally accelerated as organizations learn, adapt, and race to build the next generation of applications and services to compete for customers and meet their needs. At Next 2022, we made a prediction that the barriers between transactional and analytical workloads will mostly disappear.

Traditionally, data architectures have separated transactional and analytical systems—and that’s for good reason. Transactional databases are optimized for fast reads and writes, and analytical databases are optimized for analyzing and aggregating large data sets. This has siloed enterprise data systems, leaving many IT teams struggling to piece together solutions. The result has been time consuming, expensive, and complicated fixes to support intelligent, data-driven applications. 

But, with the introduction of new technologies, a more time-efficient and cost-effective approach is possible. Customers now expect to see personalized recommendations and tailored experiences from their applications. With hybrid systems that support both transactional and analytical processing on the same data, without impacting performance, these systems now work together to generate timely, actionable insights that can be used to create better experiences and accelerate business outcomes.   

According to a 2022 research paper from IDC, unifying data across silos via a data cloud is the foundational capability enterprises need to gain new insights on rapidly changing conditions and to enable operational intelligence. The modern data cloud provides unified, connected, scalable, secure, extensible, and open data, analytics, and AI/ML services. In this platform, everything is connected to everything else.

Why reducing data barriers delivers more value 

The primary benefit of a unified data cloud is that it provides an intuitive and timely way to represent data and allows easy access to related data points. By unifying their data, enterprises are able to: 

1. Ingest data faster – for operational intelligence
2. Unify data across silos – for new insights
3. Share data with partners – for collaborative problem solving
4. Change forecasting models – to understand and prepare for shifting markets
5. Iterate with decision-making scenarios – to ensure agile responses
6. Train models on historical data – to build smarter applications

As generative AI applications akin to Bard, an early experiment by Google, become available in the workplace, it will be more important than ever for organizations to have a unified data landscape to holistically train and validate their proprietary large language models. 

With these benefits enterprises can accelerate their digital transformation in order to thrive in our increasingly complex digital environment. A survey of more than 800 IT leaders indicated that using a data cloud enabled them to significantly improve employee productivity, operational efficiency, innovation, and customer experience, among others. 

Build modern apps with a unified and integrated data cloud

Google Cloud technologies and capabilities reduce the friction between transactional and analytical workloads and make it easier for developers to build applications, and to glean real-time insights. Here are a few examples. 

1. AlloyDB for PostgreSQL, a fully managed PostgreSQL-compatible database, and AlloyDB Omni, the recently launched downloadable edition of AlloyDB, can analyze transactional data in real time. AlloyDB is more than four times faster for transactional workloads and up to a 100 times faster for analytical queries compared to standard PostgreSQL, according to our performance tests. This kind of performance makes AlloyDB the ideal database for hybrid transactional and analytical processing (HTAP) workloads.
2. Datastream for BigQuery, a serverless change data capture and replication service, provides simple and easy real time data replication from transactional databases like AlloyDB, PostgreSQL, MySQL and Oracle directly into BigQuery, Google Cloud’s enterprise data warehouse.
3. And, query federation with Cloud Spanner, Cloud SQL, and Cloud Bigtable, make data available right from the BigQuery console allowing customers to analyze data in real-time in transactional databases. 

Speed up deployments and lower costs

By reducing data barriers, we’re taking a fundamentally different approach that allows organizations to be more innovative, efficient, and customer-focused by providing: 

• Built-in industry leading AI and ML that helps organizations not only build improved insights, but also automate core business processes and enable deep ML-driven product innovation. 
• Best-in-class flexibility. Integration with open source standards and APIs ensures portability and extensibility to prevent lock-in. Plus, choice of deployment options means easy interoperability with existing solutions and investments.  
• The most unified data platform with the ability to manage every stage of the data lifecycle, from running operational databases to managing analytics applications across data warehouses and lakes to rich data-driven experiences. 
• Fully managed database services that free up DBA/DevOps time to focus on high-value work that is more profitable to the business. Organizations that switch from self-managed databases eliminate manual work, realize significant cost savings, reduce risk from security breaches and downtime and increase productivity and innovation. In an IDC survey, for example, Cloud SQL customers achieve an average three-year ROI of 246% because of the value and efficiencies this fully managed service delivers. 

Google Data Cloud improves the efficiency and productivity of your teams, resulting in increased innovation across your organization. This is the unified, open approach to data-driven transformation bringing unmatched speed, scale, security, and with AI built in. 

In case you missed it: Check out our Data Cloud and AI Summit that happened on March 29th, to learn more about the latest innovations across databases, data analytics, BI and AI. In addition, learn more about the value of managed database services like Cloud SQL in this IDC study.

^{1. How Big is Big? – Statista}

As Google Cloud has become a choice for more startups, I’ve experienced an increase in founders asking how they should think about cloud services. Though each startup is different and requirements may vary across industries and regions, I’ve seen a few core best practices that help startups to succeed—as well as several traps to avoid. 

For example, If you go with a public cloud provider, you’re ideally not starting from ground zero like you would running your own data center, but it’s important not to introduce similar complexity in a virtualized environment. Just because you’re using public cloud infrastructure doesn’t mean you want to manage it.

Instead, you want to leverage platforms that abstract away complexity so your team can focus on delivering value to customers. That’s where serverless comes in. Serverless platforms are fully managed by the provider, offering automatic scaling for workloads, as well as provisioning, configuring, patching, and management of servers and clusters. Freed from these resource-intensive tasks, your technical talent can focus on the things that differentiate your business, not on IT curation. 

This applies to not only running stateless applications, but also managing and analyzing data. You should be collecting data points about which features are most popular on your platform, what people are buying, the types of activities that help your customers get their jobs done, and so on. This information is essential to building and executing on a product roadmap that will serve your customers. However, it’s not enough to collect this data, you need to make your data accessible, secure, and easy for your team to analyze—so where do you run and host it? 

On Google Cloud, this is where options like Spanner and Cloud SQL can play a large role, as can BigQuery for analysis. You won’t have to worry about standing up infrastructure or patching servers—you can just stream your data to our data management platforms, where it’s available whenever someone needs to run a query. By leveraging a serverless architecture, your startup can be data-driven without having to invest in the traditional complexity of database administration and management—and that can significantly change your playing field. 

Serverless is just one of the factors you should consider as you build out your tech stack. To hear my thoughts on a range of other topics relevant to startups — such as security, cloud credits, and the differences among managed services — check out the below video or visit our Build and Grow page.

https://www.youtube.com/embed/gn4RjLbs8Hc?enablejsapi=1&

Customers love Cloud Spanner because it gives them the benefits of relational semantics and SQL while also delivering the scale and availability of non-relational databases. Many of these customers want to move even more of their work to Spanner, and have requested smaller instance sizes to support development, testing and small production workloads. We’re happy to announce that they’ll soon get what they asked for: more granular instance sizing is coming to Spanner.

Granular instance sizing will be available in Public Preview soon. With this feature, you can run workloads on Spanner at as low as 1/10th the cost of regular instances, equating to approximately $65/month.

In addition, we are launching new enterprise capabilities to break down operational silos for real-time insights and provide greater database observability to developers: 

• Datastream, now in public preview, is a change data capture (CDC) service that allows enterprises to synchronize data across heterogeneous databases and applications. With Spanner support in Datastream, users will be able to stream data from MySQL or Oracle to Spanner reliably and with minimal latency. 
• BigQuery federation to Spanner (coming soon) lets users query transactional data residing in Spanner, from BigQuery, without moving or copying data. 
• Key Visualizer, available now in public preview, provides interactive monitoring so developers can quickly identify trends and usage patterns in Spanner. 

Democratizing access with more granular instance sizing 

Today, customers provision Spanner instances by specifying the number of nodes they need to run their workloads. Each node can provide up to 10,000 queries per second (QPS) of reads or 2,000 QPS of writes (writing single rows at 1 KB of data per row) and 2 TB of storage. A Spanner node is replicated across 3 zones for regional instances, and 5 or more zones for multi-regional instances. The choice of node count determines the amount of serving and storage resources that are available to the databases in a given instance. 

Historically, the most granular unit for provisioning resources on Spanner has been one node. To enable more granular control, we are introducing Processing Units (PUs); one Spanner node is equal to 1,000 PUs. Customers can now provision in batches of 100 PUs, and get a proportionate amount of compute and storage resources. This will allow teams to run smaller workloads on Spanner at much lower cost. With this feature, customers can start at 100 PUs and scale up as needed in batches of 100 PUs, to up to 1,000 PUs (1 node), all with zero downtime. Subsequently, customers can continue to scale up by adding more nodes, just like what they do today. Customers do have the choice of using either PUs or nodes to provision resources within workloads, when those workloads occupy multiple nodes of capacity. 

To illustrate with an example, let’s say a game developer creates a Spanner instance called “baseball-game” in us-central1, with 100 PU compute capacity at $65/month price.

As you can see below, proportional maximum storage of 205 GB is assigned to the 100 PU instance.

Once the game starts becoming popular and resource demands increase, the user edits the instance to increase the compute capacity to 500 PUs with proportional maximum 1 TB of storage.

The game grows in popularity, and the gaming company prepares to launch a much-awaited capability in the game. Anticipating a sharp increase in usage at launch day, the game developers increase the compute capacity to 3,000 PUs. Compute capacity assigned to the instance over a period of time can be viewed in Cloud Monitoring graphs:

Request Access 
You can request early access to granular instance sizing feature by filling this form.

Breaking down operational silos with Datastream CDC and BigQuery federation

BigQuery Federation. BigQuery has made analytics easy by bringing together data from multiple sources for seamless analysis. Soon you’ll also be able to analyze data in Spanner directly in BigQuery. With  Spanner’s BigQuery federation, you’ll be able to instantly query data residing in Spanner in real-time without moving or copying the data. Simply set up the Spanner as an external data source in BigQuery, as shown below.

Change Data Capture ingest. Now in public preview, Datastream lets you stream change data into Google Cloud from MySQL and Oracle databases. As of today, you can ingest this change data directly into Spanner using a built-in Dataflow template. This lets you migrate data from MySQL and Oracle databases into Spanner in near-real time.

Understanding performance and resource usage with Key Visualizer

Key Visualizer is a new interactive monitoring tool that lets developers and administrators analyze usage patterns in Spanner. It reveals trends and outliers in key performance and resource metrics for databases of any size, helping to optimize queries and reduce infrastructure costs. Designed for performance tuning and instance sizing, Key Visualizer is available today in public preview in the web-based Cloud Console for all Spanner databases at no additional cost. Learn more in the Key Visualizer blog. 

Learn more

• To request early access to granular instance sizing in Spanner, fill out this form.
• To get started with Spanner today, create an instance or try it out with a Spanner Qwiklab.