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Google named a Leader in the 2023 Forrester Wave: Data Management for Analytics

April 17, 2023
Dave Nettleton

Sr. Director, Product Management, Google Cloud

We're excited to announce that Forrester Research has recognized Google as a Leader in The Forrester Wave™: Data Management for Analytics Q1 2023. We believe this is a testimony to Google's vision and strong track record of delivering continuous product innovation in open data ecosystems, unified data cloud offerings, and built-in intelligence. 

Organizations looking to stay ahead of competitive and economic pressures want to leverage the vast amount of data available today to make informed decisions, improve business process efficiency, and drive innovation. However, with the exponential growth in the amount and types of data, workloads, and users, harnessing the data's full potential is incredibly complex, while still critical for customers’ success. We take this challenge seriously and are honored by the Forrester Wave recognition. 

Download the complimentary report: The Forrester Wave: Data Management for Analytics, Q1 2023. 


Forrester evaluated 14 data management for analytics (DMA) providers in this report against pre-defined criteria, evaluating them on their current offerings and strategy. In addition to being named a Leader, Google received the highest possible score in 11 different evaluation criteria, including roadmap execution, performance, scalability, data security and visualization.

Google offers a fully managed, serverless [data management for analytics] solution that can scale to hundreds of petabytes using standard SQL.

The Forrester Wave™: Data Management for Analytics, Q1 2023

Customers like British Telecom, Vodafone, Carrefour, and tens of thousands of others around the world, have partnered with Google Cloud to drive innovation with a unified, open, and intelligent data ecosystem. 

Unified data management

Google provides a unified data platform that allows organizations to manage every stage of the data lifecycle — from running operational databases for applications to managing analytical workloads across data warehouses and data lakes, to data-driven decision-making, to AI and Machine Learning. How we've architected our platform is unique and enables customers to bring together their data, people, and workloads. Our databases are built on highly scalable distributed storage with fully disaggregated resources and high-performance Google-owned global networking. This combination allows us to provide tightly integrated data cloud services across our data cloud products, including Cloud Spanner, Cloud Bigtable, AlloyDB for PostgreSQL, BigQuery, Dataproc, and Dataflow

In the past year, we launched several capabilities that further strengthen these integrations, making it easier for customers to accelerate innovation:

  • Unified transactional and analytics platform. With change streams, customers can track writes, updates, and deletes to Spanner and Bigtable databases and replicate them to downstream systems such as BigQuery, Pub/Sub, and Cloud Storage. Datastream for BigQuery is now generally available and provides easy replication of data from operational database sources, such as AlloyDB, PostgreSQL, MySQL, and Oracle, directly into BigQuery, allowing you to easily set up an ELT (Extract, Load, Transform) pipeline for low-latency data replication that enables real-time insights.

  • Unified data of all types. BigLake enables customers to work with data of any type, in any location. This allowed us to deliver object tables, a new table type that provides a structured interface for unstructured data. Object tables let customers natively run analytics and ML on images, audio, and documents, changing the game for data teams worldwide, who can now innovate without limits with all their data in one unified environment.

  • Unified workloads. We introduced new developer extensions for workloads that require programming beyond SQL. With BigQuery stored procedures for Apache Spark, customers can run Spark programs directly from within BigQuery, unifying transformation, and ingestion and enabling Spark procedures to run as a step in a set of SQL statements. This unification increases productivity and brings costs and billing benefits, as customers only pay for the Spark job duration and resources consumed. 

To help customers further manage data cloud costs, we announced BigQuery editions with three pricing tiers — Standard, Enterprise and Enterprise Plus — for you to choose from, with the ability to mix and match for the right price-performance based on your individual workload needs. 

BigQuery editions come with two innovations. First, compute capacity autoscaling adds fine-grained compute resources in real-time to match the needs of your workload demands, and ensure you only pay for the compute capacity you use. Second, physical bytes billing pricing allows you to only pay for data storage after it’s been highly compressed. With compressed storage pricing, you can reduce your storage costs while increasing your data footprint at the same time.

Open data ecosystem 

Google Cloud provides industry-leading integration with open source and open APIs, which ensures portability, flexibility, and reduces the risk of vendor lock-in. We see customers like PayPal, HSBC, Vodafone, Walmart, and hundreds of others increasingly leverage our suite of migration services to power their data cloud transformation journeys. For example, BigQuery Migration Service has helped hundreds of customers automatically translate over 9 million statements of code from traditional data warehouses to BigQuery, and the comprehensive Database Migration Program accelerates migrations to the cloud with the right expertise, assessments, and financial support. Customers can also take advantage of our managed services that are fully compatible with the most popular open-source engines, such as PostgreSQL, MySQL, and Redis.

And we don’t stop there. We also offer BigQuery Omni, which enables insights to data in other cloud environments, while providing a single pane of glass for analysis, governance, and security.

We continue to focus on making Google Cloud the most open data cloud that can unlock the full potential of your data and remove the barriers to digital transformation. Some recent launches in this area allow you to:

  • Modernize your PostgreSQL environment. We announced the technology preview of AlloyDB Omni, a downloadable edition of AlloyDB designed to run on-premises, at the edge, across clouds, or even on developer laptops. We also announced a new Database Migration Assessment (DMA) tool, as part of the Database Migration Program. This new tool provides easy-to-understand reports that demonstrate the effort required to move to one of our PostgreSQL databases — whether it’s AlloyDB or Cloud SQL.

  • Build an open-format data lake. To support data openness, we announced the general availability of BigLake, to help you break down data silos by unifying lakes and warehouses. BigLake innovations add support for Apache Iceberg, which is becoming the standard for open-source table format for data lakes. And soon, we’ll add support for formats including Delta Lake and Hudi.

  • Bring analytics to your data. To help you analyze data irrespective of where it resides, we launched BigQuery Omni. Recently, we added new capabilities such as cross-cloud transfer and cross-cloud larger query results, which will make it easier to combine and analyze data across cloud environments.

At the same time, we significantly expanded our data cloud partner ecosystem and are increasing partner investments across many new areas. Today, more than 900 software partners are building their products using Google’s Data Cloud, and more than 50 data platform partners offer validated integrations through our Google Cloud Ready - BigQuery initiative. Partners like Starburst are deepening their integration with BigQuery and Dataplex so that customers can bring analytics to their data no matter where it resides, including data lakes, multi and hybrid cloud sources. 

Built-in intelligence

At Google, AI is in our DNA. For two decades, we’ve leveraged the power of AI to organize the world’s information and make it useful to people and businesses everywhere. From enhancing the performance of our Search algorithm with ML, to sharpening content recommendations on YouTube with unsupervised learning, we have constantly leveraged AI to solve some of the toughest challenges in the market.

BigQuery ML, which empowers data analysts to use machine learning through existing SQL tools and skills, saw over 200% growth in usage in 2022. Since BigQuery ML became generally available in 2019, customers have run hundreds of millions of prediction and training queries.

We continue to bring the latest advancements in AI technology to make our data cloud services even more intelligent. Here are a few recent examples:

  • BigQuery inference engine. We announced BigQuery ML inference engine, which allows you to run predictions not only with popular models formats directly in BigQuery, but also using remotely hosted models and Google’s state-of-the-art pretrained models.

  • Database system optimizations. Capabilities such as Cloud SQL recommenders and AlloyDB autopilot make it easier for database administrators and DevOps teams to manage performance and cost for large fleets of databases. 

  • Databases and AI integration. In addition to infusing AI and ML into our products, we have tightly integrated Spanner, AlloyDB, and BigQuery with Vertex AI to simplify the ML experience. With these integrations, AlloyDB and Spanner users can now enable model inferencing directly within the database transaction using SQL. 

  • Simplified ML Ops. Models created in BigQuery using BigQuery ML are now instantly visible in Vertex AI model registry. You can then directly deploy these models to Vertex AI endpoints for real-time serving, use Vertex AI pipelines to monitor and train models, and view detailed explanations for your predictions through BigQuery ML and Vertex AI integration. 

And of course, we deliver all this on infrastructure built for some of the world’s most demanding workloads. Google Cloud databases and analytics solutions are proven to operate at scale. For example, Spanner consistently processes 2 billion requests per second, and BigQuery customers analyze over 100 terabytes of data per second. 

We look forward to continuing to innovate and partner with you on your digital transformation journey and are honored to be a Leader in the 2023 Forrester Wave™: Data Management for Analytics.

Download the complimentary Forrester Wave™: Data Management for Analytics, Q1 2023 report.

Learn more about how organizations are building their data clouds with Google Cloud solutions. 

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