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Google Cloud named a leader in the 2024 Forrester Wave™: Data Lakehouses

To unlock the value of AI, organizations recognize that they need to combine their data and AI. That also means they need a simple way to manage the entire data-to-AI lifecycle. Tens of thousands of organizations already choose BigQuery and its integrated AI capabilities to power their data clouds. Today, we’re excited to announce that Google Cloud has been named a leader in The Forrester Wave™: Data Lakehouses Q2 2024 report. Forrester rated Google Cloud a score of 5 out of 5 across 15 different criteria — a testament to our vision and track record of delivering continuous product innovation. 

“Google excels in enterprise lakehouse with strong AI capabilities …. Google’s vision is clear and differentiated, aiming for a seamlessly integrated, intelligent, and meticulously automated lakehouse poised to accelerate diverse use cases and deliver GenAI at scale …. Google is an ideal choice for firms seeking to establish an enterprise data lakehouse with robust scalability, catering to a myriad of use cases such as data science, AI/ML, data engineering, BI, and operational insights, and with an eye on evolving the architecture to a data fabric.” – The Forrester Wave™: Data Lakehouses, Q2 2024.

Customers like Priceline, Deutsche Telekom, HCA Healthcare and many more choose BigQuery to build their data lakehouses to unify all their data and innovate with AI. 

“At HCA Healthcare we are committed to the care and improvement of human life. We are on a mission to redesign the way care is delivered, letting clinicians focus on patient care and using data and AI where it can best support doctors and nurses. We are building our unified data and AI foundation using Google Cloud’s lakehouse stack, where BigQuery and BigLake enable us to securely discover and manage all data types and formats in a single platform to build the best possible experiences for our patients, doctors, and nurses. With our data in Google Cloud’s lakehouse stack, we’ve built a multimodal data foundation that will enable our data scientists, engineers, and analysts to rapidly innovate with AI.” – Mangesh Patil, Chief Analytics Officer, HCA Healthcare

From cloud data warehouse to a unified, AI-ready data platform

BigQuery started as a cloud data warehouse designed for Google-scale; we’ve since evolved BigQuery to be a single, AI-ready data analytics platform. BigQuery makes it easy to build a scalable data and AI foundation with support across all data types and multiple open formats, multiple engines, and multiple clouds, all with the best price-performance and a single, unified experience that is deeply integrated with Vertex AI, Google Cloud’s fully managed AI development platform. 

Customers use BigQuery to manage all data types, structured and unstructured, with integrated governance and fine-grained access controls. BigLake, BigQuery’s unified storage engine, supports open table formats, letting you use existing open-source and legacy tools to access structured and unstructured data, while also benefiting from an integrated data platform. BigLake supports all major open table formats, including Apache Iceberg, Apache Hudi, and Delta Lake natively integrated with BigQuery, along with a fully managed experience for Iceberg, including DDL, DML and streaming support. BigLake’s single interface to query structured and unstructured data across analytics and AI workloads is powered by a single runtime metadata service. This allows BigLake to offer universal table definitions and enforce fine-grained access-control policies for analytics and AI runtimes across Google Cloud, open-source engines (through connectors) and third-party partner engines. 

Direct integration between BigQuery and Vertex AI now enables seamless preparation and analysis of multimodal data such as documents, audio and video files. BigQuery can also analyze unstructured data using object tables and Vertex AI Vision, Document AI and Speech-to-Text APIs.

Recent innovations from Google Cloud Next ’24: 

We took advantage of our annual end user conference this year to announce several important additions to our data analytics and data lakehouse offerings. 

Enterprise scale and performance 
BigQuery’s serverless architecture offers granular scalability, giving customers the ability to use different compute engines like SQL or Spark, on a variety of structured, unstructured, and streaming workloads, all at a 54% lower TCO than cloud-based alternatives. BigQuery eliminates the need for upfront sizing and allows you to analyze your data, at any scale, with a fully managed, serverless workload management model. With a newly integrated serverless Spark engine directly in BigQuery, data teams can collaborate on a single copy of governed data with the flexibility to run SQL, Python, and PySpark, all from the same interface. 

Unified platform from data to AI 
BigQuery lets you access, manage, and activate structured and unstructured data across a variety of systems, providing a unified data foundation that lets you tune your models on governed business data using Vertex AI. BigQuery also lets you ground large language models (LLMs) on your enterprise data. We recently announced the availability of Google’s world-class AI capabilities, including access to Gemini models through BigQuery. Bringing AI directly to your data and customers allows you to:

Improve the efficiency of your LLMs by grounding (training) your proprietary data and business context

Foster innovation by unlocking multimodal gen AI use cases 

Enable new use cases that require similarity search, recommendations or retrieval of your BigQuery data, including documents, images or videos 

Generate vector embeddings and index them at scale using vector and semantic search 

Gemini-powered always-on intelligence
Gemini also powers the data lakehouse solution, where AI-powered assistance helps accelerate the end-to-end data lifecycle — from building data pipelines and data discovery and analysis, to query design and ops automation. What makes Gemini in BigQuery unique is its contextual awareness of your business through access to metadata, usage data and semantics. Gemini in BigQuery also goes beyond chat assistance to include new visual experiences such as BigQuery data canvas, a new natural language-based experience for data exploration, curation, wrangling, analysis and visualization workflows.

World-class security and governance 
To make it easier for you to manage, discover, and govern data, BigQuery offers capabilities like data quality, lineage, and profiling. At Next ‘24, we announced that we will be expanding data-to-AI governance in BigQuery with enhanced search capabilities powered by a unified metadata catalog; this will help data users discover data and AI assets, including models and datasets in Vertex AI. Column-level lineage tracking in BigQuery and lineage for Vertex AI pipelines, along with governance rules for fine-grained access control, allow businesses to define governance policies based on metadata. Now fine-grained control can be enforced on all data accesses across SQL, Spark, Vertex AI, all built into BigQuery.

Unlock the value of AI for your business with our Data Cloud

We’re honored to be a leader in The Forrester Wave: Data Lakehouses and look forward to continuing to innovate and partner with you to help transform your business. Thank you to our customers and partners, including those of you who joined us at Next ’24, for choosing BigQuery to power your continued data cloud strategy and innovation. To learn more about BigQuery, visit our website and follow the interactive tutorial. Click on the link to download the full report: The Forrester Wave: Data Lakehouses, Q2 2024.

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