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.
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