Google Cloud Revolutionizes Database Technology with AI Integration and Natural Language Querying

What's next for Google Cloud databases? AI inside SQL and more

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More For decades, the SQL query language has been the cornerstone of database technology. But what happens when you bring SQL together with modern generative AI? That's the question that Google Cloud is answering as part of a series of database updates rolling out at the company's Google Cloud Next conference today. Over the past year, all Google Cloud databases have added some form of vector support enabling AI use cases. At Google Cloud Next, multiple databases are being updated including Firestore, which is getting MongoDB compatibility. Google Bigtable is getting support for materialized views and support for Oracle Database in Google Cloud is also expanding. The biggest and most transformational news, at least from a database AI perspective however is in the AlloyDB database. Google first launched AlloyDB in 2022 as an enhanced version of the open source PostgreSQL database. At the Google Next event in the summer of 2024, vector embeddings landed in AlloyDB as well as support for Duet AI to enable database migration. Today AlloyDB is being expanded with integration with Google's Agentspace, which is also making its debut at the Google Cloud Next event. Perhaps more interestingly though is the new AI query engine that allows natural language expressions directly within SQL queries for the first time. AlloyDB's API query engine brings natural language directly Into SQL Google's new AI query engine for AlloyDB, allows developers to use natural language expressions within standard SQL queries -- not just replacing SQL, but enhancing it with AI capabilities. "We're bringing in an AI query engine to AlloyDB," Andi Gutmans, GM and VP of Engineering, Databases at Google Cloud told VentureBeat in an exclusive interview. "Within a SQL query we will have operators that both can use natural language and foundation models and your traditional SQL operators And you can bring these together." This innovation marks a significant evolution in database interfaces. SQL, an acronym that stands for Structured Query Language, was first introduced in 1973. For the last 50 years it has been the de facto standard for structured database queries. The original promise of SQL was to make it easy to execute database queries with a language that used English words in a natural way. Common SQL queries and actions include terms such as 'insert' and 'join' but it's not quite natural language. "We're delivering on a 50 year old promise where SQL should mimic English now," Gutmans said. The query engine enables developers to combine precise SQL syntax with flexible natural language expressions. Unlike other approaches that simply translate natural language to SQL, Google's implementation integrates natural language directly into the query language itself. Google runs foundation model-powered semantic operators alongside traditional relational operators in the database engine. "When SQL first came out in 1973 it was all about, hey, we want a natural language for query data and so SQL was kind of that natural language," Gutmans said. "But really, the way you should think about it is now, this is more the promise of SQL, because now you can use even more natural language as part of your SQL query, but it's still well structured." Agentspace integration democratizes database access Google Cloud is also connecting AlloyDB with its Agentspace platform, creating a natural language interface that extends database access beyond technical specialists to virtually any employee in an organization. While developers and database administrators benefit from AlloyDB's AI query engine, regular business users will utilize Agentspace. "It's for the average employee in an organization, trying to get their job done," Gutmans said. "One of the ways to get their job done is actually to have a natural language interface, being able to ask questions about all the enterprise data they have access to." What makes this integration particularly powerful is how it maintains security while expanding access. Unlike other natural language database interfaces, Google's implementation leverages its powerful Agentspace platform, which knows how to reason, not just about a single data source, but multiple data sources. It could be a web search, AlloyDB or other enterprise unstructured data. Vector search optimization delivers measurable business outcomes Google has also dramatically improved AlloyDB's vector search capabilities, optimizing both performance and usability. AlloyDB's Scalable Nearest Neighbor (ScaNN) index now delivers up to 10x faster filtered vector search queries compared to hierarchical navigable small world (HNSW) indexes in standard PostgreSQL. "We've seen AlloyDB's vector search adoption increase nearly seven times since the launch of the state-of-the-art Scalable Nearest Neighbor (ScaNN) for AlloyDB index in 2024," Gutmans said. This rapid adoption reflects real business impact, as evidenced by retail giant Target's experience. Gutmans noted that Target has used AlloyDB to improve its online search experience. "They're using vector search, and they're using these capabilities to really improve the accuracy," he said. "And if you think about the 20% improvement in accuracy that translates to revenue...20% better targeting means more conversions, more revenue." Real-time processing capabilities advance with Bigtable's materialized views One of the more technically significant announcements is Bigtable's new continuous materialized views feature, designed for high-throughput, real-time applications. "This is a really cool capability that is very specific to Bigtable," Gutmans explained. "Bigtable is used a lot in clickstream counters, like real time counters for real time applications, there's very low latency, and it scales." Unlike traditional materialized views that require periodic refreshes, Bigtable's implementation updates automatically. This capability eliminates the need for complex data flow pipelines to calculate aggregates, simplifying architectures for real-time analytics. What this means for enterprise AI adoption For enterprises developing AI applications, Google's database enhancements offer several immediate advantages. The AI query engine enables more intuitive data access while maintaining SQL's structure and security. The optimized vector search delivers measurable performance improvements for semantic search applications. Finally, the Agentspace integration extends database access throughout organizations without requiring SQL expertise. For enterprises looking to lead in AI adoption, these innovations mean database infrastructure can now actively participate in AI workflows rather than just storing data. The convergence of SQL's structure with natural language's flexibility creates opportunities for smarter applications that leverage both human and machine intelligence without requiring complete system redesigns. Perhaps most importantly, Google's approach demonstrates that enterprises don't need to abandon existing database investments to embrace AI capabilities. As Gutmans succinctly put it when asked if SQL was becoming obsolete: "SQL is dead. Long live SQL."

Google boosts AI and agentic features across BigQuery and AlloyDB - SiliconANGLE

Google boosts AI and agentic features across BigQuery and AlloyDB Google Cloud today unveiled a series of new data analytics capabilities aimed at streamlining enterprises' use of unstructured used to train artificial intelligence models. The updates include specialized AI agents and an autonomous data foundation that spans the data lifecycle. The company also said it's adding AI features to its PostgreSQL-compatible AlloyDB database management system and boosting support for Oracle DBMS in its cloud. The highlight of today's announcements is specialized agents integrated into Google Cloud's BigQuery and Looker business intelligence platform that address data engineering, data science and user experience. Data engineering agent capabilities assist in building data pipelines, performing data transformations, maintaining data quality through anomaly detection (in preview), and automating metadata generation. They help data engineers maintain consistent data standards without extensive manual work. A new data science agent is embedded within Google's Colab notebook, a cloud-based platform that allows developers to write and execute Python code in a browser. It helps automate processes such as feature engineering, intelligent model selection and training to enable data scientists to focus on workflow design rather than infrastructure. The Looker conversational analytics agent (in preview), which was developed in partnership with Alphabet Inc.'s DeepMind subsidiary, allows users to query and analyze data via natural language. At the same time, it provides explanations of its reasoning and clarifying ambiguities through a semantic layer that maps business terms like "revenue" to internal metrics. A new Conversational Analytics application program interface (in preview) lets developers embed conversational analytics into existing applications and workflows. Google also introduced the BigQuery Knowledge Engine in preview mode. Powered by Google's Gemini family of multimodal large language models, the engine examines schema relationships, table descriptions and query histories. Google said it can generate metadata, recommend business glossary terms and model data relationships on the fly. The engine underpins new AI-driven functions in BigQuery, including data insights and semantic search. It's now generally available. To streamline data science tasks, Google Cloud is enhancing BigQuery's notebook with intelligent SQL cells that understand data context and provide smart suggestions. New exploratory analysis, visualization and collaboration feature allow teams to join and analyze data directly in notebooks and share findings. Automated scheduling can update analyses periodically. The new BigQuery AI Query Engine integrates the Gemini foundation model to process unstructured data, That enables analysts, for example, to analyze images and match items to a product catalog. Expanded integrations with open-source projects include Google Cloud support for Apache Kafka and a serverless version of Apache Spark. BigQuery continuous queries (now generally available) allow SQL analysis of streaming data. New BigQuery multimodal tables (in preview) allow for unified storage and querying of varied data types. Enhanced governance features (in preview) centralize data classification, curation, usage and sharing. Automated cataloging (generally available) and metadata generation (experimental) further reduce manual tasks for data stewards. Google's AgentSpace agentic development platform can now search structured data in AlloyDB. The feature allows structured and unstructured data to be combined in real time. Natural language support, which was added to AlloyDB last year, in being extended to use supplied context and interactive intent clarification when responding to queries. Administrators can define which data can be accessed using AlloyDB's parameterized secure views, which provide an extra layer of security for agents and generative AI applications. Optimized SQL functionality now also spans vector search and structured filters and joins. A new AlloyDB AI query engine enables natural language expressions and constructs to be used within SQL queries, including images and descriptions. Most of the new features are now available in preview model. Support for Oracle Corp.'s base database service allows users to run Oracle databases in the Google cloud. Oracle Exadata X11M is now generally available with enterprise-level capabilities such as customer managed encryption keys. Both services are being deployed natively in 20 Google Cloud locations. A new Database Migration Service now supports SQL Server-to-PostgreSQL migrations for Cloud SQL and AlloyDB in both self-managed and cloud-managed configurations. The service features low-downtime data migration and a rules-based conversion engine, augmented by a Gemini model trained to automate difficult migration steps, like converting Transact-SQL code and SQL Server-specific data types such as DATETIME to their PostgreSQL equivalents. Other database-related announcement at Next include the availability of Cloud SQL and AlloyDB on C4A Arm-base instances. Database Center, an AI-assisted dashboard that provides a single view across an entire database fleet, is now generally available, supporting every Google database. Vector search and graph visualization in Spanner, a managed distributed database service, are now generally available. Bigtable continuous materialized views, which simplify real-time updates for applications that rely on immediate reporting and insights, is now available in preview mode.