Google LLC’s cloud division is rolling out new database and data cloud innovations to support customers building and scaling artificial intelligence applications to ensure that they are grounded in accurate and relevant enterprise information.

Announced today at Google Cloud Next in Tokyo were several new capabilities for Spanner, Google Cloud’s distributed SQL relational database management and storage service, that will make it easier for customers to build and deploy AI apps fueled by data from relationship graph networks, vector search and advanced full-text search.

“Over the past year, we have been focused on helping developers build enterprise gen AI applications by providing industry-leading vector support and strong integration with Vertex AI and open-source LangChain,” said Andi Gutmans, general manager and vice president of engineering and databases at Google Cloud. “But we’ve also heard from customers that in order to build intelligent AI applications, they want to reason about knowledge — not just the data itself but how the data is interconnected.”

The newly announced capability, Spanner Graph, expands Spanner’s ability to include graph processing to include industry-standard graph processing language, which allows for searching relationships between structured and unstructured data in a single query. Gutmans said that will allow developers to build AI applications based on graph-based retrieval-augmented generation, and implement smarter recommendation engines and financial services can serve fraud detection. GraphRAG can be used to improve the accuracy of AI applications by providing more contextually relevant answers to user queries using trusted enterprise real-time data sources.

Spanner is also being upgraded with full-text search and vector search capabilities at scale. Developers can access both vector and full-text in a single query, receiving the power of both keyword search and context-aware semantic search from vector at the same time.

“With Spanner Graph, full-text search and vector search, we have evolved Spanner from not only being the most available, globally consistent and scalable database, to a multi-model database with intelligent capabilities that seamlessly interoperate to enable you to deliver a new class of AI-enabled applications,” Gutmans said.

Bigtable, Google’s high-performance NoSQL database service that can store large amounts of data in wide tables with thousands of columns and billions of rows, is receiving SQL query support. Now developers can use more than 100 SQL functions directly into Bigtable. Google also recently introduced Bigtable distributed counters that will enable developers to rapidly prototype and deploy real-time applications with real-time embedded analytics. Distributed counters are data types optimized for high throughput writes for processing high-speed events that can support AI and fast transactions at scale.

Google’s Data Cloud brings more AI and data capabilities to customers

To assist organizations in handling data, which is the lifeblood of AI applications, Google Cloud is rolling out data analytics products and AI data platform capabilities into general availability to support its customers. It begins with Gemini in BigQuery, which provides the assistance of Google’s most powerful large language model for data engineering, data exploration and analysis, governance and security tasks. This adds new features such as code generation, completion and expiation of SQL and Python.

“Google Cloud continues to strengthen its AI-ready data ecosystem,” said Doug Henschen, vice president and principal analyst at Constellation Research Inc. “Gemini integration is an example of the gen AI augmentation we’re seeing that will drive innovation and enhance use cases for data teams and information workers. Platform unification, like the innovations we’re seeing with BigQuery, will make things simpler and easier for customers looking at data platform migrations.”

With access to Gemini in BigQuery, data engineers will be able to have the AI assist them with data preparation, cleansing, analysis and the entire data journey. It can also provide intelligent recommendations to enhance productivity and optimize costs.

Gemini in Looker, now in preview for Google’s business intelligence tool that will provide AI-powered assistance for building formulas, help explore data and create metrics from complex information and generate slides and presentations on the fly with conversational prompts. That means business users will be able to create calculation fields without having to remember complicated formulas, making their lives easier.

Developers also now get access to powerful tools with support for open-source Apache Spark and Apache Kafka data streaming and processing, which provide real-time analytics and event streaming respectively, allowing them to build versatile, high-speed apps.

Image: Google