Google unveils advanced AI capabilities in BigQuery platform

Google has revealed a suite of new capabilities within its Data & AI Cloud, focusing on providing businesses with a comprehensive data to AI platform. The enhancements are designed to augment the functionalities of BigQuery, integrating AI capabilities directly into data workflows.

The new offerings bring five times more usage to BigQuery compared to alternative cloud platforms. Users such as Radisson Hotel Group and Gordon Food Service have already reported significant business improvements through the platform. Radisson, for example, noted a 50% increase in campaign productivity and over a 20% rise in revenue using BigQuery, while Gordon Food Service achieved better real-time responses and analytics by unifying over 170 data sources.

General Mills, leveraging BigQuery and Vertex AI, has saved more than USD $100 million. Their Chief Digital & Technology Officer, Jaime Montemayor, stated, "We didn't just need a place to store or consume data, we wanted a collaborator that could help us scale the most advanced data management in the industry."

Significant improvements include the introduction of specialized agents designed for various users, from data engineers to business analysts. These agents aim to simplify and expedite data tasks, providing support in areas like data pipeline construction and anomaly detection. Simultaneously, data science teams receive assistance via a new AI-assisted notebook environment that handles tasks such as feature engineering and model selection.

A key feature is the Looker conversational analytics agent, which utilises natural language processing to enable interactions with data. Developed in collaboration with DeepMind, this technology aims to clarify analytical processes for users by transparently explaining its operations and simplifying the interpretation of results.

The release of the BigQuery Knowledge Engine is another noteworthy development. This engine, now in preview, analyses schema relationships and generates metadata, underpinning AI-driven insights across BigQuery. These insights enable more context-driven interactions, enhancing how users engage with their data.

Also introduced is the BigQuery AI Query Engine, which supports the processing of both structured and unstructured data, adding context and understanding to queries. This engine facilitates more advanced analytical capabilities, enabling users to perform nuanced queries and model building across complex datasets.

Trivago has highlighted the platform's unified approach. Andrés Sopeña Pérez, Head of Data and AI at Trivago, emphasised their reliance on the complementarity of SQL and Spark: "We see SQL and Spark as two complementary ways of accessing and transforming data. Spark is especially useful to us in use cases that require complex business logic, which although niche, are extremely business-critical. Having a unified platform for SQL, Spark, and AI, with the development experience in notebooks will considerably simplify these critical use cases."

Google's enhanced capabilities for handling unstructured data within BigQuery form part of its autonomous data foundation initiative. This allows multimodal data types to be unified for analysis alongside structured data, expanding the platform's versatility. The integration of Apache Iceberg tables ensures the openness and adaptability of the ecosystem.

The company has also optimised its platform with self-managing and scalable features while introducing a unified spend commitment across different functionalities, aimed at delivering efficiency and cost-effectiveness.