Google has unveiled a suite of new AI-powered agents and features for its BigQuery data warehouse and Looker business intelligence platform, designed to automate and simplify analytics tasks for data practitioners. The updates, announced at the company's Google Cloud Next conference, include data engineering and data science agents, a conversational analytics tool, and a knowledge engine, among others.

The data engineering agent, embedded inside BigQuery, is designed to help data practitioners build data pipelines, perform data preparation, and automate metadata generation. According to Google, this agent will simplify and accelerate analytics tasks, as data practitioners typically spend a majority of their time creating pipelines and preparing data to get actionable insights. The agent is also capable of detecting anomalies to maintain data quality, a feature currently in preview.

The data science agent, accessible via Google's free, cloud-based Jupyter notebook service Colab, is designed to help data scientists automate feature engineering. This involves revamping raw data into features that a model can use to make predictions. The agent is also capable of providing intelligent model selection and enabling scalable training along with faster iteration cycles, allowing enterprise data science teams to focus on building data science workflows instead of worrying about revamping data and managing infrastructure.

Google has also added a conversational analytics tool to Looker, currently in preview, which allows enterprise users to interact with data using natural language. The tool, powered by Looker's semantic layer, shows the reasoning behind its response to a query, helping the end user understand and monitor its behavior. An API for conversational analytics is also available to developers, enabling them to integrate it into applications and workflows.

All of these new agents and features come at no additional cost, according to Google. This move is likely to strengthen the company's position in the cloud analytics market, where it competes with the likes of Amazon Web Services, Microsoft Azure, and Snowflake.

In addition to the new agents, Google has also made significant updates to BigQuery, including the addition of a knowledge engine and an AI query engine. The knowledge engine uses Gemini to analyze schema relationships, table descriptions, and query histories to generate metadata on the fly, and model data relationships. This engine will act as the foundational layer for enterprises to ground AI models and agents in business context, according to Google.

The AI query engine, on the other hand, enables data scientists to move beyond simply retrieving structured data to seamlessly processing both structured and unstructured data together with added real-world context. This engine co-processes traditional SQL alongside Gemini to inject runtime access to real-world knowledge, linguistic understanding, and reasoning abilities.

Other updates to BigQuery include the addition of multimodal tables, which will allow enterprises to bring complex data types to BigQuery and store them alongside structured data in unified storage for querying. Google has also enhanced BigQuery governance, providing a single, unified view for data stewards and professionals to handle discovery, classification, curation, quality, usage, and sharing, including automated cataloging and metadata generation.

The general availability of Google Cloud for Apache Kafka to facilitate real-time data streaming and analytics, as well as the addition of serverless execution of Apache Spark workloads in preview, are also notable updates to BigQuery. These updates demonstrate Google's continued focus on improving its cloud analytics offerings and providing more value to its customers.

Overall, these new agents and features are likely to have a significant impact on the way data practitioners and scientists work with data, making it easier and faster to extract insights and value from complex data sets. As the cloud analytics market continues to evolve, Google's investments in AI and machine learning are likely to play a key role in shaping its future.