Routine tasks such as data wrangling and pipeline maintenance often inhibit data teams from doing higher-value analysis and insights-led decision-making. This session showcases how intelligent data agents in BigQuery can help automate complex data engineering tasks. You’ll learn how to use natural language prompts to streamline data engineering tasks from ingestion and transformation, such as data cleaning, formatting, and loading results into BigQuery tables that accelerate the time to build and validate data pipelines.

BigQuery helps you build an autonomous data and AI platform from your organization. In this session, you’ll learn how BigQuery agentic intelligence is automating critical data workflows, including data preparation, analysis, predictions, model tuning, security, and governance. We’ll explore the BigQuery AI capabilities that all data practitioners can use to address data challenges in the AI era.

Join us to learn how to activate the full potential of your data with AI in BigQuery. Take an in-depth look at how BigQuery's core integration with generative AI models like Gemini, coupled with its petabyte-scale analytics capabilities, enables new possibilities for gaining insights from your data. Learn how to derive insights from your untapped and unstructured data such as images, documents, and audio files, and explore BigQuery vector search and multi-modal embeddings, all powered by Google's industry-leading AI capabilities in BigQuery using simple Cloud SQL queries. You will also learn how Unilever is creating a data strategy that allows data teams to scale efficiently and rapidly experiment with AI models and gen AI use cases.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

Routine tasks such as data wrangling and pipeline maintenance often inhibit data teams from doing higher-value analysis and insights-led decision-making. This session showcases how intelligent data agents in BigQuery can help automate complex data engineering tasks. You’ll learn how to use natural language prompts to streamline data engineering tasks from ingestion and transformation, such as data cleaning, formatting, and loading results into BigQuery tables that accelerate the time to build and validate data pipelines.

Routine tasks such as data wrangling and pipeline maintenance often inhibit data teams from doing higher-value analysis and insights-led decision-making. This session showcases how intelligent data agents in BigQuery can help automate complex data engineering tasks. You’ll learn how to use natural language prompts to streamline data engineering tasks from ingestion and transformation, such as data cleaning, formatting, and loading results into BigQuery tables that accelerate the time to build and validate data pipelines.