There are a lot of amazing AI features being announced at Google Cloud Next. In order to take full advantage of these, you need to make sure your data is being managed in a secure, centralized way. In this talk, you’ll learn how to set up your lakehouse to get your data ready for downstream workloads. You’ll view a demo involving an architecture of Google Cloud products that includes managing permissions on your data, configuring metadata management, and performing transformations using open source frameworks.

This session provides a comprehensive guide to building a secure and unified AI lakehouse on BigQuery with the power of open source software (OSS). We’ll explore essential components, including data ingestion, storage, and management; AI and machine learning workflows; pipeline orchestration; data governance; and operational efficiency. Learn about the newest features that support both Apache Spark and Apache Iceberg.

Join Google Cloud's Yasmeen Ahmad, Deutsche Telekom's VP of Data & Architecture Ashutosh Mishra and Snap's Senior Engineering Leader Bo Chen for a fireside chat exploring the future of the data lakehouse. They'll discuss how evolving architectures can empower organizations to handle explosive data growth and leverage the full potential of AI. Gain valuable insights into building a future-proof data foundation that fuels innovation in 2025 and beyond.

Unlock the potential of AI with high-performance, scalable lakehouses using BigQuery and Apache Iceberg. This session details how BigQuery leverages Google's infrastructure to supercharge Iceberg, delivering peak performance and resilience. Discover BigQuery's unified read/write path for rapid queries, superior storage management beyond simple compaction, and robust, high-throughput streaming pipelines. Learn how Spotify utilizes BigQuery's lakehouse architecture for a unified data source, driving analytics and AI innovation.

Redpanda, a leading Kafka API-compatible streaming platform, now supports storing topics in Apache Iceberg, seamlessly fusing low-latency streaming with data lakehouses using BigQuery and BigLake in GCP. Iceberg Topics eliminate complex & inefficient ETL between streams and tables, making real-time data instantly accessible for analysis in BigQuery This push-button integration eliminates the need for costly connectors or custom pipelines, enabling both simple and sophisticated SQL queries across streams and other datasets. By combining Redpanda and Iceberg, GCP customers gain a secure, scalable, and cost-effective solution that transforms their agility while reducing infrastructure and human capital costs.

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There are a lot of amazing AI features being announced at Google Cloud Next. In order to take full advantage of these, you need to make sure your data is being managed in a secure, centralized way. In this talk, you’ll learn how to set up your lakehouse to get your data ready for downstream workloads. You’ll view a demo involving an architecture of Google Cloud products that includes managing permissions on your data, configuring metadata management, and performing transformations using open source frameworks.