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Topic

Iceberg

Apache Iceberg

table_format data_lake schema_evolution file_format storage open_table_format

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2020-Q1 2026-Q1

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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.

Modern analytics and AI workloads demand a unified storage layer for structured and unstructured data. Learn how Cloud Storage simplifies building data lakes based on Apache Iceberg. We’ll discuss storage best practices and new capabilities that enable high performance and cost efficiency. We’ll also guide you through real-world examples, including Iceberg data lakes with BigQuery or third-party solutions, data preparation for AI pipelines with Dataproc and Apache Spark, and how customers have built unified analytics and AI solutions on Cloud Storage.

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.

This Session is hosted by a Google Cloud Next Sponsor.
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