Analytics Storage and AI, Data Prep and Data Lakes with Google Cloud

This presentation explores how Google Cloud Storage addresses the intersection of analytics and AI workloads, with a focus on data preparation and data lakes. Learn how analytics serves as a critical component in the AI data pipeline, particularly for data ingestion, preparation, and cleaning. Discover how organizations are building unified data lake houses using open metadata table formats like Apache Iceberg to enable both analytics and AI workloads. Follow Group Product Manager Vivek Sarswat as he shares real-world examples, including how Snap processes trillions of user events weekly using Spark for data preparation on Google Cloud Storage. Examine key optimizations such as the Cloud Storage Connector, Anywhere Cache, and Hierarchical Namespace (HNS) that enhance data preparation workflows. Understand the data lakehouse concept that combines structured and unstructured data in a unified platform, with implementation examples from Snowflake, Databricks, Uber, and Google Cloud's BigQuery. Address common challenges including data fragmentation, performance bottlenecks, and optimization strategies for resilience, security, and cost through solutions like Storage Intelligence, Anywhere Cache, and Bucket Relocate, supported by customer case studies from Spotify and Two Sigma.