The Total Economic Impact™ of Building a Data Lakehouse on Google Cloud

Data lakehouses combine the best of both worlds, enabling multi-engine interoperability with open table formats like Apache Iceberg, helping organizations optimize their storage costs and reduce extract, transform, load (ETL) complexity without losing the analytics capabilities, transaction support, and governance of traditional data warehouses. They are designed to support structured and unstructured data and work with multiple engines, such as Spark and SQL, to support new AI use cases, advanced automation, expanded access to enterprise data, and data science acceleration. Forrester’s research sees broad growth in data lakehouse initiatives across industries, with enterprises leveraging this data architecture to accelerate new and emerging business cases, such as business intelligence, data science, agentic AI experiences, IoT insights, business 360, and real-time insights, while also reducing costs and improving data governance.

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The Total Economic Impact™ of Building a Data Lakehouse on Google Cloud