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Lakehouse vs Delta Lake vs Warehouse

Lakehouse vs Data Warehouse vs Delta Lake🔗

Data warehouses have powered business intelligence (BI) decisions for about 30 years, having evolved as a set of design guidelines for systems controlling the flow of data. Enterprise data warehouses optimize queries for BI reports, but can take minutes or even hours to generate results.

Designed for data that is unlikely to change with high frequency, data warehouses seek to prevent conflicts between concurrently running queries. Many data warehouses rely on proprietary formats, which often limit support for machine learning. Data warehousing on Databricks leverages the capabilities of a Databricks lakehouse and Databricks SQL. For more information, see Data warehousing on Databricks.

Powered by technological advances in data storage and driven by exponential increases in the types and volume of data, data lakes have come into widespread use over the last decade. Data lakes store and process data cheaply and efficiently. Data lakes are often defined in opposition to data warehouses: A data warehouse delivers clean, structured data for BI analytics, while a data lake permanently and cheaply stores data of any nature in any format. Many organizations use data lakes for data science and machine learning, but not for BI reporting due to its unvalidated nature.

The data lakehouse combines the benefits of data lakes and data warehouses and provides:

  • Open, direct access to data stored in standard data formats.

  • Indexing protocols optimized for machine learning and data science.

  • Low query latency and high reliability for BI and advanced analytics.

  • By combining an optimized metadata layer with validated data stored in standard formats in cloud object storage, the Data Lakehouse allows you to work from the same data and in the same platform across different use cases.