![]() Here at Oakland we feel it is still easier to set up and optimise Cloud Native Warehouses like Snowflake and Google Big Query, than Databricks, as there are fewer moving parts. Maybe, but note it may take some time for a data team used to Databases/Data Warehouses and SQL to convert to Data Lakehouse. Architecture of many Lakehouse Data Products in a Data Mesh – the query layer and governance layer will have access to all Data Products, limited by access permissions. Today, Databricks has a fully featured SQL Data Warehouse, enterprise security, data governance with Unity Catalog, many data connectors, as well as the ability to output data to Power BI and Tableau, so it can meet all common data use cases.Īrchitecture of a simple data platform using just both a Data Lake and Data Warehouse.įor those looking at building a Data Mesh, Databricks has federated query in preview, though Delta Lake also has connectors for Trino, Starburst and Dremio so you can join up many Data Lakes across your organisation. Combined with Spark to process and transform a wide variety of data, this gave birth to the Data Lakehouse. ![]() In 2019 Databricks released Delta Lake, a file format with attributes only found previously in Databases and Data Warehouses as mentioned above. It worked mainly in tandem with a Data Lake, with similar advantages and drawbacks. Until a few years ago, Databricks was mainly designed as an easy way to run Spark, a distributed data processing library for large scale Data Engineering and Data Science. Architecture of an example data platform using both a Data Lake and a Data Warehouse What is a Data Lakehouse?Ī D ata L akehouse is a n open data management architecture that combines the flexibility, cost-efficiency, and scale of D ata L akes with the data management and ACID transactions of D ata W arehouses, enabling business intelligence (BI) and machine learning (ML) on all data. nor connect easily to business intelligence applications in the way that a Data Warehouse or Database can do. ![]() However, without Delta Lake it cannot easily or efficiently do row level updates and inserts. Data Lakes can also easily store non tabular data (images, videos and music) that Data Warehouses cannot without some pre-processing. This is often called separating storage from compute, which has become so popular that many Data Warehouses offer this too now.
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