The lakehouse pattern enables you to keep a large amount of your data in Data Lake and to get the analytic capabilities without a need to move your data to some data warehouse to start an analysis. A lakehouse represents a good trade-off between query performance and the ability to access the latest version of data without the need to wait for data to be reloaded.
Azure Synapse Analytics workspace enables you to implement the Lakehouse pattern on top of Azure Data Lake storage.
When you think about your lakehouse solution, be aware that there are two options for creating databases over the lake:
– Lake databases that are created using Spark or database template
– SQL databases that are created using serverless SQL pools on top of data lake.
Although you might use different tools and languages to create these types of databases, the principles described in this article apply to both types. I will use the term “lakehouse” whenever i reference Spak Lake database or SQL database created using the serverless SQL pools.
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