![]() To help data teams evaluate this value we listed, in no particular order, some of the top data lake vendors to consider including:ĭatabricks has native integration with multiple cloud providers–including AWS, Azure, and GCP–but what sets Databricks apart is its approach to collaborative machine learning. While the lines between data lake and data warehouse have blurred, the value of data lakes or lakehouses is as clear as ever. This trend has enabled data teams to leverage the data lakehouse for more analytical use cases traditionally reserved for the data warehouse. Databricks Data Catalog and AWS Lake Formation are examples in this vein. However, one of the biggest trends in data lake technologies, and a capability to evaluate carefully, is the addition of more structured metadata creating “lakehouse” architecture. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption. By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms, particularly those serving streaming or machine learning use cases. Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. ![]()
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