Snowflake can augment existing data lakes by using materialised views to query external data. Find out how to process critical data and generate reports much faster by using Snowflake alongside your existing data lake.

Traditionally, the data warehouse and data lake have been like oil and water—they don’t mix. The data warehouse was a curated and governed source of strictly relational data that enabled businesses to build reports with SQL. The data lake was a less-governed home for much larger volumes of data, usually encompassing semi-structured data as well as structured data. Having two silos for data was inconvenient, but there simply wasn’t a technology that could deliver the benefits of both a data warehouse and a data lake in one place.

Now with Snowflake’s cloud data platform, you can store virtually any amount of data of any kind with the flexibility of schema-on-read. At the same time, you can query all of your data with standard SQL and realise the governance and simplicity of the relational data warehouse. 

Join us to see how Snowflake’s cloud data platform can be used alongside your existing data lake.