Both concepts also bring baggage from the past: Both of these are driven with a focus on a single technology – which immediately should ring alarm bells for any architect. While both of these architectures have some merit, a number of questions immediately spring to mind. Is there really such a stark divergence of views about how to architect a modern data platform? So on one-hand, the Data Lakehouse advocates says “There is no longer a need for a relational database, do it all in the data lake”, while Snowflake is saying “Build your data lake in a relational database”. They have taken this a step further now though and are now pushing the concept of “Make Snowflake Your Data Lake” Snowflake has quickly become a major player in the data warehousing market, making use of its cloud native architecture to drive market share. The Data Lakehouse approach proposes using data structures and data management features in a data lake that are similar to those previously found in a data warehouse: The focus here is how traditional Data Lakes have now advanced so that the capabilities previously provided by the Data Warehouse can now be replicated within the Data Lake. I am encountering two overriding themes when talking to data architects today about their data and analytics strategy – which take very different sides, practically at the extreme ends of the discussion about the future design of the data platform.
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