I dunno, I have to first put my data somewhere though. But where.. In a warehouse? Silo? Data lake? Lake house? (I really despise that last one, who could coin that phase with a straight face..)
Data lake: typically refers to Parquet files / CSV files in some storage system (cloud or HDFS). Data lakes are better for non-SQL workflows compared to data warehouses, but have a number of disadvantages.
Lakehouse storage formats: Based on OSS files and solve a number of data lake limitations. Options are Delta Lake, Iceberg, and Hudi. Lakehouse storage formats offer a ton of advantages and basically no downsides compared to Parquet tables for example.
Lakehouse architecture: An architectural paradigm to store data in a way such that's it's easily accessible for SQL-based and non-SQL-based workflows, see the paper: https://www.cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf
There are a variety of tradeoffs to be weighed when selecting the optimal solution for your particular needs.
If not please elaborate why you doubt this is not factual?
"data lake" = collection of all data sources: HDFS, S3, SQL DB, etc
"lake house" = tool to query and combine results from all such sources: Dremio
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