Data Lakes and Lakehouses Explained Simply (Without the Jargon)

I interact with people every day who talk about data lakes and lakehouses.
Within a few minutes, it often becomes clear that they do not fully understand what those terms mean.

When someone keeps relying on fancy terminology instead of explaining the underlying idea, it usually means the concept is not clear to them either.

So here is a simple explanation of data lakes and lakehouses, without the jargon.

What Is a Data Lake?

A data lake is typically built on blob or object storage and can contain almost any type of business data, including:

JSON, CSV, Parquet, Avro, and other serialized formats
Application logs and database exports
Images, audio, and video
Raw and processed datasets
Text files and other unstructured data
Backups, archives, and historical records

In simple terms: it is a central storage location for nearly anything related to the business, whether structured or unstructured.

What Is a Lakehouse?

A lakehouse uses object storage and adds technologies that organize, manage, and provide structured access to the data.

These technologies may include:

Apache Iceberg, Delta Lake, or Hudi for table management
Hive or other catalogs for table definitions and metadata
BigQuery, Spark, Snowflake, Trino, or similar engines for querying and processing
External tables that expose files in object storage as structured tables

In simple terms: a lakehouse turns files in object storage into data that can be managed and queried like database tables.

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