A data lakehouse is a data architecture that combines the cheap, open storage of a data lake (files in S3-type object storage) with the guarantees of a data warehouse: reliable transactions, schemas and fast SQL queries.
The key is an open table format (Apache Iceberg, Delta Lake or Apache Hudi) layered on top of Parquet files. This metadata layer brings transactions, the ability to query a past version of the data (time travel) and schema evolution without rewriting everything.
The benefit: a single copy of the data serves BI analysis, data science and machine learning alike, and several engines (Spark, Trino, Snowflake, DuckDB…) can read the same tables. This limits dependence on a single vendor.
The term was popularized by Databricks. It is found in companies handling large volumes or wanting to keep control of their storage.
Why it matters when hiring
A Data Engineer who has really worked on a lakehouse can talk about file compaction, partitioning, schema management and query costs. That is where real experience stands apart from surface-level use of Databricks. Do not confuse “knows Databricks” with “designed a lakehouse architecture”: they are two different levels.