Data Lake vs. Data Warehouse vs. Lakehouse
Three ways to store a company's data for analysis. A warehouse (Snowflake, BigQuery, Redshift) holds cleaned, structured tables ready for SQL. A lake (S3, Azure Data Lake) holds raw files of any kind, cheaply. A lakehouse (Databricks, Delta Lake, Iceberg) adds warehouse-style tables on top of a lake. Vendors blur the lines on purpose, so candidates often use the words loosely.
How to tell them apart on a resume
Warehouse
Snowflake, BigQuery, Redshift, dbt, SQL, star schemas, BI dashboards. Usually analytics-focused work.
Lake / Lakehouse
S3, Parquet, Spark, Databricks, Delta Lake, Iceberg. Often mixed with machine learning and very large or unstructured data.
The question that settles it
“Where did the data actually live, and did people query it mostly with SQL in a warehouse or with Spark over files?”
Read the full definitions
Open the full tool for the other look-alike pairs, role profiles, and the JD decoder.