Traditional data lakes (just files like Parquet, ORC, CSV) are:
Cheap and scalable, but
Lack database-like features (no transactions, updates, deletes, schema management, etc.).
That’s where these formats come in → they bring data warehouse features to data lakes → often called “Lakehouse architecture”.
🔹 1. Delta Lake
Created by: Databricks (open source now).
Best for: Data engineering pipelines, streaming + batch processing.
Key Features:
ACID transactions (ensures consistency when multiple jobs run at once).
Schema enforcement & evolution.
Time travel (query older versions of data).
Great integration with Spark and Databricks.
👉 Example use: Updating sales records in a data lake with reliability, even if multiple ETL jobs are writing.