What is the primary storage format used by Delta Lake?

Delta Lake primarily uses the Parquet storage format for its data storage. Parquet is a columnar storage file format that is highly efficient for both storage space and query performance. It is optimized for storing complex data structures, which is beneficial for the analytical workloads typically processed in data lakes.

The choice of Parquet allows Delta Lake to leverage effective data compression and encoding schemes, making it suitable for large datasets commonly encountered in data engineering. Since Delta Lake is built on top of Apache Spark, using Parquet enhances read and write speeds, improving the overall performance of data processing tasks. Additionally, Parquet works seamlessly with Spark's distributed computing capabilities, allowing users to handle large volumes of data effortlessly.

In comparison, while formats like ORC, CSV, and JSON are also used in various contexts, they do not provide the same level of performance and features that Parquet offers, especially within the Delta Lake framework. ORC is primarily optimized for use with Hive, CSV is a simple text format that may lack the efficiency needed for complex queries, and JSON is versatile but can lead to larger file sizes and slower performance for analytics. Therefore, Parquet's efficient data handling and performance advantages establish it as the foundational storage format for Delta Lake.