Some high level context for those less familiar with the Lakehouse storage system space. For various reasons, several companies moved from data warehouses to data lakes starting around 7-10 years ago.

Data lakes are better for ML / AI workloads, cheaper, more flexible, and separate compute from storage. With a data warehouse, you need to share compute with other users. With data lakes you can attach an arbitrary number of computational clusters to the data.

Data lakes were limited in many regards. They were easily corrupted (no schema enforcement), required slow file listings when reading data, and didn't support ACID transactions.

I'm on the Delta Lake team and will speak to some of the benefits of Delta Lake compared to data lakes:

* Delta Lake supports ACID transactions, so Delta tables are harder to corrupt. The transaction log makes it easy to time travel, version datasets, and rollback to earlier versions of your data.

* Delta Lake allows for schema enforcement & evolution

* Delta Lake makes it easy to compact small files (big data systems don't like an excessive number of small files)

* Delta Lake lets readers get files and skip files via the transaction log (much faster than a file listing). Z ORDERING the data makes reads even faster.

The Delta Lake protocol is implemented in a Scala library and exposed via PySpark, Scala Spark, and Java Spark bindings. This is the library most people think of when conceptualizing Delta Lake.

There is also a Delta Lake Java Standalone library that's used to build other readers like the Trino & Hive readers.

The Delta Rust project is another implementation of the Delta Lake protocol that is implemented in Rust. This library is accessible via Rust or Python bindings. Polars just added a Delta Lake reader with delta-rs and this library can also be used to easily read Delta Lakes into other DataFrames like pandas or Dask.

Lots of DataFrame users are struggling with data lakes / single data files. They don't have any data skipping capabilities (unless Parquet file footers are read), their datasets are easily corruptible, and they don't have any schema enforcement / schema evolution / data versioning / etc. I expect the data community to accelerate the shift to Lakehouse storage systems as they learn about all of these advantages.