Apache Iceberg is probably the closest product to what databricks is open sourcing, but none of these products are everything that's needed for datalake management.

What these products do is make it as easy to use decoupled storage and compute as your analytics system as it would be to use a fully managed analytics DBMS system.

You can see some related but not directly comparable efforts in HuDI see https://eng.uber.com/uber-big-data-platform/

Or https://iceberg.apache.org/

Which both keep track of data versioning and management of file based datasets on object stores.

Most people today have very ad how approaches to handling data versioning and lineage on Hadoop datasets.

Throwing my own project's hat in the ring, Pachyderm[0] is opensource, written in Go and built on Docker and Kubernetes. It versions controls your data, makes modifications atomic and tracks data lineage. You implement your pipelines in containers, so any tool you can put in a container can be used (and horizontally scaled) on Pachyderm.

[0] https://github.com/pachyderm/pachyderm

DIY is probably the biggest alternative. I think Databricks saw so many customers solving the same problem and decided to make a service of it. Great idea and a bit surprising that it hasn't continued as an Enterprise feature that they only offer customers.

Hive ACID solves a similar set of problems with a similar design (base Parquet/ORC files + delta files in the same format). It's a bit more "managed" in that compaction is automatic and it supports features like column-level access control, which aren't possible when you're executing an end-user's Scala code against a directory of files.

https://docs.hortonworks.com/HDPDocuments/HDP3/HDP-3.1.0/usi...

AWS has one in preview: https://aws.amazon.com/lake-formation/

That's what I'd be interested in, too :-)