Using lakeFS with Delta Lake

Delta Lake Delta Lake is an open-source storage framework designed to improve performance and provide transactional guarantees to data lake tables.

Because lakeFS is format-agnostic, you can save data in Delta format within a lakeFS repository and benefit from the advantages of both technologies. Specifically:

ACID operations can span across many Delta tables.
CI/CD hooks can validate Delta table contents, schema, or even referential integrity.
lakeFS supports zero-copy branching for quick experimentation with full isolation.

Viewing Delta Lake table changes in lakeFS ^BETA
1. Installing the Delta Lake diff plugin
Spark Configuration
Best Practices
1. When running lakeFS inside your VPC (on AWS)
2. Using multi cluster writes (on AWS)
Further Reading

Viewing Delta Lake table changes in lakeFS ^BETA

Using lakeFS you can

Compare different versions of Delta Lake tables
Get a detailed view of all Delta Lake table operations performed since the tables diverged.

For example, comparing branches dev and main, we can see that the movies table has changed on dev since the branches diverged. Expanding the delete operation, we learn that all movies with a rating < 4 were deleted from the table on the dev branch.

Note: The diff is available as long as the table history in Delta is retained (30 days by default). A delta lake table history is derived from the delta log JSON files.

Installing the Delta Lake diff plugin

To enable the Delta Lake diff feature, you need to install a plugin on the lakeFS server. You will find the plugin binary in the release tarball (versions >= 0.97.3). Put the delta_diff binary under ~/.lakefs/plugins/diff on the machine where lakeFS is running.

You can customize the location of the Delta Lake diff plugin by changing the diff.delta.plugin and plugin.properties.<plugin name>.path configurations in the .lakefs.yaml file.

Notice: If you’re using the lakeFS docker image, the plugin is installed by default.

Spark Configuration

Given the native integration between Delta Lake and Spark, it’s most common that you’ll interact with Delta tables in a Spark environment.

To configure a Spark environment to read from and write to a Delta table within a lakeFS repository, you need to set the proper credentials and endpoint in the S3 Hadoop configuration, like you’d do with any Spark environment.

Once set, you can interact with Delta tables using regular Spark path URIs. Make sure that you include the lakeFS repository and branch name:

df.write.format("delta").save("s3a://<repo-name>/<branch-name>/path/to/delta-table")

Note: If using the Databricks Analytics Platform, see the integration guide for configuring a Databricks cluster to use lakeFS.

Best Practices

Production workflows should ideally write to a single lakeFS branch that could then be safely merged into main. This is because the Delta log is an auto-generated sequence of text files used to keep track of transactions on a Delta table sequentially. Writing to one Delta table from multiple lakeFS branches is possible, but note that it would result in conflicts if later attempting to merge one branch into the other.

When running lakeFS inside your VPC (on AWS)

When lakeFS runs inside your private network, your Databricks cluster needs to be able to access it. This can be done by setting up a VPC peering between the two VPCs (the one where lakeFS runs and the one where Databricks runs). For this to work on Delta Lake tables, you would also have to disable multi-cluster writes with:

spark.databricks.delta.multiClusterWrites.enabled false

Using multi cluster writes (on AWS)

When using multi-cluster writes, Databricks overrides Delta’s S3-commit action. The new action tries to contact lakeFS from servers on Databricks’ own AWS account, which of course won’t be able to access your private network. So, if you must use multi-cluster writes, you’ll have to allow access from Databricks’ AWS account to lakeFS. If you are trying to achieve that, please reach out on Slack and the community will try to assist.