S3 Access in Spark and Databricks
You can use a library (like Boto 3 in Python) to access
standard Amazon S3 and point it at Immuta to access your data. The integration with Databricks
uses a file system (
retrieves your API key and communicates with Immuta as if it were talking directly to S3, allowing users to access
S3 and Azure Blob data sources
This mechanism would never go to S3 directly. To access S3 directly, you will need to expose an S3-backed table or view in the Databricks Metastore as a source or use native workspaces/scratch paths.
Accessing Object-Backed Data Sources in Spark or Databricks
Configure Your Cluster
To use the
is3a filesystem, add the following snippet to your cluster configuration:
This configuration is needed to allow any access to
is3a on Databricks 7+.
Query Your Data
In Databricks or Spark, write queries that access this data by referencing the S3 path (shown in the Basic Information section of the Upload Files modal above), but using the URL scheme
This integration is only available for object-backed data sources. Consequently, all the standard limitations that apply to object-backed data sources in Immuta apply here.
Additional configuration is necessary to allow
is3apaths to function as scratch paths. Contact your Immuta support professional for guidance.