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Databricks

DataHub supports integration with Databricks ecosystem using a multitude of connectors, depending on your exact setup.

Databricks Hive​

The simplest way to integrate is usually via the Hive connector. The Hive starter recipe has a section describing how to connect to your Databricks workspace.

Databricks Unity Catalog (new)​

The recently introduced Unity Catalog provides a new way to govern your assets within the Databricks lakehouse. If you have enabled Unity Catalog, you can use the unity-catalog source (see below) to integrate your metadata into DataHub as an alternate to the Hive pathway.

Databricks Spark​

To complete the picture, we recommend adding push-based ingestion from your Spark jobs to see real-time activity and lineage between your Databricks tables and your Spark jobs. Use the Spark agent to push metadata to DataHub using the instructions here.

Watch the DataHub Talk at the Data and AI Summit 2022​

For a deeper look at how to think about DataHub within and across your Databricks ecosystem, watch the recording of our talk at the Data and AI Summit 2022.

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Incubating

Important Capabilities​

CapabilityStatusNotes
Asset Containers✅Enabled by default
Column-level Lineage✅Enabled by default
Dataset Usage✅Enabled by default
Descriptions✅Enabled by default
Detect Deleted Entities✅Optionally enabled via stateful_ingestion.remove_stale_metadata
Domains✅Supported via the domain config field
Extract Ownership✅Supported via the include_ownership config
Platform Instance✅Enabled by default
Schema Metadata✅Enabled by default
Table-Level Lineage✅Enabled by default

This plugin extracts the following metadata from Databricks Unity Catalog:

  • metastores
  • schemas
  • tables and column lineage

Prerequisities​

  • Get your Databricks instance's workspace url
  • Create a Databricks Service Principal
    • You can skip this step and use your own account to get things running quickly, but we strongly recommend creating a dedicated service principal for production use.
  • Generate a Databricks Personal Access token following the following guides:
  • Provision your service account:
    • To ingest your workspace's metadata and lineage, your service principal must have all of the following:
      • One of: metastore admin role, ownership of, or USE CATALOG privilege on any catalogs you want to ingest
      • One of: metastore admin role, ownership of, or USE SCHEMA privilege on any schemas you want to ingest
      • Ownership of or SELECT privilege on any tables and views you want to ingest
      • Ownership documentation
      • Privileges documentation
    • To include_usage_statistics (enabled by default), your service principal must have CAN_MANAGE permissions on any SQL Warehouses you want to ingest: guide.
    • To ingest profiling information with call_analyze (enabled by default), your service principal must have ownership or MODIFY privilege on any tables you want to profile.
      • Alternatively, you can run ANALYZE TABLE yourself on any tables you want to profile, then set call_analyze to false. You will still need SELECT privilege on those tables to fetch the results.
  • Check the starter recipe below and replace workspace_url and token with your information from the previous steps.

CLI based Ingestion​

Install the Plugin​

pip install 'acryl-datahub[unity-catalog]'

Starter Recipe​

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
  type: unity-catalog
  config:
    workspace_url: https://my-workspace.cloud.databricks.com
    token: "mygenerated_databricks_token"
    #metastore_id_pattern:
    #  deny:
    #    - 11111-2222-33333-44-555555
    #catalog_pattern:
    #  allow:
    #    - my-catalog
    #schema_pattern:
    #  deny:
    #    - information_schema
    #table_pattern:
    #  allow:
    #    - test.lineagedemo.dinner
    # First you have to create domains on Datahub by following this guide -> https://datahubproject.io/docs/domains/#domains-setup-prerequisites-and-permissions
    #domain:
    #  urn:li:domain:1111-222-333-444-555:
    #    allow:
    #      - main.*

    stateful_ingestion:
      enabled: true

pipeline_name: acme-corp-unity


# sink configs if needed

Config Details​

Note that a . is used to denote nested fields in the YAML recipe.

FieldDescription
token ✅
string
Databricks personal access token
workspace_url ✅
string
Databricks workspace url. e.g. https://my-workspace.cloud.databricks.com
bucket_duration
Enum
Size of the time window to aggregate usage stats.
Default: DAY
end_time
string(date-time)
Latest date of usage to consider. Default: Current time in UTC
format_sql_queries
boolean
Whether to format sql queries
Default: False
include_column_lineage
boolean
Option to enable/disable lineage generation. Currently we have to call a rest call per column to get column level lineage due to the Databrick api which can slow down ingestion.
Default: True
include_operational_stats
boolean
Whether to display operational stats.
Default: True
include_ownership
boolean
Option to enable/disable ownership generation for metastores, catalogs, schemas, and tables.
Default: False
include_read_operational_stats
boolean
Whether to report read operational stats. Experimental.
Default: False
include_table_lineage
boolean
Option to enable/disable lineage generation.
Default: True
include_top_n_queries
boolean
Whether to ingest the top_n_queries.
Default: True
include_usage_statistics
boolean
Generate usage statistics.
Default: True
platform_instance
string
The instance of the platform that all assets produced by this recipe belong to
start_time
string(date-time)
Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)
store_last_profiling_timestamps
boolean
Enable storing last profile timestamp in store.
Default: False
top_n_queries
integer
Number of top queries to save to each table.
Default: 10
workspace_name
string
Name of the workspace. Default to deployment name present in workspace_url
env
string
The environment that all assets produced by this connector belong to
Default: PROD
catalog_pattern
AllowDenyPattern
Regex patterns for catalogs to filter in ingestion. Specify regex to match the full metastore.catalog name.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
catalog_pattern.allow
array(string)
catalog_pattern.deny
array(string)
catalog_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
domain
map(str,AllowDenyPattern)
A class to store allow deny regexes
domain.key.allow
array(string)
domain.key.deny
array(string)
domain.key.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
schema_pattern
AllowDenyPattern
Regex patterns for schemas to filter in ingestion. Specify regex to the full metastore.catalog.schema name. e.g. to match all tables in schema analytics, use the regex ^mymetastore\.mycatalog\.analytics$.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
schema_pattern.allow
array(string)
schema_pattern.deny
array(string)
schema_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
table_pattern
AllowDenyPattern
Regex patterns for tables to filter in ingestion. Specify regex to match the entire table name in catalog.schema.table format. e.g. to match all tables starting with customer in Customer catalog and public schema, use the regex Customer\.public\.customer.*.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow
array(string)
table_pattern.deny
array(string)
table_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
user_email_pattern
AllowDenyPattern
regex patterns for user emails to filter in usage.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
user_email_pattern.allow
array(string)
user_email_pattern.deny
array(string)
user_email_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling
UnityCatalogProfilerConfig
Data profiling configuration
Default: {'enabled': False, 'warehouse_id': None, 'profile_...
profiling.call_analyze
boolean
Whether to call ANALYZE TABLE as part of profile ingestion.If false, will ingest the results of the most recent ANALYZE TABLE call, if any.
Default: True
profiling.enabled
boolean
Whether profiling should be done.
Default: False
profiling.max_wait_secs
integer
Maximum time to wait for an ANALYZE TABLE query to complete.
Default: 3600
profiling.max_workers
integer
Number of worker threads to use for profiling. Set to 1 to disable.
Default: 20
profiling.profile_table_level_only
boolean
Whether to perform profiling at table-level only or include column-level profiling as well.
Default: False
profiling.warehouse_id
string
SQL Warehouse id, for running profiling queries.
profiling.pattern
AllowDenyPattern
Regex patterns to filter tables for profiling during ingestion. Specify regex to match the catalog.schema.table format. Note that only tables allowed by the table_pattern will be considered.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profiling.pattern.allow
array(string)
profiling.pattern.deny
array(string)
profiling.pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
stateful_ingestion
StatefulStaleMetadataRemovalConfig
Unity Catalog Stateful Ingestion Config.
stateful_ingestion.enabled
boolean
The type of the ingestion state provider registered with datahub.
Default: False
stateful_ingestion.ignore_new_state
boolean
If set to True, ignores the current checkpoint state.
Default: False
stateful_ingestion.ignore_old_state
boolean
If set to True, ignores the previous checkpoint state.
Default: False
stateful_ingestion.remove_stale_metadata
boolean
Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.
Default: True

Troubleshooting​

No data lineage captured or missing lineage​

Check that you meet the Unity Catalog lineage requirements.

Also check the Unity Catalog limitations to make sure that lineage would be expected to exist in this case.

Lineage extraction is too slow​

Currently, there is no way to get table or column lineage in bulk from the Databricks Unity Catalog REST api. Table lineage calls require one API call per table, and column lineage calls require one API call per column. If you find metadata extraction taking too long, you can turn off column level lineage extraction via the include_column_lineage config flag.

Code Coordinates​

  • Class Name: datahub.ingestion.source.unity.source.UnityCatalogSource
  • Browse on GitHub

Questions

If you've got any questions on configuring ingestion for Databricks, feel free to ping us on our Slack.