Extract Delta Lake tables and Unity Catalog metadata (schemas, lineage) from Databricks into Semantica with personal access token or OAuth M2M authentication.
Installation
Basic Usage
Use environment variables (or a .env file with python-dotenv) to keep credentials out of source code. DatabricksIngestor() with no arguments reads from DATABRICKS_* environment variables automatically.
Authentication Methods
Personal Access Token
OAuth M2M (Recommended)
Preferred for production: no long-lived personal token stored in config.
http_path identifies the SQL warehouse or all-purpose cluster used for query execution. Find it in the Databricks UI under SQL Warehouses → Connection details. Unity Catalog metadata calls (list_catalogs, get_table_schema, get_table_lineage, …) only need host and credentials — http_path is not required for those.
Querying
Ingest a table with filters
Custom SQL
Schema introspection
Catalogs, schemas, and tables
Table and column lineage
Use get_table_lineage to build Table --DEPENDS_ON--> Table edges in the knowledge graph directly from Unity Catalog’s lineage tracking, without re-deriving lineage from query logs.
Pass include_column_lineage=True to also resolve per-column upstream/downstream references (one extra Unity Catalog request per column, so it’s opt-in):
Export as Semantica Documents
Batch Processing Large Tables
Or use the built-in batch_size parameter:
Troubleshooting
See Also