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Extraction

5 skills for extracting schemas, data, and scripts from existing databases — essential for migration and reverse engineering.

Skills​

extract​

Combined schema and data extraction. The all-in-one extraction skill that handles both structure and data.

You: /extract Extract the complete analytics schema from Snowflake including data samples

extract-schema​

JDBC schema extraction with support for custom remarks, column selection, and filtering. Generates Starlake YAML configurations from existing database schemas.

You: /extract-schema Reverse-engineer my PostgreSQL analytics schema into Starlake YAML

Key features:

  • Extracts table structures, column types, and constraints
  • Custom remark handling for documentation
  • Column filtering and selection
  • Generates ready-to-use domain and table YAML files

extract-data​

Extract data to files. Export data from databases to CSV, JSON, or Parquet files.

You: /extract-data Export the customers table from BigQuery to Parquet files

extract-bq-schema​

BigQuery-specific schema extraction. Optimized for BigQuery's nested and repeated field structures.

You: /extract-bq-schema Extract all table schemas from the analytics dataset in BigQuery

extract-script​

Generate extraction scripts. Creates reusable extraction configurations and shell scripts.

You: /extract-script Generate an extraction script for nightly exports from Snowflake

Example: Reverse-Engineering a Database​

# metadata/extract/analytics.sl.yml
extract:
  connectionRef: my-snowflake
  jdbcSchemas:
    - schema: ANALYTICS
      tables:
        - name: CUSTOMERS
          columns:
            - name: "*"
        - name: ORDERS
          columns:
            - name: "*"
      tableTypes:
        - TABLE
        - VIEW

The extraction generates Starlake-compatible YAML that you can immediately use for ingestion or transformation pipelines.