Databricks is highlighting how a leading Canadian transportation and logistics company used Genie Code, Unity Catalog, custom Agent Skills, and Databricks Apps to modernize legacy data pipelines at scale. The approach automated more than 90% of new table ingestion and reduced pipeline delivery from days to minutes.
What’s new
- Generate pipelines from a short prompt: A compact YAML request can produce production-ready ingestion artifacts, including table definitions, historical loads, streaming ingestion, incremental merges, and tests.
- Grounded in live metadata: Genie Code uses Unity Catalog to inspect source and target schemas, match columns, identify transformations, and validate required fields before generating code.
- Enterprise standards built in: A custom Agent Skill packages the company’s naming conventions, audit fields, deduplication rules, merge logic, soft-delete handling, and testing patterns so they can be reused consistently.
- Human review where it matters: A Databricks App helps data designers review source-to-target mappings and transformation logic before code generation, keeping business expertise in the workflow.
- Deterministic, governed outputs: Genie Code handles discovery and orchestration, while rule-based templates generate repeatable PySpark and Spark SQL artifacts designed to run through Lakeflow Jobs.
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