Octopai and Databricks Unity Catalog offer a powerful, complementary solution for organizations looking to achieve end-to-end data lineage visibility across complex, multi-cloud environments. While Databricks Unity Catalog provides native lineage capabilities for databases, tables, and views, Octopai extends this functionality by offering detailed file-level and column-level lineage across a broader range of platforms, tools, and systems.
With Octopai’s integration, organizations can seamlessly track data flows from ingestion through transformation to reporting and analysis, filling in critical gaps in Unity Catalog’s native lineage tracking, such as non-Hive metastore tables, file-level lineage, and complex ETL processes. This ensures a complete and accurate picture of data movement across AWS, Azure, and Google Cloud environments, helping users quickly identify the ripple effects of changes in their data ecosystems.
Together, Octopai and Databricks provide:
- Comprehensive Lineage: Octopai adds value by capturing lineage for unsupported patterns in Unity Catalog, including non-standard transformations and files, ensuring no data flow is left undocumented.
- Enhanced Multi-Cloud Support: While Databricks Unity Catalog supports metadata tracking within its environment, Octopai extends this to include comprehensive lineage for AWS, Azure, and GCP, making the combined solution cloud-agnostic and suitable for hybrid cloud environments.
- Streamlined Metadata Management: By automating the metadata extraction process from Databricks through Octopai, users can save time on manual data discovery and documentation tasks, allowing for quicker troubleshooting and impact analysis.
- Operational Efficiency: The integration enhances data governance by providing audit trails and ensuring compliance with regulatory standards. It also accelerates change management by offering accurate insights into data dependencies, schema changes, and data flow transformations.
