Automate metadata extraction in Oracle AI Data Platform Workbench to reduce manual catalog work and improve data governance.
See how Auto Populate Catalog supports scalable discovery from OCI Object Storage at the resources linked below.
In this demo, you learn how Auto Populate Catalog in Oracle AI Data Platform Workbench automates metadata extraction and catalog population so your team can scale data discovery with less manual effort. The walkthrough explains why manually creating schemas, tables, and partitions can slow down data onboarding, then shows how a metadata extractor can discover datasets and generate catalog entities automatically.
You start by selecting Auto Populate Catalog from the home page and creating a new metadata extractor. You specify a data source in Oracle Cloud Infrastructure Object Storage, such as a bucket or folder, and choose a target catalog where discovered metadata will be created. The extractor scans the location and detects supported formats including CSV, JSON, Avro, Parquet, and Delta Lake.
Next, you choose an approval approach based on your governance needs. With automatic approval, entities are created without manual steps. With manual review, you approve or reject each discovered entity from the reviewed entities view before it is added to the catalog, which is useful for high-value or compliance-sensitive datasets. The demo also covers a best practice to rerun or recreate extractors when object storage content changes so catalog entities stay current and consistent. Once approved, the resulting entities reference the underlying Object Storage data so teams can begin querying and governing datasets faster.
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