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oracle-ai-data-platform-workbench-spark-connectors Claude Code Plugin

Author
Oracle
Category
Development
Topics
Data Engineering & Analytics · Databases & Storage · Cloud, Deployment & CI/CD
First cataloged
2026-07-09 (UTC)
Explanation last updated
2026-09-25 (UTC)
Source (GitHub) last updated
2026-10-05 (UTC) (10 days ago)

The explanation below is AI-generated. Please verify it against the sources.

Each skill produces plain Python code using Spark JDBC, structured streaming, cloud storage connectors, or REST-to-DataFrame patterns that can run directly inside a workbench notebook. The README also states that the connectors were validated against a live workbench test cluster running Spark 3. 5. 0, with most test rows passing and a few marked as usable without further validation. The item description gives a smaller skill count than the README, so the exact current number of connectors is not fully certain from the sources provided.

About the service

Oracle AI Data Platform Workbench, as described on its homepage, is a cloud service for cataloging, ingesting, and analyzing organizational data, providing the platform and framework needed to build data analytics pipelines. The plugin's skills are built to let this workbench's Spark notebooks read from and write to many external systems, including Oracle databases, Fusion applications, EPM Cloud, Kafka-based streaming, object storage, and third-party services such as Salesforce, NetSuite, Snowflake, and major public cloud storage systems.

What you can do with oracle-ai-data-platform-workbench-spark-connectors

  • Connect Workbench Spark notebooks to Oracle Autonomous Database (ALH/ADW/ATP), Exadata Cloud Service, and generic Oracle DB using wallet, IAM DB-token, or plain credential authentication
  • Pull data from Fusion ERP/HCM/SCM REST APIs, Fusion BICC bulk extracts, EPM Cloud Planning, and Essbase 21c into Spark DataFrames
  • Read from OCI Streaming (Kafka-compatible), OCI Object Storage, and Apache Iceberg tables stored on OCI
  • Connect to external relational databases such as PostgreSQL, MySQL/HeatWave, SQL Server, Azure SQL, DB2, and Hive using JDBC or the platform's native format handler
  • Access SaaS systems including Salesforce and NetSuite in read-only mode, and Snowflake in read-only mode
  • Work with Azure ADLS Gen2 and AWS S3 storage, generic REST APIs, custom JDBC drivers, and Excel files
  • Run a one-time bootstrap skill that uploads helper code to the workbench workspace and confirms it loads correctly
  • Use cross-cutting patterns such as pushing full SQL queries to the source, referencing existing catalog connections by id, and pointing REST connectors to manifest files stored in the workspace

Sources

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