# oracle-ai-data-platform-workbench-spark-connectors

This plugin gives Claude Code skills for wiring data sources into Oracle AI Data Platform Workbench Spark notebooks and producing connector code.

- plugin · `type=plugin` · Claude Code · ★ 57
- Author: Oracle
- License: UPL-1.0 · policy=link-only · description only; the file stays with the author
- Source: https://github.com/oracle-samples/oracle-aidp-samples/tree/HEAD/ai/claude-code-plugins/oracle-ai-data-platform-workbench-spark-connectors
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/plugins/oracle-ai-data-platform-workbench-spark-connectors

## What it does

The plugin covers Oracle Autonomous DB, Exadata, Fusion services, OCI Object Storage, Kafka, Apache Iceberg, PostgreSQL, MySQL, Snowflake, BigQuery and other sources. Its skills produce plain Python for Spark JDBC, structured streaming, `oci://`, `s3a://`, `abfss://`, or REST-to-DataFrame flows.

A workspace needs a one-time bootstrap before connector work begins. Workbench tools upload the helper package to `/Workspace/Shared/oracle_ai_data_platform_connectors/`, then a bootstrap notebook checks that the package imports. Excel input uses `zipfile` and an XML parser, while SSL PostgreSQL connections use a driver loaded at runtime.

Each source still needs its own credentials or access setup, such as a wallet, IAM token, HTTP Basic credentials, AWS keys, or database login. The generated code cannot reach a source until that access is configured.

## Install

### Claude Code (native platform)

**Plugin marketplace**
In the agent chat (not a shell):
```text
/plugin install oracle-ai-data-platform-workbench-spark-connectors@claude-plugins-official
```
In the agent chat (not a shell):
```text
/plugin marketplace add anthropics/claude-plugins-community
```
In the agent chat (not a shell):
```text
/plugin install oracle-ai-data-platform-workbench-spark-connectors@claude-community
```
In the agent chat (not a shell):
```text
/plugin marketplace add anthropics/claude-plugins-official
```

## When to use it
- connect Oracle Autonomous DB to Spark
- load a REST API response into a DataFrame
- read tables from PostgreSQL, Snowflake, or BigQuery
- configure streaming reads from OCI Streaming
- process Excel, S3, and Object Storage files

## Example prompts
- Set up the connector skills in this workspace and run the bootstrap check.
- Write Spark code that reads an ATP table using the available authentication method.
- Load data from a REST API into a DataFrame using a manifest at `manifest.path`.

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From the same set: [oracle-samples/oracle-aidp-samples](https://affpapa.org/en/nevibecode/by/oracle-samples/oracle-aidp-samples.md) — 3 cards

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