aidp-siebel

# `aidp-siebel` — Oracle Siebel CRM via AIDP `aidataplatform` ## When to use - User wants to ingest Siebel CRM data (contacts, accounts, opportunities, service requests) into a Spark DataFrame from an AIDP notebook. - User mentions: "Siebel", "Siebel CRM", "S_CONTACT", "S_ORG_EXT", "S_OPTY", Siebel base tables. ## When NOT to use - For Oracle Autonomous DB family (ALH/ADW/ATP) → [`aidp-alh`](../aidp-alh/SKILL.md). - For Oracle PeopleSoft → [`aidp-peoplesoft`](../aidp-peoplesoft/SKILL.md). - For generic Oracle DB → [`aidp-oracle-db`](../aidp-oracle-db/SKILL.md). ## Prerequisites in the AIDP notebook 1. Helpers on `sys.path` (run `aidp-connectors-bootstrap` first). 2. Env vars / OCI Vault secrets: - `SIEBEL_HOST`, `SIEBEL_PORT` (typically `1521`) - `SIEBEL_DATABASE_NAME` (Oracle SID / service) - `SIEBEL_USER`, `SIEBEL_PASSWORD` - `SIEBEL_SCHEMA` (typically `SIEBEL`) - `SIEBEL_TABLE` (a Siebel base table, e.g. `S_CONTACT`, `S_ORG_EXT`) ## Read ```python import os from oracle_ai_data_platform_connectors.aidataplatform import ( AIDP_FORMAT, aidataplatform_options, ) opts = aidataplatform_options( type="ORACLE_SIEBEL", host=os.environ["SIEBEL_HOST"], port=int(os.environ["SIEBEL_PORT"]), database_name=os.environ["SIEBEL_DATABASE_NAME"], user=os.environ["SIEBEL_USER"], password=os.environ["SIEBEL_PASSWORD"], schema=os.environ.get("SIEBEL_SCHEMA", "SIEBEL"), table=os.environ["SIEBEL_TABLE"], ) df = spark.read.format(AIDP_FORMAT).options(**opts).load() df.show(10) ``` ## Pushdown SQL Use `pushdown.sql` to run a complete source query — push joins, filters, and aggregations to the Siebel DB instead of pulling whole base tables into Spark. ```python opts = aidataplatform_options( type="ORACLE_SIEBEL", host=os.environ["SIEBEL_HOST"], port=int(os.environ["SIEBEL_PORT"]), database_name=os.environ["SIEBEL_DATABASE_NAME"], user=os.environ["SIEBEL_USER"], password=os.environ["SIEBEL_PASSWORD"], extra={ "pushdown.sql": ( "SELECT C.ROW_ID, C.LAST_NAME, C.FST_NAME, O.NAME AS ACCOUNT " "FROM SIEBEL.S_CONTACT C " "JOIN SIEBEL.S_ORG_EXT O ON C.PR_HELD_POSTN_ID = O.ROW_ID " "WHERE C.STATUS_CD = 'Active'" ), }, ) df = spark.read.format(AIDP_FORMAT).options(**opts).load() df.show(10) ``` ## Gotchas - **Connector is read-only.** Siebel data should be written back through Siebel's EAI/REST channels, not Spark. The connector is intentionally one-way. - **Underlying Oracle DB.** Siebel runs on Oracle DB; network reachability rules from `aidp-oracle-db` apply. - **`SIEBEL` schema owner.** Standard Siebel install owns all base tables (`S_*`) under the `SIEBEL` schema. The connector user needs `SELECT` privs. - **Soft-delete columns.** Siebel uses `ROW_ID` keys and `LAST_UPD` for incremental ingest. Filter with `WHERE LAST_UPD > :since` via `pushdown.sql` for delta loads. - **Audit columns.** `CREATED`, `CREATED_BY`, `LAST_UPD`, `LAST_UPD_BY` are populated by triggers on every row — useful for change tracking. ## References - Helper: [scripts/oracle_ai_data_platform_connectors/aidataplatform.py](../../scripts/oracle_ai_data_platform_connectors/aidataplatform.py) - Official sample: [oracle-samples/oracle-aidp-samples → `data-engineering/ingestion/Read_Only_Ingestion_Connectors/Oracle_Siebel.ipynb`](https://github.com/oracle-samples/oracle-aidp-samples/blob/main/data-engineering/ingestion/Read_Only_Ingestion_Connectors/Oracle_Siebel.ipynb)