aidp-federate

aidp-federate — cross-source federation in one Spark session

Blend multiple sources by reading each into one Spark session and joining them — a signature differentiator (competitor agents degrade on foreign tables or need an external orchestrator + manual key-joins). Execution is via the bundled scripts/aidp_sql.py helper (no MCP, no AIDP_SESSION required).

When to use

• "Join data from and ", "combine Fusion + the lakehouse", "blend Snowflake with store_sales", any cross-source analysis.
• Also the entry point for a SINGLE external source ("a notebook that connects to Fusion / EPM / ADB / …"): the connection recipe still comes from the spark-connectors plugin's aidp-<source> skill (step 1) — this skill just adds the join. Never hand-roll the source connection. (Check the spark-connectors plugin is installed via claude plugin list; run its aidp-connectors-bootstrap skill once to push the oracle_ai_data_platform_connectors helper package to /Workspace/Shared — via the AIDP MCP, or manually if the MCP can't reach the instance.)

How it works (composition, not new surface)

1. Source reads → spark-connectors plugin. For each external source, use the matching connector skill from oracle-ai-data-platform-workbench-spark-connectors (e.g. aidp-alh/aidp-oracle-db/aidp-exacs, aidp-fusion-bicc/aidp-fusion-rest, aidp-snowflake, aidp-aws-s3, aidp-object-storage, …) to get the spark.read.format(...).option(...).load() recipe for each source. This skill does not re-implement connectors.
2. Join in one session. Run a single cell that reads each source into a DataFrame, registers each as a temp view, and spark.sql(...) the join — all in the same Spark session created by the helper. Use join keys from .aidp/semantic.md / .aidp/catalog.md (don't guess). The helper mints a UPST from the api_key DEFAULT profile and auto-creates a scratch notebook on the target cluster:

   python "$PLUGIN_DIR/scripts/aidp_sql.py" \
     --region <region> --datalake <DATALAKE_OCID> --workspace <ws> --cluster <cluster-key> \
     --code "
   df_lake = spark.table('default.default.customer')
   df_ext  = spark.read.format('...').option('...', '...').load()   # recipe from the connector skill
   df_lake.createOrReplaceTempView('lake')
   df_ext.createOrReplaceTempView('ext')
   spark.sql('''SELECT ... FROM lake l JOIN ext e ON l.key = e.key ...''').show(50, truncate=False)
   "
   

   Returns JSON {status, execution_count, outputs, spark_job_ids, error}. Read the SQL/Spark error from the error field and fix grounded in the catalog — don't guess repeatedly. Smoke-test connectivity first with --code "spark.sql('SELECT 1').show()".
3. Present the blended result; optionally persist as a table (aidp-ingest-file-to-table / manage-tables) or save the join via aidp-verified-queries.

Honesty / framing (no-fabrication)

• Describe this as "federate in one Spark session" (read each source into Spark, then join) — that is literally what the helper does: every source read and the join run in the single Spark session of one aidp_sql.py invocation. Do not claim single-query pushdown federation across heterogeneous sources unless verified live on the target environment — that capability is an open question, so don't overstate it.
• Keep all reads in one invocation/cell so they share the session; a second aidp_sql.py call is a fresh session and the earlier temp views are gone.
• Mind volume: large external reads should be filtered/pushed down at the source where the connector supports it; otherwise sample. Ensure the cluster is RUNNING before executing (see aidp-cluster-ops).

References

• spark-connectors plugin (source reads) · scripts/aidp_sql.py — bundled Spark-SQL executor
• references/semantic-model.md · references/no-mcp-rest-map.md · pairs with aidp-analyzing-data