aidp-check-data

aidp-check-data — pre-migration data-availability scan

Pass-2 of the migrator is expensive (live cluster time + Claude-with-tool-use tokens per cell). Running this scan first catches the "no source table" and "wrong bucket" failure modes in seconds instead of hours.

When to use

• After aidp-build-dag, before aidp-migrate-job.
• After aidp-migrate-catalog (verify schemas + tables actually landed).
• Any time the user wonders "is the data ready".

Invocation

python3 ${CLAUDE_PLUGIN_ROOT}/engine/scripts/check_data_availability.py \
  --root "<databricks-workspace-path>" \
  --cluster <CLUSTER_ID> \
  --aidp-base <AIDP_BASE> \
  --datalake-ocid <DATALAKE_OCID> \
  --workspace-id <WORKSPACE_UUID> \
  --oci-profile <profile>

Or for the workflow-shape input (matches aidp-build-dag's workflow path):

python3 ${CLAUDE_PLUGIN_ROOT}/engine/scripts/check_data_availability_for_workflow.py \
  --job-id <databricks-job-id> \
  --cluster <CLUSTER_ID> \
  --aidp-base <AIDP_BASE> \
  --datalake-ocid <DATALAKE_OCID> \
  --workspace-id <WORKSPACE_UUID> \
  --oci-profile <profile>

What it does

1. Walks every notebook in the manifest.
2. Extracts every reference to:
   • spark.read.table("...") / spark.table("...")
   • spark.read.parquet/csv/json/delta("...")
   • .saveAsTable("...") (target — wrote-to)
   • 3-part name references in %sql / spark.sql(...) strings
3. For each unique reference, opens a Spark session on the cluster and runs a probe:
   • tables → DESCRIBE TABLE <fq> (and SHOW TABLES IN <schema> to differentiate "schema missing" from "table missing")
   • paths → dbutils.fs.ls(path) via the migrator's helper
4. Emits a report with three columns:
   • OK — table/path exists, accessible
   • MISSING — does not exist on the cluster
   • EMPTY — exists but has 0 rows / 0 files (often a sign that the catalog migration succeeded but data wasn't replicated)

How to read the output

Sample shape:

== check_data_availability_for_workflow report ==
TABLES
   OK      <catalog>.<schema>.<table_a>             1234567 rows
   MISSING <catalog>.<schema>.<table_b>             -- DESCRIBE failed: SCHEMA_OR_TABLE_NOT_FOUND
   EMPTY   <catalog>.<schema>.<table_c>             0 rows

PATHS
   OK      oci://<bucket>@<ns>/path/to/file         52 objects
   MISSING oci://<bucket>@<ns>/missing/path         -- listObjects 404

MISSING rows → Pass-2 will definitely fail at those cells. Options:

• Run aidp-migrate-catalog if the underlying schema is missing.
• Configure aidp-bucket-mapping if s3:// → oci:// rewrites haven't been done.
• Mark the table as "out of scope" in the manifest and migrate the consuming notebook with a stub upstream.

EMPTY rows → Pass-2 may pass (no error) but produce empty downstream tables. This is the silent failure mode. Decide whether to:

• Backfill the source.
• Use the synthetic-data path (if your team has one).
• Accept and document.

Reusing the bucket-mapping config

If the manifest references s3:// paths, the scanner also consults <migrator-repo>/config/oci_bucket_tenancy_mapping.json (or whatever your bucket mapping helper resolves) to translate before probing. If the mapping is missing the bucket, the scanner reports a clear S3 bucket X not found in OCI bucket mapping. Fix via aidp-bucket-mapping and re-run.

Performance + cost

• Each table probe is a small DESCRIBE — sub-second on a warm cluster.
• Each path probe is a listObjects against OCI Object Storage — also fast.
• Total scan time scales linearly with unique references; expect <2 min for a workflow with 50 notebooks.
• No Claude tokens spent — this is pure REST + Spark.

Gotchas

• 2-part vs 3-part name resolution — if the source code uses schema.table (no catalog), the scanner resolves against the cluster's current catalog (default). If the user expects a non-default catalog, surface that mismatch.
• Cluster must be Active. A Stopped cluster will make every probe fail with a connection error — instruct the user to start the cluster first.
• CTEs and computed names: the regex extractor catches static names, not names built at runtime via f-strings. False negatives are possible — review notebooks that consume dynamic table names manually.

After this

If everything is OK: proceed to aidp-migrate-job. If anything is MISSING: resolve via aidp-migrate-catalog or aidp-bucket-mapping and re-run this skill.