Operations Blueprint

Pipeline Architecture

End-to-end operating model for the mock automation platform, including controls, ingest lifecycle, and compute trigger points.

Operational Lanes

Temporary API credentials are minted per user identity for each execution window.

Remote source payloads are pulled into a staging directory for validation.

File hashes and folder parity checks detect drift before cloud upload proceeds.

Cleaned assets are pushed into object storage and tagged by run metadata.

Databricks jobs are launched with run context and tracked as downstream execution units.

Run status, timings, and output artifacts are surfaced for support and operations review.

Failed phases are retried with bounded attempts and resumable checkpoints per run.

Execution slots, queue depth limits, and back-pressure rules protect downstream systems.

Role-based access, credential rotation policy, and immutable run logs support compliance.

Versioned pipeline definitions enable controlled promotion from test to production modes.

Phase 01

Acquire runtime credential and validate policy scope.

Phase 02

Download payload and compute initial file fingerprints.

Phase 03

Copy to staging and synchronize local structure.

Phase 04

Upload approved payload to blob destination.

Phase 05

Trigger Databricks and capture run correlation identifiers.

Phase 06

Finalize status, timings, and post-run diagnostics.