Apache Airflow & Data Orchestration Engineering Bootcamp Certificate
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Bootcamp Mastery Certificate B41
Build production workflow orchestration with Airflow 3: architecture, TaskFlow DAGs, operators/sensors, scheduling, dynamic mapping, assets, backfills, versioning, security and operational recovery.
Benchmark: Public learning videos are provided by Data with Jay, Ansh Lamba, Astronomer, Apache Airflow and Databricks. Zuyini independently curates them and applies its own build-and-proof completion rubric; this is not an Apache Airflow, Astronomer or Databricks-issued certification.
Core Video Sequence
01 — Complete 5+ hour Airflow 2026 masterclass
02 — Architecture, scheduler + executors
03 — DAG authoring + modern TaskFlow API
04 — Operators, sensors + XCom boundaries
05 — Scheduling, catchup + backfill
06 — Task groups + dynamic task mapping
07 — Airflow 3 full-course reinforcement
08 — Official bridge: asset-oriented workflows
09 — Official bridge: DAG versioning
10 — Current bridge: Airflow 3 architecture + security
Build-Along Checkpoints
- Checkpoint 1: Run Airflow locally with Docker or Astro CLI; map scheduler, API/UI, executor/workers, metadata database and triggerer responsibilities.
- Checkpoint 2: Build a TaskFlow DAG with typed inputs/outputs, explicit dependencies, retries/timeouts and trigger rules; prove repeatable task behavior.
- Checkpoint 3: Add Connections/Secrets, a sensor or deferrable wait, XCom only for small metadata, plus dynamic task mapping driven by runtime input.
- Checkpoint 4: Implement scheduling plus a safe backfill/reprocessing path; add one asset/data-aware dependency and show idempotency across reruns.
- Checkpoint 5: Add pools/concurrency controls, structured logs and operational metrics/alerts; define a failure taxonomy and runbook escalation path.
- Checkpoint 6: Version/deploy the DAG, inject an upstream or task failure, diagnose it from evidence, recover without duplicate side effects and document rollback.
7-Day Independent Capstone
- Day 1: Choose a real workflow; define data contracts, dependencies, schedule/freshness target, SLO, trust boundaries and idempotency strategy.
- Day 2: Build ingestion and transformation tasks with TaskFlow; add connections/secrets, validation, retries and deterministic outputs.
- Day 3: Add a sensor/deferrable wait, dynamic task mapping and one asset/data-aware trigger; capture execution evidence in the UI/logs.
- Day 4: Implement catchup/backfill and replay logic; deliberately rerun historical intervals and prove no duplicate or corrupted downstream state.
- Day 5: Add pools/concurrency limits, monitoring/alerts and a test suite for DAG structure plus one task-level failure mode.
- Day 6: Deploy/version the workflow; inject dependency/task failure, diagnose from telemetry, recover and quantify runtime/operational tradeoffs.
- Day 7: Publish DAG code, architecture, run history, backfill/recovery proof, monitoring evidence, demo video and production runbook.
Airflow - Orchestrate, Observe & Recover Workflows
Ship an idempotent Airflow 3 workflow that coordinates real data work, handles retries and backfills safely, uses asset/data-aware dependencies where appropriate, and produces observable recovery evidence.
Proof Rubric — What Must Be Shown
- ARCHITECTURE: components + executor rationale
- DAG DESIGN: TaskFlow + explicit dependencies
- ORCHESTRATION schedule/assets + backfill:
- SAFETY: idempotency + secrets
- DYNAMISM: runtime mapping + sensors
- OBSERVABILITY: logs, metrics + alerts
- RECOVERY: failure drill + safe rerun
- OPERATIONS: versioning + runbook