Apache Airflow
Own the pipelines nobody else wants to be paged about. Build, version and operate production workflows on Airflow 3 — and migrate the Airflow 2 estate you inherited. Airflow 3 isn't a version bump: if you learned Airflow 2, several things you were taught no longer exist. Built for fresh IT graduates and working engineers alike.
Airflow 3 isn't a version bump. If you learned Airflow 2, several things you were taught no longer exist — and Airflow 2 has reached the end of its open-source support life. A large share of teams still run 2.x, which means engineers are maintaining pipelines on a version that no longer receives community support while most training material still teaches the old model. This course teaches Airflow 3 — and how to get an Airflow 2 estate onto it. Migration is a first-class track here, not a footnote: assessment, automated detection and fixes, configuration linting, staged cutover, rollback planning.
The syllabus is Airflow 3 throughout: Assets
replacing Datasets, with data-driven scheduling that in practice
is one of the most-used features in the product;
event-driven triggering so DAGs react to
external systems instead of waiting for cron;
DAG versioning and what it changes about
debugging, auditing and compliance; the
Task Execution API and Task SDK —
a service-oriented model where tasks no longer touch the
metadata database directly, enabling isolation and remote
execution; and the rewritten React UI. You'll also learn exactly
what was removed — SubDAGs, execution_date, XCom
pickling, direct database access from task code — because those
removals are what bite a migration.
It is designed for two audiences at once. If you are a fresh IT graduate, Airflow is one of the highest-leverage single tools in the data stack — the de facto standard for workflow orchestration, appearing across a very large share of data engineering job descriptions. If you are a working engineer — or a team still on Airflow 2 that needs to migrate — the version transition has created immediate demand for people who know the current model and can move legacy estates onto it. The program runs 7 weeks (~50 hours), live online and lab-heavy, closing with a production orchestration platform capstone.
Where This Fits in Our Catalogue
- Apache Airflow (this course) — the conductor: it decides what runs, when, in what order, and what happens when something fails at 3am.
- Apache Kafka moves the events · Apache Spark & PySpark processes the data
- Snowflake Data Warehouse stores it · Microsoft Power BI presents it
- MLOps Engineering — where orchestrated retraining and deployment pipelines meet the ML platform.
When Airflow Is the Wrong Tool
For sub-second streaming, simple cron jobs, or in-application task queues, Airflow's operational weight isn't justified — a stream processor, a crontab, or a job queue is the right answer. Teams also genuinely evaluate alternatives like Dagster and Prefect, and this course covers that comparison fairly rather than pretending it doesn't exist. Knowing when not to deploy an orchestrator — and which one fits — is a senior-engineer signal, and we teach the boundary explicitly.
Course at a Glance
- Duration: 7 weeks | ~50 hours | live + hands-on
- Format: Live online · lab-heavy · portfolio of running pipelines
- Prerequisite: Working Python, basic SQL, comfort with the command line and Docker basics. No prior orchestration experience required.
- Curriculum: 8 modules, from orchestration fundamentals to a first-class Airflow 2 → 3 migration track
- Certification: Aligned to Astronomer's Apache Airflow 3 certifications (Fundamentals and DAG Authoring)
- Curriculum reviewed: July 2026 — Airflow 3 is evolving across minor releases; this syllabus tracks the current line
Course Highlights
- Every module leads with what you build and ship, not what you "learn about"
- Airflow 3 throughout: Assets, event-driven scheduling, DAG versioning, the Task SDK
- A first-class Airflow 2 → 3 migration track — the removals that bite, the tooling that catches them
- Reliability engineering: idempotency, safe backfills, tasks you can rerun without corrupting data
- Testing and CI/CD for DAGs — pipelines treated as software, not scripts
- Integrations in practice: Spark, Snowflake, dbt, Kubernetes, ML retraining triggers
- Production operations plus honest build-vs-buy on Astronomer, MWAA and Cloud Composer
Hands-on Projects & Portfolio
Dynamic task mapping, retries, SLAs and alerting.
Downstream DAGs trigger on data updates rather than on a schedule.
Reacts to an external system rather than to cron.
Extract, transform in Spark, load to a warehouse, refresh a BI layer.
Triggered by a drift signal, with model validation gates.
Unit-tested DAGs with environment promotion.
A deliberately legacy DAG set, with an assessment report and a cutover plan.
Production orchestration platform with monitoring, RBAC and a documented runbook.
Tools & Stack
Apache Airflow 3.x Python Docker Kubernetes Celery PostgreSQL dbt Apache Spark Snowflake Apache Kafka AWS GCP Azure Astronomer AWS MWAA Cloud Composer Git GitHub Actions Prometheus Grafana
Certification Alignment
- Syllabus aligned to Astronomer's Apache Airflow 3 Fundamentals certification — architecture, scheduling, operations
- Modules 2–5 cover the core of DAG Authoring for Apache Airflow 3 — TaskFlow API, dynamic task mapping, templating, asset-driven scheduling
- Presented as preparation and alignment — no official partnership or accreditation implied, and no exam outcome guaranteed
Career Outcomes
Airflow is the de facto standard for data workflow orchestration and appears in a very large share of data engineering job descriptions — one of the highest-leverage single tools in the data stack. The version transition has created immediate demand for engineers who know the current model and can migrate legacy estates. This course targets:
- Data Engineer
- Analytics Engineer
- MLOps Engineer
- Data Platform Engineer
- Workflow / Orchestration Engineer
- DevOps Engineer (data platforms)
- Backend Engineer owning scheduled systems
- Data Architect
Who This Course Is For
- Final-year students and fresh IT graduates targeting data engineering roles
- Python developers moving into data engineering
- Data analysts and analytics engineers automating manual, scheduled work
- ML engineers orchestrating training and retraining pipelines
- DevOps and platform engineers who now operate someone else's Airflow
- Teams still on Airflow 2 who need to migrate — this course was built with you in mind
Material Includes
- Class recordings
- Lab guides & DAG starter repos
- Migration assessment checklist & runbook templates
- Certificate
Prerequisites
- Working Python and basic SQL
- Comfort with the command line; Docker basics
- No prior orchestration experience required
Estimated Time: 6 Hours
You ship:- A running Airflow 3 environment stood up from scratch, with an annotated architecture map — scheduler, API server, workers, triggerer — and a defended executor choice for a given workload
- What an orchestrator is actually for; DAGs as code
- The scheduler, API server, workers, metadata database and triggerer; the service-oriented execution model
- Executor choices (Local, Celery, Kubernetes, Edge) and the trade-offs behind each
Estimated Time: 7 Hours
You ship:- A multi-source ELT DAG with dynamic task mapping, task groups and branching — written in the modern SDK namespace, readable at fifty tasks
- The modern SDK namespace and the TaskFlow API; tasks and dependencies; operators, sensors and hooks
- XComs and their limits; connections and variables; templating and macros
- Dynamic task mapping; task groups; branching and trigger rules; DAG design patterns that stay readable
Estimated Time: 6 Hours
You ship:- An asset-driven pipeline where downstream DAGs trigger on data updates, plus an event-driven workflow that reacts to an external system instead of cron
- Schedules and timetables; catchup and backfill done properly; data intervals versus the newer non-interval model
- Assets and data-driven scheduling — among the most-used features in practice
- Cross-DAG dependencies; event-driven triggering from external systems
Estimated Time: 6 Hours
You ship:- A pipeline hardened for 3am: idempotent tasks, retries with backoff, SLAs, alerting callbacks, and a safe backfill run that doesn't corrupt downstream data
- Idempotency as a design requirement; retries and exponential backoff; timeouts and SLAs
- Alerting and callbacks; partial failure and recovery
- Backfilling safely; tasks you can rerun without corrupting downstream data
Estimated Time: 6 Hours
You ship:- A tested, CI/CD-deployed DAG repository with validation gates and environment promotion — and DAG version history used to debug a regression
- Unit-testing DAGs and tasks; DAG validation in CI; local development environments
- DAG versioning and what it changes about debugging and audit
- Deployment strategies; managing dependencies across environments
Estimated Time: 6 Hours
You ship:- An orchestrated multi-tool stack — extract, transform in Spark, load to a warehouse, refresh a BI layer — plus an ML retraining DAG triggered by a drift signal with validation gates
- Providers and the operator ecosystem; orchestrating Spark jobs, Snowflake and warehouse loads, dbt runs
- Kafka-adjacent event workflows; cloud services on AWS/GCP/Azure; containerised and Kubernetes-native tasks
- ML pipelines including training and retraining triggers
Estimated Time: 7 Hours
You ship:- A production deployment on Kubernetes — scaled schedulers and workers, secrets backend, RBAC for two teams, remote logs, metrics — plus a written build-vs-buy recommendation across the managed options
- Deploying on Kubernetes and with Docker; scaling schedulers and workers; resource isolation; secrets backends
- RBAC and multi-team tenancy; logging and remote log storage; monitoring and metrics; performance tuning and cost
- Managed options — Astronomer, AWS MWAA, Google Cloud Composer — with honest build-vs-buy reasoning
Estimated Time: 6 Hours
You ship:- A full Airflow 2 → 3 migration of a deliberately legacy DAG set — assessment report, automated fixes, staged cutover and rollback plan — and your capstone: a production orchestration platform, defended
-
Assessing an existing estate; the removals and
breaking changes that bite — SubDAGs,
execution_date, schedule handling, XCom pickling, direct database access - Automated detection and fixes; configuration linting; staged cutover, rollback planning and validation
- Capstone design, build and defense
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