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Data Pipeline Automation with Apache Airflow

Build practical data pipeline skills across nine modules, from Airflow fundamentals and your first DAG to scheduling, dynamic workflows, data validation, testing, and production operations. Learn through Python, APIs, SQL, and PostgreSQL examples, with exercises, diagrams, and worked solutions. Complete two portfolio projects: an automated e-commerce sales reporting pipeline and a multi-source inventory replenishment pipeline.

author

Rutvik Acharya

Principal Data Scientist

Atlassian

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What You'll Learn

Build Airflow DAGs with Python, define task dependencies, and configure schedules, time zones, and data intervals.

Automate API and file extraction, transform data, and load validated results into PostgreSQL.

Design flexible workflows using task groups, branching, dynamic task mapping, XCom, and deferrable sensors.

Handle failures, test pipelines, monitor freshness, and complete sales reporting and inventory replenishment projects.

Who Should Attend

Data analysts who want to automate recurring data preparation and reporting workflows.

Aspiring data engineers with basic Python and SQL knowledge who want practical orchestration experience.

Analytics engineers and developers who need to schedule, validate, and maintain data pipelines.

Professionals seeking portfolio projects that demonstrate pipeline reliability, testing, and recovery skills.

CERTIFICATION

Certificate of Completion

Certificate of Participation
Course
9 Modules
28 Hours
40 Lessons
40 Challenges
Language: English
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FAQ

FREQUENTLY ASKED QUESTIONS

You will learn to build, schedule, validate, test, and maintain data pipelines with Apache Airflow. The course covers DAGs, task dependencies, API and file extraction, PostgreSQL loading, failure recovery, and production operations.
Basic Python and SQL knowledge is recommended. The course starts with Airflow and orchestration fundamentals before progressing to more advanced workflows.
The course is designed for data analysts, aspiring data engineers, analytics engineers, and developers who want to automate data workflows and build practical pipeline projects.
The course contains nine modules and 40 lessons, including two portfolio projects. It progresses from pipeline orchestration and local setup to flexible workflows, validation, testing, and production operations.
You will work with Apache Airflow, Python, SQL, PostgreSQL, Docker, APIs, and CSV and JSON files. The course also covers Git and a sample continuous integration workflow.
Yes. The course includes 38 practice exercises with worked answers, diagrams, sample datasets, and runnable labs, followed by two complete portfolio projects.
You will build an automated e-commerce sales reporting pipeline and a multi-source inventory replenishment pipeline. Both projects include business questions, reproducible datasets, implementation code, and worked solutions.
Yes. You will learn about retries, timeouts, task states, logs, validation failures, rejected records, and recovery procedures. The course also explains safe reruns, backfills, and late-arriving data.
Yes. It covers transformation tests, DAG loading and dependency checks, file integration tests, and Git and CI workflows. Production topics include execution approaches, concurrency pools, performance, secrets, access, and data freshness monitoring.
The estimated completion time is 28 to 37 hours, including hands-on exercises and portfolio projects. Your actual time will depend on your familiarity with Python, SQL, Docker, and troubleshooting.
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