Yes. Every demonstration is designed to be rebuilt alongside the instructor rather than watched passively. You install the same tools, run the same commands, and compare your output to what appears on screen. Keeping a terminal and editor open while you watch is how the environment setup and debugging sections pay off.

Open-source data engineering means building pipelines from freely available, self-hostable tools rather than a managed vendor platform. The stack in this Specialization is PostgreSQL, PySpark, dbt Core, Apache Airflow, Apache Iceberg, MinIO, and Apache Kafka. The advantage is portability: the same patterns run locally, on a company server, or on any cloud.

No. Every tool runs locally, most of them through Docker Compose, and nothing requires a cloud subscription or credit card. That is deliberate, so cost never blocks practice. The concepts map directly onto managed equivalents if your employer runs a cloud platform.

Enough to read a basic SELECT statement. Course 1 builds from simple queries up through joins, CTEs, and window functions, so you do not need prior experience with advanced SQL. If you have never written SQL at all, expect to spend extra time on Module 2 of the first course.

ETL transforms data before loading it into the destination, which suits systems with limited compute. ELT loads raw data first and transforms it inside the warehouse or lakehouse, which is what dbt is built around and what most modern stacks use. Course 2 covers both and explains when each still makes sense.

A lakehouse stores data as open files in object storage while adding the table behaviors a warehouse provides, such as schema evolution, snapshots, and transactional updates. Apache Iceberg supplies that table layer in this Specialization, with MinIO as the object store. It gives you warehouse semantics without locking data into a proprietary format.

Iceberg is fully open and works across multiple engines rather than being tied to a single vendor, which fits a self-hosted stack. The underlying ideas, including snapshots, schema evolution, and compaction, apply to Delta Lake and Hudi as well. Readings cover how the three compare so you can work with whichever your organization uses.

Yes. Course 2 covers Airflow DAGs, tasks, schedules, dependencies, retries, and monitoring basics, and you run Airflow through Docker Compose and build a DAG that orchestrates PySpark and dbt tasks. Readings extend to backfills, catchup, and historical reprocessing.

Batch pipelines process data in scheduled chunks and can reprocess history easily. Streaming pipelines process events as they arrive, which lowers latency but introduces event time, late data, and recovery concerns that batch does not have. Course 3 builds the same style of problem as a stream so the difference is visible in code.

No. Both are introduced from first principles. Course 2 covers Spark architecture, DataFrames, and Spark SQL before any distributed tuning, and Course 3 covers Kafka brokers, topics, partitions, and consumer groups before any streaming code. Python is the only real prerequisite.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.