I finished Introduction to Data Engineering, the first course in the DeepLearning.AI + AWS Data Engineering Professional Certificate, on October 2, 2026. The labs were hands-on on AWS, and I cleared the final exam.
Course
Introduction to Data Engineering, DeepLearning.AI and Amazon Web Services, on Coursera.
Verify certificate: https://coursera.org/verify/AGUYGXWGDWE7
What I did
- Walked the full data engineering lifecycle. Data is generated in source systems, ingested, transformed, stored and then served to downstream consumers. Alongside those stages sit the undercurrents that touch all of them.
- Turned stakeholder needs into system requirements. The course has you start from what the business actually needs, write that down as requirements, and only then choose tools and technologies. The order matters, because it keeps the tool choice from driving the design.
- Spun up batch and streaming pipelines on AWS. The end goal of the course is pipelines that serve product recommendations. Batch processes data collected over a window of time. Streaming processes events as they arrive. Building both side by side made the difference concrete.
- Provisioned infrastructure with Terraform. Instead of clicking resources together by hand, the setup is described as code and applied, so the environment can be recreated.
What I took from it
The lifecycle works as a checklist I can run against any pipeline: where does the data come from, how does it get in, where does it live, how is it transformed, and who consumes it.
I had already built this kind of system before. For a previous project I handled 500GB of data and wrote ETL pipeline scripts that parsed it, cleaned it and loaded it into databases. The course made me realize I already had hands-on practice with this, and the certificate now confirms it.
Series: Data Engineering Log, part 1 of 3, from the DeepLearning.AI + AWS Data Engineering Professional Certificate.
More of my work is on GitHub.
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