I finished Data Storage and Queries, the third course in the DeepLearning.AI + AWS Data Engineering Professional Certificate, on October 3, 2026. The labs were hands-on on AWS, and I cleared the final exam.
Course
Data Storage and Queries, DeepLearning.AI and Amazon Web Services, on Coursera.
Verify certificate: https://coursera.org/verify/6AY7BEQK4RBQ
What I did
- Studied how data is physically stored. That covers storage on disk and in memory, then object, block and file storage, and the databases built on top of those.
- Followed the evolution of storage architectures. From data warehouses to data lakes to data lakehouses, comparing the advantages and drawbacks of each.
- Designed a simple data lake with AWS Glue, then built a data lakehouse with AWS Lake Formation and Apache Iceberg.
- Looked at how queries work behind the scenes. I wrote more advanced SQL, compared query performance on row-oriented versus column-oriented storage, and ran streaming queries with Apache Flink.
What I took from it
Storage and queries are connected. How data is laid out on disk, row or column, decides how fast a query can run on it, so the storage choice has to be made with the queries in mind.
Series: Data Engineering Log, part 3 of 3, from the DeepLearning.AI + AWS Data Engineering Professional Certificate.
More of my work is on GitHub.
