DEFINITION
Data engineering on AWS is the practice of ingesting, transforming, storing, and serving data with services such as S3, Glue, EMR, Redshift, Kinesis, and Lake Formation so analytics and ML workloads have reliable, governed datasets.
Core building blocks
Landing zones in S3; cataloguing with Glue; batch/stream transforms on Glue or EMR; warehouses/lakes for query; IAM and Lake Formation for access; observability for freshness and failures.
AI-ready data
GenAI and ML amplify data debt. Chunkable documents, PII handling, lineage, and reproducible feature tables matter as much as model choice. Capstones often fail on messy sources, not model APIs.
FAQ
Glue or EMR for ETL?
Glue fits serverless Spark jobs and catalog-centric lakes; EMR fits long-running or heavily customized Spark/Hadoop clusters. Many teams use both across workloads. See our Glue vs EMR comparison.
Expert
Deepskilling Cloud & Data Faculty — Programme Lead, Cloud, Data Engineering & System Design. Mentors for AWS/Azure certification paths, data engineering on AWS/GCP, and fundamentals-to-mastery system design.