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Intermediate
Data Science & Analytics
en
Data Engineering with AWS
Udacity
Platform
· California, USA
About this course
Learn to design, build, and automate data systems on AWS. Start by modeling data across relational, document, and graph databases—PostgreSQL, MongoDB, and Neo4j—understanding the tradeoffs of each paradigm. Then build cloud data warehouses in Amazon Redshift, designing dimensional schemas and ETL pipelines that extract from diverse sources, optimize query performance, and validate data quality. Explore modern lakehouse architecture with S3, Glue, Iceberg, and Athena, processing data through bronze, silver, and gold layers using Apache Spark. Finally, orchestrate production pipelines with Apache Airflow: scheduling workflows, managing data lineage, and deploying to Amazon MWAA. By the end of this program, you'll be ready to engineer end-to-end data platforms that scale.
Prerequisite Skills
Skills you should have before taking this course
Python
Relational data models
Command line interface basics
Intermediate Python
Relational database basics
Basic GitHub
GitHub
Command line basics
GitHub basics
Skills Gained
Skills you will develop by completing this course
Data Modeling
PostgreSQL
Database normalization
Denormalized data schemas
Data modeling basics
Data pipeline DAGs
Data pipeline maintenance
Data pipeline design
Data pipeline creation
Apache Spark
Dimensional data modeling
Amazon Redshift
Data lakes
Apache Airflow
AWS data engineering