Learn to build robust, production-style data pipelines using SQL, NoSQL, and big data technologies.
Every data-driven company needs engineers who can reliably move, clean, and structure data at scale. This course takes you from relational and non-relational database fundamentals through to building complete ETL pipelines and working with distributed processing frameworks like Apache Spark.
As our most comprehensive program, the 14-week format allows time to go deep on data warehousing concepts and complete a capstone pipeline project you can showcase to employers.
Writing queries, joins, aggregations, and designing relational database schemas.
Understanding document, key-value, and columnar databases and when to use each.
Extract, transform, and load workflows for moving data between systems reliably.
Process large-scale datasets efficiently using distributed computing with Spark.
Design star and snowflake schemas and understand modern data warehouse architecture.
Build a complete end-to-end data pipeline from raw ingestion to a query-ready warehouse.
Aspiring data engineers, analysts wanting to move into engineering roles, and developers interested in data-heavy systems.
Basic Python or programming familiarity is helpful. Complete beginners are supported with an early foundations module.
Graduates are prepared for roles such as Junior Data Engineer, ETL Developer, and Data Pipeline Associate.
Basic Python knowledge is helpful, and beginners are welcome — foundational scripting concepts are reinforced early in the course before building into pipeline work.
The Python course teaches general-purpose programming, while Data Engineering focuses specifically on building data pipelines, working with databases, and processing large datasets with tools like Apache Spark.
Yes, hands-on labs use real and realistic datasets so you practice building ETL pipelines and data warehouses the way you would on the job.
Graduates are prepared for roles such as Junior Data Engineer, ETL Developer, and Data Pipeline Associate.