Build & Transform Data Pipelines
By External Provider
About this course
Ready to build data pipelines that power modern analytics? This course transforms you from someone who processes data manually into a data engineer who creates automated, modular pipeline systems. This Short Course was created to help Data Management and Engineering professionals accomplish scalable, maintainable data processing workflows. By completing this course, you'll be able to design and implement production-ready pipelines that seamlessly move data from raw sources to analytics-ready destinations using industry-standard tools. By the end of this course, you will be able to: • Create modular pipeline stages for data ingestion, cleansing, transformation, and loading • Implement automated workflows using Python, dbt, and Airflow • Deploy scalable solutions on cloud platforms like AWS and Snowflake This course is unique because it focuses on hands-on implementation with real-world scenarios using popular open-source tools that drive today's data infrastructure. To be successful in this project, you should have a background in basic SQL, Python programming, and familiarity with data concepts.
What you will learn
- Modular pipeline design enables maintainable, scalable data systems that can adapt to changing business requirements.
- Integration of complementary tools (Spark, dbt, Airflow) creates more robust and efficient data processing workflows than single-tool approaches.
- Proper separation of concerns between ingestion, transformation, and loading stages reduces complexity and improves debugging capabilities.
- Automation and orchestration are essential for reliable, production-grade data systems that minimize manual intervention and human error.