Building Automated Data Pipelines with Spark,dbt,and Airflow

About this course

You'll master the art of building production-ready data pipelines that automatically process millions of records. In this hands-on course, you'll design end-to-end workflows that integrate diverse data sources—from databases and APIs to real-time streams—using industry-standard tools like Apache Spark, dbt, and Apache Airflow. You'll learn to create robust data models that preserve historical changes, implement performance optimizations that reduce processing time by 30% or more, and build automated workflows with intelligent retry logic and monitoring alerts. By the end, you'll have created a complete data pipeline system that demonstrates the technical skills data engineering teams need most. You'll know how to unify fragmented data sources, apply advanced transformation techniques, and ensure your pipelines run reliably at scale. This practical experience directly translates to the challenges you'll face as a data engineer, data analyst, or anyone working with large-scale data systems in modern organizations.

What you will learn

• Build end-to-end data pipelines that automatically ingest from databases, APIs, and streams using Spark, dbt, and Airflow tools.
• Design data models with historical tracking using SCD Type 2 patterns to preserve complete change history for analytics.
• Create automated workflows with intelligent retry logic, SLA monitoring, and parameterization for production reliability.
• Optimize Spark job performance using partitioning and caching strategies to achieve 30%+ runtime improvements.