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.

Building Automated Data Pipelines with Spark,dbt,and Airflow
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Building Automated Data Pipelines with Spark,dbt,and Airflow
This course is part of Open source Data Engineering with Spark, dbt & Airflow Professional Certificate

Instructor: Professionals from the Industry
Included with
Recommended experience
What you'll 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.
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March 2026
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There are 11 modules in this course
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