Building Automated Data Pipelines with Spark,dbt,and Airflow
Completed by Rizkiyana Prima Putra
May 30, 2026
8 hours (approximately)
Rizkiyana Prima Putra's account is verified. Coursera certifies their successful completion of Building Automated Data Pipelines with Spark,dbt,and Airflow
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.
Skills you will gain
- Category: Data Transformation
- Category: Data Mapping
- Category: Data Processing
- Category: Apache Airflow
- Category: Apache Spark
- Category: Data Warehousing
- Category: Data Flow Diagrams (DFDs)
- Category: Data Pipelines
- Category: Data Architecture
- Category: Data Modeling
- Category: Diagram Design
- Category: Service Level
