Building Automated Data Pipelines with Spark,dbt,and Airflow
Completed by Eugene Belenkov
September 2, 2026
8 hours (approximately)
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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: Apache Airflow
- Category: Data Warehousing
- Category: Data Transformation
- Category: Data Mapping
- Category: Extract, Transform, Load
- Category: Data Processing
- Category: Data Modeling
- Category: Diagram Design
- Category: Data Architecture
- Category: Dataflow
- Category: Service Level
- Category: Enterprise Security
