To develop an automated, scalable ETL orchestration system using Apache Airflow for workflow scheduling and Snowflake as a cloud data warehouse, ensuring reliable pipeline execution, monitoring, and optimized data transformations for analytical reporting.
Study DAG-based workflow orchestration in Airflow.
Install and configure Apache Airflow environment.
Design DAGs for multi-stage ETL pipelines.
Extract data from APIs and relational databases.
Transform datasets using Python and SQL.
Load processed data into Snowflake warehouse.
Implement incremental loading strategies.
Schedule automated daily and hourly jobs.
Set up task dependencies and retry mechanisms.
Monitor pipeline logs and failures.
Optimize Snowflake queries using clustering keys.
Apply role-based access controls.
Implement data quality validation tasks.
Document workflow design and performance metrics.
