Automated ELT Pipeline Development
Nabeel Farooq
I developed a fully automated ELT pipeline leveraging Snowflake, DBT, Airflow, and Iceberg to optimize data workflows, enhance performance, and ensure scalability. The pipeline was designed to efficiently ingest, process, and transform large datasets.
Technologies Used:
Data Warehouse: Snowflake
Data Transformation: DBT
Orchestration: Airflow
Data Storage: Iceberg, Amazon S3
Process:
Data Ingestion: Data is collected from various sources and stored in Amazon S3.
Data Loading: Airflow automates the movement of data into Snowflake.
Data Transformation: DBT performs the transformations for analytics.
Orchestration: Airflow schedules and monitors the pipeline.
Key Features:
Scalable Data Processing using Snowflake
Modular Transformations with DBT
Reliable Orchestration with Airflow
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Posted Apr 17, 2025
Built an automated ELT pipeline using Snowflake, DBT, Airflow, and Iceberg for scalable data processing and lakehouse integration
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