Automating ETL Pipelines with Airflow for Data Orchestration to PostgreSQL (Data Warehouse).

This project requires robust data pipelines that can efficiently process high-volume datasets. To address this, I engineered an Inflation Analysis System using an automated ETL workflow. The pipeline extracts data from disparate sources, transforms it into a star schema in PostgreSQL (Data warehouse), and integrates with BI tools for real-time visualization and analytics. Orchestrated with Apache Airflow, the system ensures scalability, automation, and reliability.

Challenge

The challenge was to process weekly food price data from web sources and transform it into actionable insights. To meet this need, the ETL system was designed to:

• Extract: Automate ingestion of structured data from web APIs.
• Transform: Normalize and structure the data into a star schema optimized for analytical queries.
• Load: Store the transformed data in PostgreSQL, ensuring data integrity and accessibility.
• Visualize: Enable decision makers to explore inflation trends through BI dashboards.

System Architecture

Tools and Technologies

• ETL Orchestration: Apache Airflow for automation and scheduling.
• Database: PostgreSQL for data warehousing and OLAP.
• Programming Language: Python for data manipulation and API interaction.
• Visualization: Power BI for dynamic reporting and trend analysis.

ETL Pipeline Design

The pipeline adheres to a modular workflow orchestrated in Apache Airflow:

• Data Extraction: Fetches weekly datasets from web sources and stores them in raw format.
• Data Transformation: Processes data into fact and dimension tables using Python and Pandas.
• Data Loading: Loads the star schema into PostgreSQL.
• BI Integration: Connects PostgreSQL to Power BI for interactive visualizations.

Implementation Details

1. Data Extraction

The extraction stage uses Python’s requests library to interact with a RESTful API. Airflow schedules and monitors this task to ensure reliability.

2. Data Transformation

The raw data is normalized into a star schema. A fact table stores price metrics, while dimension tables capture commodities, regions, and dates.

3. Data Loading

The transformed data is loaded into PostgreSQL, enabling optimized querying for BI tools.

4. Automation with Airflow DAGs

Apache Airflow is critical for orchestrating each stage of the ETL pipeline, enabling automated, reliable data workflows. In this setup, Airflow DAGs (Directed Acyclic Graphs) define the sequence and dependencies of tasks from extraction to visualization. The DAG is scheduled to run weekly to match the incoming data cadence, ensuring fresh data is processed and loaded into the data warehouse in PostgreSQL.

5. Test Logs and Monitoring

To ensure data quality and pipeline reliability, the ETL system incorporates comprehensive logging and monitoring mechanisms. Each stage of the pipeline logs its execution details, including task statuses, processing times, and potential errors. These logs are stored in a dedicated logs table in PostgreSQL, allowing for centralized monitoring and historical tracking

Visualization and Insights

Using BI tools (eg. Power BI, Tableau,Looker Studio) the PostgreSQL database is connected to create visualizations such as:

• Inflation trends by region and commodity.
• Year-over-year price comparisons.
• Forecasting future price changes.
• These dashboards enable stakeholders to make data-driven decisions - based on reliable and timely data.

Try it: https://lookerstudio.google.com/reporting/042ab5ef-2863-4bfb-a119-2bb2765742e2

Conclusion

This Inflation Analysis System demonstrates the power of data engineering principles in solving real-world challenges. The automated ETL pipeline, built with Airflow and PostgreSQL, provides a scalable and efficient foundation for economic insights. By integrating BI tools, the system transforms raw data into actionable intelligence for better decision-making.