This project focuses on building an end-to-end real-time data engineering pipeline for Beehiiv data using Apache Kafka. The goal is to process, analyze, and store streaming data efficiently by integrating various technologies such as Python, SQL, AWS, and Snowflake.
By implementing this pipeline, we will gain hands-on experience in real-time data streaming, data transformation, orchestration, and scalable storage using industry-standard tools. The project will simulate real-world data engineering challenges, making it a valuable addition to your portfolio.
- Ingest real-time Beehiiv data using Apache Kafka.
- Process and transform data using Python and SQL.
- Store structured data efficiently in Snowflake for analytics.
- Deploy cloud infrastructure using AWS services like EC2.
- Ensure scalability and reliability of the data pipeline.
- Visualize insights and trends from Beehiiv data.
- Programming Languages: Python, SQL
- Cloud Provider: Amazon Web Services (AWS)
- EC2 (Elastic Compute Cloud) – for hosting and computation
- Streaming Platform: Apache Kafka – for real-time data ingestion and processing
- Data Warehouse: Snowflake – for scalable storage and analytics
- Orchestration & Monitoring (Optional): Apache Airflow for workflow automation
- Visualization Tools (Optional): Metabase/Grafana for dashboarding and reporting
- Simulate Beehiiv subscriber and engagement data.
- Publish real-time events to Kafka topics.
- Consume Kafka data streams using Python.
- Perform necessary transformations using Spark/SQL.
- Store processed data in Snowflake.
- Optimize tables for querying and reporting.
- Deploy Kafka and processing components on AWS EC2.
- Ensure fault tolerance and scalability.
- Track pipeline performance.
- Build dashboards for real-time insights.
This project provides hands-on experience in building scalable and real-time data pipelines, making it a great showcase for data engineering skills in a production-like environment. 🚀