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DataEngineering-1Billion-Rows-Per-Hour\n\nA project that simulates how to build a complete workflow to persist 1 billion rows per hour.\n\n## Project Overview\n\nThis project is designed for learning purposes and involves the following components:\n- A Python producer sending data to Kafka\n- A Java producer sending data to Kafka\n- Data being consumed by Apache Spark\n\n## Steps Involved\n\n1. **Python Producer**: Generates and sends data to a Kafka topic.\n2. **Java Producer**: Generates and sends data to a Kafka topic.\n3. **Kafka**: Acts as the message broker to handle the data streams.\n4. **Apache Spark**: Consumes data from Kafka, processes it, and persists it.\n\n## How to Execute the Project\n\n1. **Boot the project**:\n   ```sh\n   docker compose up --build --force-recreate\n   ```\n\n2. **Run the Python Producer**:\n    ```sh\n    make producer-python\n    ```\n\n3. **Run the Java Producer**:\n    ```sh\n    make producer-java\n    ```\n\n4. **Run PySpark Consumer**:\n    ```sh\n    make pyspark-consumer\n    ```\n\n5. **Access PostgreSQL**:\n    ```sh\n    make connect-postgres\n    ```\n\n## Useful Links\n\n- [Kafka Topics](http://localhost:8080)\n- [Spark Jobs](http://localhost:4040)\n- [Grafana](http://localhost:3000)\n- [Prometheus](https://localhost:9090)\n\n## Credits\n\nTutorial made possible by following [1 Billion Records per Hour](https://www.youtube.com/watch?v=d6AFh31fO7Y\u0026t=3s) Youtube channel","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fknands42%2Fdataengineering-1billion-rows-per-hour","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fknands42%2Fdataengineering-1billion-rows-per-hour","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fknands42%2Fdataengineering-1billion-rows-per-hour/lists"}