This project demonstrates a real-time election system that integrates data ingestion, streaming, and visualization. The system is designed to handle voting data in real time using Python, Kafka, Spark Streaming, Postgres, and Streamlit. It showcases an end-to-end pipeline that processes votes as they are cast, aggregates the results, and displays them on a real-time dashboard.
- Data Source: Voting data from Kafka topics
- Data Ingestion: Kafka
- Data Processing: Spark Streaming
- Data Storage: Postgres
- Data Visualization: Streamlit
- Python: Programming language used for scripts and logic.
- Kafka: Distributed streaming platform for real-time data ingestion and processing.
- Spark Streaming: Scalable data processing engine for real-time data aggregation.
- Postgres: Relational database for storing voting data.
- Streamlit: Framework for creating real-time web applications and dashboards.
- Docker Compose: Tool for defining and running multi-container Docker applications.
- Real-Time Data Ingestion: Uses Kafka to stream votes and voter information.
- Data Processing Pipeline: Processes and aggregates voting data with Spark Streaming.
- Dynamic Visualization: Displays real-time voting results using Streamlit.
- Containerized Deployment: Deploys all components in Docker containers for easy setup and management.
- Real-Time Data Processing: Implemented streaming data ingestion and processing with Kafka and Spark Streaming.
- Data Aggregation: Developed logic for real-time data aggregation and enrichment.
- Web Development: Designed interactive real-time dashboards using Streamlit.
- Containerization: Deployed and managed services using Docker Compose.
- Scalability: Explore scaling Kafka and Spark Streaming components for higher data volumes.
- Advanced Analytics: Integrate machine learning for predictive analytics on voting trends.
- Enhanced Security: Implement additional security measures for data protection.
https://public.tableau.com/shared/NFQ9MW42Q?:display_count=n&:origin=viz_share_link
This project demonstrates a complete data engineering pipeline that integrates data ingestion, transformation, and reporting using Azure services. It showcases the end-to-end process from extracting data from an on-premise SQL Server, processing it through Azure Data Factory, Azure Databricks, and finally visualizing it with Power BI. The solution is designed to be a robust and scalable architecture suitable for real-time data processing and analytics.
- Data Source: On-premise SQL Server Database
- Data Ingestion: Azure Data Factory
- Data Transformation: Azure Databricks (Bronze, Silver, Gold layers)
- Data Storage: Azure Data Lake Storage
- Data Analysis & Reporting: Azure Synapse Analytics, Power BI
- Azure Data Factory: Used for orchestrating and automating the data pipeline.
- Azure Databricks: Provides scalable data processing and transformation capabilities.
- Azure Data Lake Storage: Serves as the storage solution for raw and processed data.
- Azure Synapse Analytics: Enables advanced analytics and data warehousing capabilities.
- Power BI: Used for creating interactive and real-time visualizations.
- Automated Data Pipeline: Configured using Azure Data Factory to automatically trigger data ingestion and processing.
- Multi-layer Data Transformation: Utilizes Azure Databricks to transform data through Bronze, Silver, and Gold layers for optimized performance.
- Real-Time Data Reporting: Integrates with Power BI for up-to-date visual analytics and insights.
- Scalable Architecture: Designed to handle large volumes of data and provide scalable data processing.
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Create a Trigger in Azure Data Factory:
- Configure a scheduled trigger to run the pipeline at specified intervals.
- The pipeline performs data extraction, transformation, and loading operations.
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Add Data to On-Premise SQL Server:
- Insert new records into the source tables to test the pipeline.
- Verify that data flows through the pipeline and is reflected in Power BI.
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Monitor Pipeline Execution:
- Use Azure Data Factory's monitoring tools to track the pipeline's progress.
- Ensure data is processed and loaded correctly into the target systems.
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Update Visualizations:
- Refresh Power BI reports to verify that new data is accurately represented in the visuals.
- ETL Processes: Mastered end-to-end ETL (Extract, Transform, Load) processes using Azure tools.
- Data Transformation: Implemented multi-layer data transformation techniques with Azure Databricks.
- Automation: Configured automated triggers and schedules for data pipelines.
- Data Integration: Integrated data from various sources into a unified reporting solution with Power BI.
This project highlights the ability to build a comprehensive data engineering pipeline using modern Azure technologies. It demonstrates practical skills in data ingestion, transformation, and visualization, providing a solid foundation for tackling real-world data engineering challenges.
- Advanced Analytics: Integrate machine learning models for predictive analytics.
- Data Quality Monitoring: Implement monitoring and validation rules to ensure data accuracy.