

OpenETL
OpenETL is a robust and scalable ETL (Extract, Transform, Load) application built with modern technologies like FastAPI, Next.js, and Apache Spark. The application offers an intuitive, user-friendly interface that simplifies the ETL process, empowering users to effortlessly...
Cost / License
- Freemium (Subscription)
- Open Source
Platforms
- Software as a Service (SaaS)
- Self-Hosted
- Docker




OpenETL
Features
- Data visualization
Tags
- etl
- big-data
OpenETL News & Activities
Recent activities
OpenETL information
What is OpenETL?
OpenETL is a robust and scalable ETL (Extract, Transform, Load) application built with modern technologies like FastAPI, Next.js, and Apache Spark. The application offers an intuitive, user-friendly interface that simplifies the ETL process, empowering users to effortlessly extract data from various sources, apply transformations, and load it into your desired target destinations.
Key Features of OpenETL:
- Backend: Powered by Python 3.12 and FastAPI, ensuring fast and efficient data processing and API interactions.
- Frontend: Built with Next.js, providing a smooth and interactive user experience.
- Compute Engine: Apache Spark is integrated for distributed data processing, enabling scalable and high-performance operations.
- Task Execution: Utilizes Celery to handle background task processing, ensuring reliable execution of long-running operations.
- Scheduling: APScheduler is used to manage and schedule ETL jobs, allowing for automated workflows.
Features
- ETL with Full Load: Easily extract data from different sources and load it into your preferred target location.
- Scheduled Timing: Schedule your ETL tasks to run at specific intervals, ensuring your data is always up-to-date.
- User Interface: A clean and user-friendly UI to monitor and control your ETL processes with ease.
- Logging: Comprehensive logging to track every action, error, and data transformation throughout the ETL pipeline.
- Integration History: Keep track of all your integration jobs with detailed records of past runs, including statuses and errors.
- Batch Processing: Handle large volumes of data by processing it in batches for better efficiency.
- Distributed Spark Computing: Utilize Spark for distributed computing, allowing you to process large datasets efficiently across multiple nodes.
Benchmark Check the detailed performance benchmark of OpenETL here: https://cdn.dataomnisolutions.com/main/app/benchmark.html
