Data sensors are deployed in airplane, trucks, server rooms, machines and other premises to capture the data. These data are sent to cloud via a Gateway. In the cloud, the data is integrated & processed. Insights from the processed data are transferred to the Analytic tool at the client end.
But here each time of data integration and processing, we need to depend on the developers to write complex codes. Hence a data scientist who has less knowledge in code is unable to perform this task.
Hence here a codeless, drag-drop UI is a good solution.
How it Helps
- Visual drag-&-drop UI
- Maximize productivity by using code-free drag-&-drop interface to build, deploy, monitor, and manage data integration
- Multiple Language Support
- Use the visual interface or write your own code in Python, .NET, or ARM to build pipelines using your existing skills. Choose from a wide range of processing services and put them into managed data pipelines to use the best tool for the job or insert custom code.
- Code-free data movement
- Improve data Integration with 50+ natively supported connectors including, AWS S3 and Redshift, Google BigQuery, SAP HANA, Oracle, DB2, Mongo DB, and many more.
- Comprehensive control flow
- Facilitate looping, branching, conditional constructs, on-demand executions, and flexible scheduling with extensive control-flow constructs.
Step 1: Build scalable data flow with codeless UI, or write your own code
Build data integration and easily transform and integrate big data processing with the visual interface.
Step 2: Schedule, run and monitor your pipelines
Invoke pipelines with on-demand and trigger-based scheduling. Visually monitor pipeline activity with logging and pipeline history, and track error sources.
Thus this framework facilitates:
- Creating, scheduling, and monitoring of data pipelines.
- Orchestrating data integration & processing workflows wherever data lives.
- Accelerating data integration & processing with multiple native data connectors.