Sparkflows provides a huge set of processors to perform different kind of data transformation task. With its less code functionality, migrating to Sparkflows would be smooth and seamless.
Some of the useful processors in Sparkflows to perform data analytics are listed below
1. Sparkflows supports multiple connectors to read data from such data csv files, excel files, databases, parquet files and so on. It can process huge amount of data and is not limited in terms of number of rows.
2. It supports Sort By processor to sort incoming dataset same as Sort function in Excel.
3. It supports various Filter processor to filter incoming dataset based on condition defined.
4. It supports Case-When processor to output data dependant on incoming data similar to What-if condition in Excel
5. It supports various language nodes such as SQL node to implement complex logic same as writing complex formula in an Excel
6. It supports multiple Add Columns nodes to compute and add new columns to the dataset same as Formula in an Excel
7. It supports various Grouping nodes such as Group By, Cube nodes
8. It supports various Visualization nodes to represent data in various Chart Type options.
9. Processed data can be saved in a database table or can be downloaded in multiple formats such as CSV