Data transformation simplified for any Data platform.

Features: The package has complete ETL process -

1. Uses metadata, transformation & data model information to design ETL pipeline
2. Builds target transformation SparkSQL and Spark Dataframes
3. Builds source & target Hive DDLs
4. Validates DataFrames, extends core classes, defines DataFrame transformations, and provides UDF SQL functions.
5. Supports below fundamental transformations for ETL pipeline -
   • Filters on source & target dataframes
   • Grouping and Aggregations on source & target dataframes
   • Heavily nested queries / dataframes
6. Has complex and heavily nested XML, JSON, Parquet & ORC parser to nth level of nesting
7. Has Unit test cases designed on function/method level & measures source code coverage
8. Has information about delpoying to higher environments
9. Has API documentation for customization & enhancement

Enhancements: In progress -

1. Integrate Audit and logging - Define Error codes, log process failures, Audit progress & runtime information