Introduction

The wrangler app is instrumental to ETL tasks.

• Extracts or streams data, of various formats, from any source; mainly using utils/etl/loads apache spark workloads
  • spark file workloads (e.g., csv,txt,pdf,json)
  • spark RDBMS workloads (e.g., postgres,mysql,etc)
  • spark NoSQL workloads unstructured data (mongoDB,couchDB,etc)
• Transforms the data, using utils/etl/transform into a format that makes domain and functional sense.
  • The data is extracted and stored in a cleansed and raw form.
  • raw data is further cleaned, transformed, cataloged, and historically achieved.
  • historic data is available for further curation and use for data mining (AI/ML), visual analytics, and datamart services.
• The ETL processes are, usually, automated with airflow using dag files.