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Data transformation with Python


An international beverage manufacturer sells its products in various markets.

The challenge

  • Objective: To analyze which products are sold in which market with which frequency and which products are no longer in stock
  • High manual effort to extract the relevant data
  • High susceptibility to errors due to manual processes


  • Transformation of the raw data using a Python script
  • Provision of the input and output files via an FTP server or provision of the application in a Docker container
  • Advantages of the Docker container: all data remains on the customer’s local computer (data protection); development environment is the same as the production environment, i.e. no problems such as incorrect Python versions or missing Python modules


  • Automatically generated and error-free output files for further analysis
  • Automated quality checks for timely detection of possible irregularities in the data
  • Error-free process due to elimination of manual work
  • Time savings thanks to the high processing speed of Python
  • Preservation of data protection due to Docker container