Industry: Healthcare Service

Service: Data Engineering

Technology: Amazon Web Services

Client Overview

US-based life-sciences company focused on medical innovations for cardiovascular disease, critical illness, and surgical monitoring. The company collaborates with the world’s leading clinicians and experimenters to address unmet healthcare requirements, working to ameliorate patient issues and enhance lives.

Business Need

  • To build a scalable, flexible, reliable solution that can streamline data processing to help them make informed business decisions
  • Reduce the manual and time consuming data processing effort which is leading to inconsistencies and errors in data
  • Unable to leverage the full potential of the data because of lack of tools and processes necessary for data integration
  • Decision-making ability is getting delayed which impacts overall organizational performance.

How Did We Help

  • CloudJournee’s experts conducted continuous discussions with client to understand and analyze the current data from multiple data sources with multiple formats
  • Analyzed the existing complexities of the client’s project at hand and proposed the solution leveraging AWS services
  • Built Multiple ETL pipelines to extract unstructured data from multiple data sources and cleaned and transformed the data using AWS Glue jobs. Each pipeline has a different data format and with different output format (Json, XML etc.,)
  • Built custom transformation AWS Glue pyspark jobs to transform the data and provided the required output and exposed the output as an API so that Salesforce cloud consume for further insights


  • Able to successfully reduce the time taken for data processing from 3 days to 1 hour per batch of data
  • Reduced the need for manual intervention and streamline data processing
  • Gained insights that may not have been able to obtain otherwise using manual data processing
  • Improved the quality of data by extracting it from various sources, standardizing, and eliminating errors that can occur when manually processing data