Healthcare Case Study


The healthcare industry has constantly faced remarkable  changes for decades. There have been a number of factors which  have been responsible for this rapid change. These factors include payment models which are extremely value-based, initiatives taken by the government to promote access to excellent care, better  involvement of consumers in  purchasing decisions of  healthcare and above all the digitization of healthcare information. Introduction to such a change has resulted in  huge data, mounting  results from decision makers for self analysis and the self opinion of the patient. This in return excites the IT organizations of so many insurers and providers.

Sparkflows supports healthcare companies by  improving their speed of decision making in the form of data analysts and similar line-of-business users who assist in the  ability to prepare, assemble, and analyze data independently, without having the hassle of any coding, any delay or any IT dependency.

Organizations across many industries are using data from many different sources. Performing Analytics and ML across them provides great Business Benefits.

However, very few users are enabled to make use of the Big Data Platforms. Data is not accessible, tools are too complex to use and achieving the end to end from Data Cleaning, Data Analytics, Feature Generation to building and analyzing ML models on these Datasets becomes very hard. This leads to frustration and failure of the Use Cases we set about to solve.

Problem Statement/ Challenges

They want to keep costs in control, and at the same time provide better services. They want to ensure to keep churn under control, better predict their customers diseases and pro-actively handle them.


Data is core to their business and they have lots and lots of it. Their main challenge has been to provide deep processing and analytics capabilities to the variety of business units within the organization. The complex big data and machine learning technology stack made it very slow for the businesses and IT to move.


They adopted Sparkflows across their organization. They deployed it in the cloud on Azure and on-premise on Cloudera and Hortonworks.


They build out 50+ new processors of their own on Sparkflows. Combined with 250+ provided by Sparkflows, they now have a library of 300+ processors to choose from.


They are seamlessly able to build and deploy both streaming and batch jobs.


Sparkflows provided next generation big data processing capability. It provided analytics and machine learning across the very complex technology stack. Business and IT are now able to build out their use cases seamlessly using the self-serve capabilities of Sparkflows.


Thousands of workflows have been built and deployed. Multiple business units have adopted Sparkflows and are finding deep success.

Summary/ Conclusion

The organization is now rolling along with big data and analytics. Business units are being seamlessly on-boarded.

The use cases are being built in a predictable fashion using the next generation of self-serve and reusability of complex components simplifying the overall process.

20X more users are now able to extract value from big data, build complex use cases and derive deep analytics.









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