Data analytics: How to improve existing infrastructure to accommodate live streaming data [case study]

Companies are turning to their data to create better customer experiences that drive additional revenue and increase satisfaction. As these new initiatives roll out across companies in the United States, many are facing challenges with their existing infrastructure.

In this use case, a Fortune 50 company sought to improve their existing infrastructure to accommodate live streaming data from their customers. Additionally, the company wanted to increase the speed and access of their data assets.

But moving to live data had numerous organizational impacts. The company identified two challenges they needed to overcome:

  • Existing query structure and timelines were slow. There were long lead times to get the right answer – this was too slow for the data and marketing teams.
  • Streaming data might overstress the internal resource management system. The original architectures were designed for completely correct answers with large datasets that couldn’t live on one computer. Consistency and partitioned data were achievable, but the customer was now demanding more availability (see CAP theorem).

The company contacted Shadow-Soft, an open source leader in emerging technologies, for help. Shadow-Soft was asked to review its existing software infrastructure, make recommendations, and perform a proof-of-concept on those recommendations.

Download this case study to learn:

  • Limitations with the client’s existing systems
  • Architecture requirements to overcome the challenges
  • Why we recommended a new container scheduler
  • How the new solution increased performance by 10x
Data analytics case study

 

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