CHALLENGE 

A leading global media measurement firm faced a data explosion, threatening their ability to process information from 40,000 households.  

Their on-premises solution was choked, putting their business, finances, and reputation at risk.  

They needed a scalable, cloud-based solution capable of handling petabytes of data instantly. 
 

APPROACH 

We delivered a custom cloud-first solution built on: 

Scalability: Separate Kubernetes clusters for presentation, backend, and HPA ensured stability and handled massive data inflows. 

Performance: A powerful Spark cluster of 100s of nodes processed petabytes daily, scaling instantly to meet demand. 

Efficiency: Throttling at user and client levels prevented resource contention, while granular logging and monitoring enabled easy management. 

Flexibility: ML-driven resource allocation optimised costs, and the admin console allowed customisation. 
 

AGILE TEAMWORK 

We employed a global, cross-functional team using: 

Architecture-driven approach: A clear vision guided development. 

Distributed Scrums: Teams in India, Poland, and the US worked collaboratively. 

Parallel development: Components were built and tested simultaneously. 

Scrum of Scrums: Regular syncs ensured synergy and alignment. 

Show and tells: Functional demos facilitated course correction and delivery precision. 
 

OUTCOME 

Within 9 months, we delivered a framework capable of processing data from 230 million households, capturing every remote control click. This resulted in: 

Business continuity: The new solution mitigated risks and ensured business survival. 

Massive data growth: We increased data volume by 575%, enabling deeper insights. 

Cost optimisation: ML-driven resource allocation saved costs. 

Successful handover: The framework and reports were transitioned smoothly to the client's team.
 


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