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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