Streaming supremacy
DAZN Group — Media & Entertainment — Global Sports OTT Streaming
DAZN Group stands as one of the world's most ambitious and rapidly scaling sports OTT streaming platforms. Operating across multiple continents and delivering thousands of live sporting events annually to millions of concurrent viewers, DAZN's business generates an extraordinary volume of high-velocity, high-complexity data — every second, from every stream, across every market.
Massive business growth was beginning to outpace the legacy data architecture originally designed for a far smaller operational footprint. The foundational data infrastructure — built piecemeal during earlier growth phases — was straining under the weight of petabytes of complex, fast-moving streaming data. Leadership recognised that sustaining global competitiveness demanded a radical overhaul of the entire data ecosystem.
The pain behind the numbers.
DAZN's legacy data infrastructure relied heavily on batch processing, creating bottlenecks that made it impossible for data scientists to access clean, real-time datasets without extensive manual preparation. Leadership could not make real-time decisions during live broadcasts — a critical disadvantage in an industry where milliseconds shape viewer retention, advertising yield, and content investment strategy.
The platform was processing petabytes of streaming telemetry per second — viewer engagement signals, quality-of-service metrics, content performance indicators, ad delivery confirmations — yet the analytical layer lagged behind by hours. Traditional modernisation approaches projected multi-year build cycles, threatening to delay critical capabilities while competitors advanced.
The cost of inaction was quantifiable: suboptimal ad placement, delayed content decisions, and an inability to personalise the viewing experience at scale — all directly impacting revenue and subscriber retention.
The solution in practice.
Lognormal — foundational re-architecture
Restructured legacy pipelines into decoupled, scalable cloud architecture. Established rigorous data governance frameworks and standardised ingestion protocols across all streaming markets, creating a unified data foundation from previously siloed systems.
FalconDive — real-time streaming analytics
Deployed pre-configured, scalable analytics environments for data science workloads. Integrated streaming data capture from millions of concurrent streams into query-ready datasets with sub-second latency — enabling live broadcast decision-making for the first time.
Intelligent resource allocation
Implemented automated, intelligent cloud resource allocation that dynamically scales with viewership patterns — eliminating manual capacity planning and significantly reducing cloud OPEX during off-peak periods.
Executive decision cockpit
Built real-time executive dashboards enabling dynamic ad optimisation, viewer retention interventions, and content investment decisions during live broadcasts — transforming leadership from reactive observers to proactive operators.
Bottom-line results.
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