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Apache Spark at Scale

How Netflix and Uber process petabytes of data with Spark

Why These Case Studies Matter

Apache Spark has revolutionized big data processing, enabling companies to analyze petabytes of data in hours instead of days. These case studies reveal how Netflix and Uber built production Spark platforms that power critical business decisions.

You'll learn the architectural patterns for both batch processing (Netflix recommendations) and stream processing (Uber real-time pricing). More importantly, you'll discover the performance optimizations, cost strategies, and lessons learned that only come from running Spark at massive scale.

Learning Path: After reading these case studies, build your own Spark pipeline with the ShopStream Spark Project, then follow the step-by-step walkthrough.

Note on Metrics: These case studies are based on publicly available information from engineering blogs, conference talks, and open-source documentation. While we've verified core architectural patterns and technologies, some specific numbers (especially cost figures and exact scale metrics) are estimates for educational purposes. Where possible, we've updated unverified claims to reflect documented information or general ranges.

Featured Case Studies

Deep dives into batch and streaming Spark architectures at Netflix and Uber

Netflix

Case Study #1

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The Problem

Processing 500+ billion events daily for personalized recommendations, A/B testing, and content analytics. Hadoop MapReduce was too slow (hours) for iterative algorithms needed for recommendation models.

Scale

Events/Day
500+ billion
Data Warehouse
60+ PB
Spark Jobs
100,000+/day
Spark Clusters
Thousands active
EC2 Instances
10,000+ peak
ML Models
Thousands trained daily
Click "Read More" to see the full solution, impact metrics, and key takeaways

Uber

Case Study #2

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The Problem

Real-time analytics for driver surge pricing, trip matching, and fraud detection across 10,000+ cities worldwide. Required processing streaming data with complex joins and sub-second latency while handling 15 million trips/day.

Scale

Trips/Day
15 million
Events/Second
1 million+
Data Ingested
100 TB/day
Spark Clusters
500+
Streaming Jobs
2,000+
Infrastructure
HDFS + GCP
Click "Read More" to see the full solution, impact metrics, and key takeaways
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