Big Data & Analytics8 min read

End-to-End Big Data Applications: Use Cases, Architecture & Gains

Written by Sarah Kimani, Lead Data ArchitectPublished February 2026

Modern applications process millions of events per second. Traditional relational SQL databases fail under high-concurrency event ingestion, necessitating real-time streaming architectures.

1. Event Streaming with Apache Kafka: Kafka serves as the distributed message log handling millions of concurrent events. Decoupling data producers from processing pipelines ensures high system resilience.

2. Distributed Computing via Apache Spark & Flink: Real-time analytics platforms process streaming data using windowed aggregations, calculating metrics like fraud scoring or supply chain delays in sub-second intervals.

3. Data Lakehouse Architecture: Storing raw event logs in Delta Lake or Snowflake provides high-speed SQL analytical query capability for business intelligence dashboards.

End-to-End Big Data Applications: Use Cases, Architecture & Gains

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