Data EngineeringVER_4.02
Streaming Analytics Platform
PythonApache KafkaApache SparkClickHouseApache Airflow
System Architecture
Logic Flow & Infrastructure Analysis
01_The_Problem
Tightly coupled ingestion and analytics workloads caused query latency spikes and made backfills unsafe.
02_Architecture_Solution
Kafka Streams and Spark Structured Streaming handled ingestion with replay and backpressure control. Events landed into a clustered ClickHouse deployment sized for columnar analytics over billions of rows, with materialized views powering downstream dashboards and ad-hoc exploration.
Execution Metrics
Throughput / Volume
Billions of events per day
Performance / Latency
Sub-second analytical queries