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SaaS Engineering FinTech / Enterprise
Autonomous FinTech Analytics
Architecting a real-time financial data pipeline with sub-second latency using ClickHouse and Python.
The Outcome
Query latency reduced by 94% (from 500ms to 30ms). System now handles 50k events/second with zero downtime.
Tech Stack
Python ClickHouse Kafka React Docker
Technical Bottlenecks
The client, a high-frequency trading firm, faced 500ms+ latency in their existing SQL-based analytics, causing missed arbitrage opportunities. Data fragmentation across 4 legacy ERPs made real-time reconciliation impossible.
The Solution Design
High-level System Design
We replaced the relational core with a columnar ClickHouse cluster, ingesting event streams via Apache Kafka. A Python-based aggregation layer was built to normalize data in-flight.
Engineering Rigor
- Implemented automated CI/CD pipelines ensuring 99.9% uptime.
- Comprehensive unit and integration testing coverage using standard libraries.
- Full documentation of API endpoints and system dependencies.