Data Engineering
DataFlow
Real-time financial transaction pipeline 4M+ events per day, sub-800ms end-to-end latency.
About the Project
What We Built
DataFlow is a high-throughput, fault-tolerant streaming data platform built for a leading fintech company processing over 4 million financial transactions daily. The system replaced a legacy nightly batch pipeline that caused 4-hour delays in fraud detection signals, missed regulatory reporting windows, and an inability to act on customer behaviour in real time. The new architecture delivers end-to-end latency under 800ms from event ingestion to warehouse availability.
The Problem We Solved
Challenge, Solution & Outcome
A legacy nightly batch pipeline processed all financial transactions in a single overnight job, producing fraud detection signals 4 hours after the fact, missing intraday regulatory reporting windows, and leaving the data team with no ability to act on real-time customer behaviour.
A cloud-native streaming architecture built on Kafka, Spark Structured Streaming, and Snowflake with dbt handling transformation logic and Airflow managing orchestration, SLA monitoring, and automated recovery delivering sub-second data freshness at fintech scale.
Fraud detection signals now arrive within 800ms of transaction initiation, cutting fraud losses by 22% in the first quarter post-launch. Regulatory reports are generated intraday without manual intervention, and the data team ships new analytical models in days rather than weeks.
Technology
Tech Stack Used
Capabilities
Key Features
A 24-partition Kafka cluster ingests 50,000+ events per second from payment gateways, ATMs, mobile wallets, and card networks with guaranteed exactly-once delivery semantics.
Apache Spark Structured Streaming applies multi-stage enrichment, PII tokenisation, schema validation, and deduplication within 400ms feeding the fraud model before transactions fully settle.
Validated events land in Snowflake through micro-batch COPY commands. dbt models apply business logic across 60+ transformation layers with full column-level lineage tracked in the data catalogue.
DAGs manage scheduled aggregations, regulatory report generation, SLA enforcement, and automated backfill operations. PagerDuty integration triggers on-call alerts within 2 minutes of any degradation.
Great Expectations data quality checks run on every micro-batch. Monte Carlo monitors freshness, volume, and schema drift across 400+ tables catching anomalies before they reach downstream consumers.
Streaming aggregations compute velocity features, rolling averages, and behavioural baselines that the fraud ML model consumes within 800ms of transaction initiation cutting false negatives by 34%.