FinTech & Real-Time AnalyticsSingapore & UK

ApexMetrics Financial

Constructed a high-throughput event streaming pipeline and Snowflake Lakehouse processing 50M+ daily financial telemetry events.

42 ms

Data Latency

50M+

Daily Event Volume

38%

Infra Cost Savings

99.99%

Pipeline Uptime

The Challenge & Context

ApexMetrics relied on batch nightly database dumps to populate investor dashboards. As transaction volume grew, processing delays stretched to 6+ hours, leaving portfolio managers and risk analysts looking at stale data during volatile market hours.

System Architecture & Design

Distributed Apache Kafka cluster capturing transaction event streams, Apache Spark streaming workers for real-time validation and deduplication, Snowflake Cloud Data Warehouse with dbt data modeling, and ClickHouse for sub-100ms dashboard aggregations.

Technologies Utilized:

PythonApache KafkaApache SparkSnowflakedbtClickHouseAWS ECSTerraform

Core Deliverables & Implementations

Fault-tolerant Apache Kafka ingestion engine with automated schema registry checks

Stream processing worker cluster handling over 50,000 events/second during peak volume

Snowflake Lakehouse architecture separating raw landing, bronze staging, and gold analytics marts

Automated dbt transformation pipelines with integrated schema and data quality assertions

Live analytics API serving sub-second metric queries to internal and customer portals

Measurable Business Impact

Reduced query latency from 6 hours to 42 milliseconds with 99.99% pipeline reliability.

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