FINTECH

Real-time Analytics Platform

Building a high-throughput transaction monitoring system processing 50K+ transactions per second with sub-100ms latency.

6 months4 engineers8 min read

Key Results

System Uptime99.99%

Cost Reduction40%

Response Time<100ms

Throughput50K+ TPS

Client

Series B Fintech Startup

Industry

Financial Technology

Location

San Francisco, USA

Overview

A Series B fintech startup needed to process and analyze financial transactions in real-time to provide instant insights to their customers. Their existing batch-processing system couldn’t keep up with the growing volume of transactions, leading to delayed insights and missed opportunities for their users.

We designed and built a high-throughput, real-time analytics platform capable of processing over 50,000 transactions per second with sub-100ms end-to-end latency. The system needed to be highly available, cost-efficient, and compliant with financial industry regulations.

The Challenge

Performance Requirements

Process 50K+ transactions per second with sub-100ms P95 latency while maintaining data accuracy and consistency across all analytics pipelines.

Reliability & Compliance

Ensure 99.99% uptime SLA while meeting SOC 2 Type II and financial industry regulatory requirements for data handling and auditability.

Scalability

Design a system that can horizontally scale to handle 10x traffic spikes during market events without degradation in latency or data freshness.

Cost Optimization

Reduce infrastructure costs by 40% compared to the previous batch-processing system while significantly improving performance and capabilities.

Our Solution

Architecture Overview

Ingestion Layer

Kafka + Golang

Processing Layer

Distributed Golang Services

Storage Layer

TimescaleDB + Redis

High-Throughput Ingestion Pipeline

Built a multi-partition Kafka ingestion layer with custom Golang consumers capable of processing 50K+ events per second. Implemented back-pressure mechanisms and dead-letter queues for fault tolerance.

Apache KafkaGolangProtocol Buffers

Time-Series Database Optimization

Deployed TimescaleDB with hypertables optimized for financial time-series data. Implemented continuous aggregates and data retention policies to maintain query performance as data volumes grew.

TimescaleDBPostgreSQLData Compression

Distributed Caching Layer

Designed a Redis Cluster caching strategy with intelligent cache invalidation. Hot data is served from cache with sub-10ms latency, reducing database load by 70%.

Redis ClusterIn-Memory CacheCache Warming

Kubernetes Orchestration

Deployed on Kubernetes with custom HPA policies based on Kafka consumer lag. Implemented rolling deployments with zero-downtime migrations and automated canary releases.

KubernetesHPAService Mesh

Performance Metrics

Transaction Throughput

Response Time Distribution

50K+

Transactions/sec

<100ms

P95 Latency

99.99%

Uptime

40%

Cost Reduction

Technology Stack

Backend & Processing

Golang 1.21
Apache Kafka
Protocol Buffers

Data Storage

TimescaleDB
Redis Cluster
PostgreSQL 15

Infrastructure

Kubernetes
AWS
Prometheus + Grafana

Outcomes & Impact

Business Impact

Enabled real-time financial insights for 10,000+ end users
Reduced time-to-insight from hours to milliseconds
Supported the client’s Series C fundraise with proven scalability

Technical Achievements

Achieved 99.99% uptime over 12 months of operation
Sub-100ms P95 latency at 50K+ TPS sustained throughput
Zero data loss with exactly-once processing semantics

Cost Optimization

40% reduction in infrastructure costs vs. previous system
Auto-scaling reduced off-peak compute costs by 60%
Eliminated need for expensive third-party analytics tools

Security & Compliance

Achieved SOC 2 Type II certification
All data encrypted at rest and in transit
Full audit trail for regulatory compliance

“BeluMind transformed our analytics infrastructure. What used to take hours now happens in milliseconds. Their team’s deep expertise in distributed systems and financial technology made them the perfect partner for this critical project.”