Design and rollout of a scalable transaction data storage and processing platform

Overview
A robust backend platform to store, process, and analyze high volumes of transaction data in real time. Designed for scalability, resilience, and integrity, it powers reporting, analytics, and system integrations for business-critical operations.

Client

• Country: (confidential / US-based)

• Industry: Financial services / transaction processing

• Scale: Millions of transactions daily across multiple systems

• Type: Enterprise (Fortune 500)

• URL: Confidential

Challenge
“Our transaction data kept growing beyond the limits of our legacy systems. Reporting lagged by hours, queries timed out, and compliance teams lacked reliable audit trails. We needed a scalable, resilient backend that could ingest millions of events per day without bottlenecks.”

Solution

• Delivered a backend platform with:

  • Scalable Data Ingestion via Apache Kafka for high-frequency streams

  • Reliable Storage Layer combining PostgreSQL (transactions) and ClickHouse/BigQuery (analytics)

  • Stream Processing Pipelines using Apache Flink & dbt for real-time transformations and enrichment

  • Security & Compliance with encryption, RBAC, and audit logs

  • Integration-Ready APIs exposing curated datasets to BI, finance, and ML systems

• Backend & Data Tech: Kafka, Flink, dbt, PostgreSQL, ClickHouse/BigQuery, Python

• Infrastructure: Kubernetes, Terraform, AWS/GCP with encrypted S3 backups

• Team: 2 backend/data engineers, 1 DevOps, 1 project manager

Result

• Ingestion throughput: >50,000 transactions/second without degradation

• Reporting latency reduced from hours to seconds

• Regulatory audits completed 30% faster due to reliable logs

• Enabled real-time dashboards for finance and operations teams

• Provided integration endpoints for machine learning models and BI platforms

Additional Information

• Key numbers: 5B+ rows stored, 7 TB processed daily, 99.99% uptime

• Technologies: Kafka, Flink, dbt, PostgreSQL, ClickHouse, Kubernetes, Terraform, AWS/GCP

• Security: OAuth2, encryption at rest & in transit, full audit trail

Process

• Discovery → mapped transaction flows, compliance needs, and reporting SLAs

• Architecture → designed multi-tier storage and streaming pipelines

• Implementation → iterative rollouts with blue/green deployments on Kubernetes

• Validation → performance tests at 2× peak traffic; compliance validation with anonymized data

• Go-live → phased migration from legacy, followed by 24/7 monitoring and support

Client Testimonial

“With this new platform, we finally trust our data. Reports run in seconds, regulators are satisfied, and our systems scale as the business grows.”