Data Migration: Modernising legacy fintech with cloud to cut downtime, boost analytics, and speed up delivery
Project Overview
This project focused on modernising legacy infrastructure for a fintech company facing scalability and performance challenges due to increasing data volumes. The goal was to migrate outdated systems to a modern cloud-based architecture using Snowflake, separating transactional and analytical workloads to unlock faster insights and greater operational efficiency. The transition also laid the groundwork for future product development and faster time-to-market across key business lines.
Problem Statement
The OLTP database dual role as a data warehouse caused performance issues, slowing analytics. Its highly normalized Informix structure added inefficiencies. The project involved migrating analytical processes to the cloud with Snowflake for improved performance.
Key Findings
- Legacy System Limitations: The OLTP database, originally designed for real-time transactions, was also being used for analytics—causing performance degradation and slow decision-making.
- Complex Database Architecture: The Informix system’s high normalization made it inefficient for analytical queries, slowing down data processing and reporting across the organisation.
- Cloud-Driven Scalability Needs: A modern cloud solution like Snowflake was essential to support long-term growth, enabling elastic compute power and cost-effective data storage.
Implemented Solution
To address these challenges, we implemented the following solutions:
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Database Separation:
A dedicated OLAP data warehouse was created to decouple analytical tasks from transactional operations, significantly improving system performance.
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Data Migration to Greenplum:
Leveraged Java and Talend to migrate and sync large volumes of data in real time from OLTP to Greenplum, enhancing accessibility for analytics teams.
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Cloud Migration to Snowflake:
Set up a Snowflake VPS and used Talend for batch migration of structured data. Scheduled automation ensured smooth and consistent syncing to Snowflake, unlocking scalable, high-speed analytics.
Results
The successful migration to Snowflake resulted in an 84% improvement in analytical processing speeds and a 32% reduction in downtime caused by legacy application failures. This infrastructure upgrade enabled the rapid development and deployment of three new fintech products, each delivered faster and with greater reliability than before. By modernising its data stack, the company not only resolved immediate performance issues but also established a scalable foundation for innovation, revenue growth, and long-term technical resilience.