Scaling Fintech Infrastructure: 70% Cost Reduction with MySQL to Aurora Migration

Executive Summary

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High-growth fintechs reach a predictable inflection point: transaction scale outpaces legacy database architecture.

Decentro had accumulated 27TB of transactional and historical data on Amazon RDS MySQL. Performance remained stable, but cost efficiency, operational overhead, and scalability elasticity were deteriorating.

Shellkode redesigned the data architecture, tiered workloads intelligently, migrated active data to Amazon Aurora MySQL, and offloaded cold data to Amazon S3.

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Migrated from MySQL on Amazon RDS to Amazon Aurora with near-zero downtime and zero data loss.

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Outcome:

• 70% reduction in database spend
• 75% reduction in active data footprint
• 3x faster query performance
• 60–70% reduction in database operations effort
• Scalable architecture aligned to fintech-grade growth

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‍About Decentro

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Decentro is a fintech infrastructure platform enabling enterprises to embed payments, KYC, and financial workflows via modular APIs.

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• 1,600+ enterprise customers
• Tens of thousands of crores in annual transaction value
• Enterprise-grade fintech infrastructure across India and global markets
• Recently achieved profitability while scaling infrastructure

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Decentro operates in a trust-sensitive domain where latency, reconciliation accuracy, and uptime directly impact revenue and compliance.

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The Inflection Point

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As transaction volumes increased, so did data retention requirements.

The environment:

• 27TB across Amazon RDS MySQL
• 350+ tables in one primary database
• 14 high-growth tables driving the majority of expansion
• Applications relying primarily on the most recent 6 months of data

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The architecture was reliable, but inefficient at this scale.

Operational friction began increasing:

• Backup windows expanding
• Maintenance complexity rising
• Storage costs compounding
• Scaling requiring disproportionate provisioning

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As data scaled, infrastructure costs increased non-linearly—driven by rising EBS storage costs, snapshot accumulation, and inter-region data transfer overhead.

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Why Costs Were Increasing

• EBS storage costs growing with continuous data accumulation
• Snapshot storage increasing due to backup retention policies
• Inter-region data transfer costs adding to overall infrastructure spend

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For a fintech platform, this trajectory becomes structurally unsustainable.

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The mandate was clear:
Improve performance. Reduce cost. Preserve compliance. Eliminate operational drag.

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Shellkode’s Architecture Strategy

Shellkode did not recommend a lift-and-shift.

The intervention began with workload pattern analysis.

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Step 1 — Data Intelligence

• Identified high-frequency vs. archival datasets
• Mapped transactional criticality
• Quantified access patterns
• Designed lifecycle tiers

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Insight:
Only recent transactional data required low-latency access. Historical data could be retained at a lower cost without affecting live systems.

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Step 2 — Tiered Data Architecture

Shellkode implemented a data tiering architecture separating hot and cold data:

Active Tier:

• Migrated critical live datasets to Amazon Aurora
• Leveraged Aurora’s distributed, high-throughput architecture

Archive Tier:

• Offloaded historical data to Amazon S3
• Enabled long-term data archival while preserving auditability and compliance integrity

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This reduced the active database from 27TB to ~7TB.

Performance was improved not by scaling hardware, but by removing structural inefficiency.

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Step 3 — Zero-Disruption Migration

Execution discipline defined the outcome.

• Bulk load via MySQL Shell Utility
• Continuous replication between RDS and Aurora
• Validation cycles before cutover
• Phased migration to eliminate risk

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Achieved near-zero downtime migration with zero data loss through continuous replication and phased cutover.

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• No downtime exposure.
• No transactional loss.
• No compliance compromise.

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Quantified Business Impact

• 70% Lower Database Spend : Right-sized compute and storage aligned to actual usage instead of historical accumulation.

• 75% Smaller Active Dataset : From 27TB → 7TB.
  Lean, performance-optimized core.

• 3x Faster Query Performance : Reduced dataset density improved query planning and API responsiveness.

• 60–70% Less Operational Effort : Shorter backup windows, Simplified maintenance cycles and Cleaner scaling model

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Scalable for Growth

Architecture now supports transaction growth without proportional cost expansion.

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Closing Note

This architecture demonstrates practical implementation of data-tiering, data-archival, and MySQL-to-Aurora migration strategies for high-growth fintech systems.