Cutting Integration Costs by 62%, Migrating Java + Mulesoft Pipelines to AWS

— Delivered by Infoslab’s Enterprise Data Engineering & Cloud Modernization Squad

Project Overview

A large multinational financial services company relied on a legacy Java + Mulesoft architecture for its data integrations across core business functions — Risk, Finance, Customer Experience, and Compliance. The system had grown rigid and costly, with over 60 interconnected microservices operating under high maintenance, licensing, and latency overheads. As digital product adoption grew, their existing infrastructure could no longer support real-time analytics or agile iteration cycles.

Industry

Finance

Challenge

The client's entire integration layer — built over years using tightly coupled Java services and Mulesoft APIs — was bloated, expensive, and operationally brittle. Specific pain points included:

Cost Inefficiency: Over $1.5M annually spent on Mulesoft licensing, on-prem compute, and support.
Low Agility: Each new data pipeline took 3–4 weeks to develop and deploy.
Latency & Downtime: Data synchronization across systems ran on 6–8 hour batch cycles with frequent failures.
Scaling Bottlenecks: JVM tuning and service-level upgrades required extensive manual intervention.
Talent Gaps: Java + Mulesoft engineering expertise was hard to find, delaying fixes and improvements.

Infoslab’s Data Engineering-Led Solution

Infoslab deployed a 12-member Data Engineering and Cloud Migration Squad with deep AWS expertise. The squad worked in agile sprints to dismantle and rebuild the client’s ETL landscape for scalability, observability, and cost efficiency.

Business Impact & Cost Savings

KPI Before Infoslab After Infoslab Impact Annual Integration Cost ~$1.52M (Java + Mulesoft) ~$580K (AWS-native) –62% ($940K saved) Pipeline Deployment Time 3–4 weeks per pipeline <1 week (avg) –75% faster delivery Batch Latency 6–8 hours 15–45 minutes –80% latency reduction Pipeline Failure Rate 12+ failures/week Near-zero –95% operational noise Dev Velocity 2–3 releases/month Weekly cycles 4× faster iteration Scalability Manual JVM tuning Auto-scaled Elastic & maintenance-free

Why Infoslab?

Top-Tier Data Engineering Talent: 12+ experts deployed in <2 weeks with AWS, Glue, PySpark, Terraform, and DevOps fluency.
Cost-Efficient Modernization: Migrated entire ETL infrastructure without disruption — cutting costs by 62%.
End-to-End Ownership: From architecture to delivery and knowledge transfer, Infoslab owned the full modernization lifecycle.
Future-Ready Platform: Enabled downstream AI, real-time analytics, and scalable data product delivery.

Get in Touch with Infoslab

Contact us

Key Engineering Highlights:

Pipeline Migration: Over 60 Java-Mulesoft pipelines were re-engineered into modular, serverless PySpark workflows using AWS Glue.
Orchestration Layer: Introduced AWS Step Functions and EventBridge for resilient, event-driven execution.
Modern Data Lake: Built an S3-backed lakehouse using Glue Catalog and Athena, enabling real-time querying and storage tiering.
AI Enablement: Streamlined ML pipeline handoffs using standardized feature stores feeding into SageMaker endpoints.
Security & Governance: Enforced IAM role boundaries, encryption at rest & in transit, and integrated CloudWatch logs for full auditability.
CI/CD & Infra as Code: All deployments containerized via Docker, provisioned using Terraform, and pushed through a managed CI/CD pipeline.