Modernizing Data Infrastructure with a Real-Time, Scalable ETL on AWS

Our client, a leading provider of digital loan solutions, sought to modernize its data infrastructure for better performance and agility. NeenOpal redesigned the cloud architecture with an AWS-native ETL pipeline, enabling scalable, near real-time data movement and robust transformation. This ensured enhanced data infrastructure and analytics capabilities while reducing operational overhead. The re-architecture facilitates infrastructure modernization and serves as a blueprint for secure, cost-efficient cloud data operation

Customer Challenges

Before the transformation, the client’s existing data infrastructure struggled to keep pace with their growth and analytical needs. Several legacy constraints created bottlenecks in performance, visibility, and scalability.

Fragmented Data Ingestion System

Legacy pipeline used multiple disconnected tools causing inefficiencies, duplication, and latency in ingesting and processing data.

High Operational Overhead

Manual monitoring and recovery added burden on teams; lacked automated remediation and observability.

Lack of Real-Time Visibility

Ingested data suffered from 15+ minute delays, restricting timely insights and actionability for downstream analytics.

Scalability Limitations

Adding or modifying data pipelines required engineering intervention, limiting business agility and extensibility.

Solutions

NeenOpal implemented a holistic, cloud-native ETL framework using AWS services including EC2, Glue,Redshift, and S3. The pipeline now supports near real-time fault-tolerant processing for 60+ tables. Config-driven transformation logic enables flexibility while simplifying ongoing operations. Historical and incremental data loads are optimized with SCD Type-2 implementation, ensuring audit readiness and data integrity at scale.

Cloud-Native Architecture Rebuild

We re-architected the ETL workflow using AWS-native services – EC2, Glue, Redshift, and S3 – supporting full AWS modernization. This enabled scalable, modular deployment and improved data infrastructure consistency.

Near Real-Time Data Ingestion

The redesigned pipelines now process data every 1–2 minutes, reducing latency drastically. Change data capture logic ensures accurate, real-time updates — an essential step in data modernization.

Modular Config-Driven Framework

Introduced per-table JSON configurations stored in S3 that define SQL logic, schedule, and flags. Enabled rapid onboarding of new tables with zero-code changes, empowering teams to scale ingestion independently.

Automated Monitoring & Recovery

Embedded error logging and CDC reconciliation logic with SNS alerting, Lambda-controlled stop/resume features, and CloudWatch logs. Ensures robust operations and quick self-healing of data flows with minimal human oversight.

Why choose NeenOpal?

NeenOpal combines deep data engineering expertise with agile delivery practices to build scalable, resilient cloud data platforms. Our consultative approach ensures tailored solutions that are both technically sound and business-aligned, enabling organizations to maximize value from their cloud investments quickly and reliably.

Conclusion

NeenOpal's reimagined platform empowers our client with faster decision-making, reduced costs, and long-term flexibility. By consolidating fragmented workflows into a robust cloud-native framework, we've not only solved current pain points but also laid the foundation for scalable data infrastructure and analytics. This case is a testament to what’s possible through targeted infrastructure modernization and agile execution.

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