Overview
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.
80%
Faster Data Pipeline Execution Time
~50%
Operational Cost Saving
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.
Real-Time ETL & AWS Data Modernization Architecture
A scalable AWS data pipeline framework that enables real-time data ingestion, processing, and analytics to support faster insights and modernized data workflows.
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.
01.
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.
02.
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.
03.
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.
04.
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.
Our Consultative Approach Ensures Tailored Solutions that are Both Technically Sound and Business-Aligned.
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Benefits
Real-Time & Reliable Data
Our client’s modern ETL system ensures consistent and near-instantaneous data access, a key win in their data modernization journey.
Operational Efficiency and Resilience
Automated monitoring and recovery reduce manual effort, minimize errors, and enhance reliability. Teams experience lower operational overhead and improved system uptime.
Scalable and Flexible Architecture
The framework allows seamless onboarding of new data sources without rewriting code or causing downtime. This supports business agility and simplifies future scaling.
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.
FAQ
Find quick answers about how we modernized data infrastructure with real-time, scalable ETL on AWS.
Why was data modernization necessary?
The legacy infrastructure limited scalability, delayed insights, and increased maintenance overhead.
How did the new ETL architecture improve performance?
The AWS-based framework enabled real-time data ingestion, automated processing, and scalable resource management.
How did this impact business operations?
The modernized infrastructure improved data reliability, accelerated decision-making, and supported future growth.
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