About the Client

The client is a major Indian non-banking financial company (NBFC). It offers lending products such as home loans, loans against property, and business loans, and was looking to modernize how it manages and processes data across the organization.

Business Need

The client needed a cloud data platform that could bring together information from multiple systems, including PostgreSQL, Salesforce, and various APIs, into one place. The platform had to be scalable, secure, and automated, so the business could get faster insights, meet compliance requirements, and cut down the effort spent running data operations manually.

Business Challenges

Building this platform meant solving two problems.

The existing data pipelines were slow and did not scale well. As data volumes grew and more sources were added, processing times increased, and teams struggled to keep pace with business demand.

The legacy, on-premise setup also added cost and manual effort. Infrastructure had to be maintained by hand, which limited flexibility and slowed down new development.

Business Solution

NuSummit designed and built an end-to-end cloud-native data platform on AWS and Snowflake.

The platform used AWS Glue for automated data extraction and transformation, Amazon S3 for storage, Amazon RDS for structured data and audit logging, and Snowflake as the core data warehouse. Pipelines were built for high availability and scale, using Glue auto-scaling and Snowflake’s elastic compute to handle changing workloads without manual intervention.

Governance and security were built into the platform from the start. Multi-factor authentication, IAM roles, private VPCs, and encryption across S3, RDS, and Snowflake protected data at every stage.

The team also set up automation and monitoring through infrastructure-as-code deployments, CI/CD pipelines, CloudWatch dashboards, and audit tables in RDS. This gave the business visibility into pipeline health and reduced the need for manual checks.

ETL performance was tuned using partitioning, Parquet and Snappy file formats, and efficient schema design, helping the platform consistently meet its SLA targets.

Technology Used

The solution was built using:

• Snowflake as the core data warehouse, providing elastic compute for varying workloads.
• AWS Glue for automated ETL and pipeline orchestration.
• Amazon S3 for scalable, secure data storage.
• Amazon RDS for structured data management and audit logging.

Project Differentiator

The project went beyond replacing an on-premises system with a cloud one. Security and governance were built into the platform’s foundation rather than added later, and monitoring was automated so issues could be caught before they affected the business.

This gave the client a data platform that was not just faster, but also easier to manage, monitor, and extend. It also created a foundation the business can build on for future analytics and AI/ML work, rather than a platform that would need to be rebuilt again in a few years.

Business Impact

The new platform reduced ETL latency from 4 hours to under 1 hour, helping the team meet SLA targets consistently.

Automated reporting and monitoring reduced 40 to 50 hours of manual work every month, freeing up time for higher value tasks.

Security and compliance also improved, with multi-factor authentication, encryption across S3, RDS, and Snowflake, and IAM best practices built into day-to-day operations.

The platform was designed for high availability, using multi-AZ RDS, auto-scaling Glue, and Snowflake’s elastic architecture, so it can handle load spikes without downtime.

It also gives the client a strong base for advanced analytics and AI/ML workloads on Snowflake going forward.

Outcomes

The client moved from a manual, on-premises data setup to a cloud-native platform that is faster, more secure, and easier to scale. Data from PostgreSQL, Salesforce, and multiple APIs now flows through automated pipelines instead of manual processes.

The result is quicker access to reliable data, stronger governance, lower operational effort, and a platform ready to support the business’s next set of analytics and AI initiatives.