How a New-Generation Indian Bank built a Cloud Data Lake for Faster Insights

Learn how a leading new-generation Indian private sector bank partnered with Nihilent to modernize its data platform on cloud SQL data warehouse as a service and deliver faster, governed access to trusted data for business users and advanced analytics teams.

Service

Data & Analytics, Cloud Data Platforms, and Modern Data Warehouse

Vertical

BFSI (Retail and Corporate Banking)

Region

India (Nationwide Branch and Digital Network)

Tech Stack

Azure-based SQL DWaaS, Metadata-Driven ETL Framework, BI Semantic Layer, Power BI, SQL Server Reporting Services

How a New-Generation Indian Bank built a Cloud Data Lake for Faster Insights

Client Overview

The client is a fast-growing Indian private sector bank that offers consumer and corporate banking, cards, loans, and digital services across a large branch and ATM network. With millions of customers and dozens of core and surround systems, the bank relies heavily on data to support regulatory reporting, risk management, customer analytics, and digital experiences.

As data volumes and use cases grew, existing platforms struggled with performance, consistency, and timely availability of data for business users. The bank was looking for a partner to help define an overall data strategy and to implement a modern cloud data platform that could scale with its digital and analytics ambitions.

Business Challenges

The bank wanted to enable faster, data-driven decisions but faced several interconnected challenges.

Difficulty Managing Growing Data Volumes

Existing platforms were not designed for current data scale, leading to ongoing performance issues and operational bottlenecks.

Limited Availability of Useful Data for Business Users

Business and analytics teams often lacked timely access to trusted data for reporting, insights, and regulatory needs.

Need for Clear Data Strategy and Transformation Roadmap

The client needed guidance to define a data strategy, maturity path, and initiatives to modernize the data estate on cloud.

How Nihilent Addressed This

Nihilent adopted a consulting-led approach that combined strategy, architecture, and implementation of a modern data platform on cloud SQL DWaaS, with strong focus on governance and regulatory compliance.

Data Maturity Assessment and Strategy Definition

Nihilent performed a data maturity assessment, reviewing how data was captured, stored, processed, and consumed across systems and reports, then formulated a data strategy aligned with regulatory, risk, and business requirements.

Modern Cloud Data Warehouse and ETL Framework

The solution reconciled existing data sources and warehouses and designed a new enterprise data warehouse on cloud SQL DWaaS, including functional consulting, data modeling, data pipelines, EDW setup, and reporting visualization. A metadata-based ETL framework was developed to minimize effort in loading staging environments while improving flexibility, scalability, and governance.

BI Semantic Layer and Self-Service Reporting

Nihilent implemented a BI semantic layer with predefined aggregates to support ad hoc analysis and faster query performance and used Power BI and SQL Server Reporting Services to deliver dashboards and reports to business users.

Governance, Testing, and Data Verification

With governance and ETL frameworks in place, Nihilent led integration and system testing of the new EDW model on SQL DWaaS, enabling robust data verification and reconciliation so that business and advanced analytics teams could access accurate data at their fingertips.

Key Results & Outcomes

Modern Cloud Data Warehouse and Data Lake

A new cloud-based EDW and data lake architecture now provides a scalable foundation for analytics and reporting while consolidating existing warehouses.

Faster And Easier Access to Data

Business users and analytics teams can access curated, trusted data much faster, improving responsiveness for reporting, regulatory submissions, and insight generation.

Efficient Management of Large-Scale Data

The platform manages around 5 TB of data with 8-10 GB of incremental data ingested daily, supporting sustained growth in data volume.

Broad Coverage Across Customers and Systems

The solution supports data for approximately 5 million customers across more than 45 data sources and feeds over 150 reports used by more than 500 users.

Improved Governance and Scalability

The metadata-driven ETL framework, BI semantic layer, and governed EDW model provide a scalable, governed environment ready for expanded analytics and AI use cases.

Key Takeaways

For a new-generation Indian bank aspiring to be digital-first, modernizing its data foundation on cloud was critical to delivering faster insights, better compliance, and richer customer analytics. By combining data maturity assessment, a clear data strategy, and implementation of a metadata-driven SQL DWaaS platform with robust governance and BI capabilities, Nihilent helped the bank move from fragmented, slow data processes to a modern, scalable data environment that serves both business and advanced analytics teams.

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