Case Study

Data Engineering & Analytics

Data Lake for FinTech Firm Using AWS

Data Lake was created by using proprietary frameworks and tools provided by AWS to minimize the time of Data Lake implementation and cost associated with it.

Client Introduction

A leading FinTech company had a requirement to retrieve insights in seconds to automate business decision making, leveraging & democratizing AI & ML for the organization.

Technologies Used By NutaNXT

  • Big Data
  • Data Analytics
  • Amazon Web Services
  • Data Lake

Use Case Applicable To Verticals

  • Multiple Industries

Data Processing Time

10 Hours



Data Analytics and Reporting time

4 Hours


The traditional on-premise data storage centered around an Enterprise Data Warehouse architecture was incompatible with limited data formats only. 

Key challenges with the existing model were:

  • Most of the unstructured data resided in silos incurring significant high costs and posed a challenge for real-time analytics implementation. 
  • Loss of insightful data had a direct impact on business performance.

Team we built

  • 2
  • Data Engineers
  • 1
  • Cloud Architect
  • 1
  • Data Analyst
  • 1
  • DevOps Engineer

Our Solution

We created a Data Lake to move data from the on-premise data center to the cloud, with functionality to ingest real-time data generated by various consumer-facing solutions, sensors, devices etc. into the Data Lake.

  • We developed various solutions like Customer 360, Big Data Processing, real-time analytics for business consumers, and an analytics dashboard based on third-party tools such as Tableau for the organization to consume at every point of decision making.

Technology Stack Used

Data Processing Time

Reduced To

4 Hours



Data Analytics & Reporting Time

Reduced To

1.5 Hours

Business Impact and Results

The enterprise data lake provided high availability, scalability, security and flexibility for more enterprise adoption, reducing the cost of managing data and helped clients achieve the agility of data-driven decisions. 

  • 20 times increase in power and capability of analytics models
  • 60% reduction in data processing time
  • 80% reduction in data analyzing and reporting time

More Works

From MVP to end-to-end digital products, our culture of intelligent innovation and engineering excellence have solved complex Digital Product Engineering challenges and delivered maximum business ROI for our clients.