fintech analytics in AWS

FinTech platform provider transforms raw data into actionable insights with AWS Glue, S3, and Quicksight

Industry

Financial Services

Teams & Services

Data Engineering / ETL Development / Cloud Governance / Data Visualization

Tech & Tools

AWS Glue / AWS Lambda / AWS Lake Formation / Amazon CloudWatch / Amazon S3 / Amazon Quicksight / MongoDB / Delta Tables / Parquet

Key Data Points

Delivered a fully functional and governed Data Lake solution in 4 weeks
Enabled secure, role-based access using AWS Lake Formation and IAM policies
Built a suite of custom dashboards in Quicksight for actionable insights

The Vision

Client envisioned a centralized and scalable data solution to unlock the potential of their operational data. By integrating data from MongoDB and CloudWatch logs into a governed data lake, the company aimed to enable robust reporting and real-time insights for its business operations.

The Goal

To create a secure, scalable, and automated data ingestion and processing pipeline that consolidates data from disparate sources into an accessible and governed data lake. This solution needed to support real-time analytics through dashboards while ensuring strict access control.

The Challenge

Client sought to centralize and streamline their data management processes to gain deeper insights into product usage, customer demographics, and overall business performance. Their data was dispersed across MongoDB and AWS CloudWatch logs, lacking a unified platform for comprehensive analysis. Additionally, they required secure, role-based access to this data to ensure compliance and data integrity.

The Solution

Over a focused four-week engagement, we collaborated with our client to design and implement a robust data infrastructure leveraging AWS services:

  1. Automated Data Ingestion and Processing
    • Developed AWS Glue jobs to extract data from MongoDB, transforming and loading it into Amazon S3 as Delta tables.
    • Configured these Glue jobs to perform hourly incremental updates, ensuring the data lake remained current.
    • Created AWS Lambda functions to capture CloudWatch logs and store them in Amazon S3, followed by Glue jobs to convert the logs into Parquet format for efficient querying.
  2. Secure Data Access and Governance
    • Utilized AWS Lake Formation to manage permissions at the Glue database level, controlling access to specific tables based on user roles.
    • Implemented IAM policies to restrict access to S3 buckets, ensuring that only authorized services and users could interact with the data.
  3. Real-Time Business Intelligence Dashboards
    • Configured Amazon QuickSight to connect securely to the data lake, enabling the creation of interactive dashboards.
    • Developed dashboards providing various teams with real-time insights into product usage patterns, identification of key customer segments, and other critical business metrics.

OUTCOMES

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