Serverless Architecture for Scalability & Efficiency at VMLA

Usama Idrees

Data Analyst
Data Engineer
DevOps Engineer
Amazon DynamoDB
AWS
Redshift
Capital One
Vector ML Analytics

About Business

Vector ML Analytics B2B, SaaS, Financial Forecasting Platform for Banks & Lending Institutions. Vector is the complete analytical package for profitability modeling designed to help lending and leasing institutions analyze the financial performance of their loan portfolios. The platform helps lending & leasing institutions make optimized decisions leading to higher return on investment. Vector ML is simple, intelligent, accurate, and affordable. The platform uses automation to replace countless Excel models used by CFOs and analysts, producing a 360-degree view of the institutions’ balance sheet.

Problem Statement

The client was facing significant challenges with their data processing pipeline. Their reliance on EC2 instances for processing bank data and generating installment plans resulted in high costs, operational inefficiencies, and scalability issues. The manual nature of the process led to frequent errors and delays, impacting overall business operations.

Solution Provided

As a consultant, I assessed the client’s specific needs and identified key areas for improvement. The primary issue was the inefficient use of EC2 resources due to fluctuating workloads. To address this, I recommended transitioning to a serverless architecture. By leveraging serverless functions, the client could significantly reduce costs, improve performance, and enhance scalability.
I proposed integrating serverless data warehousing and ETL services to optimize the data processing. This combination creates a highly efficient, cost-effective, and scalable data processing solution.
By adopting these serverless technologies, the client could streamline their operations, reduce costs, improve data processing speed, and gain valuable insights from their data.
AWS Serverless - Usama
AWS Serverless - Usama
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