Cloud Infrastructure Optimization With GCP For An Aspiring FinTech Start-up

Case Study


The Client

The client is a fintech start-up that helps other fintech companies by offering Application Programming Interface (API) infrastructure solutions. This API infrastructure offers APIs that go across bill payments, savings, credit, and payments. They also provide developers with different APIs that let them create financial products that cater to the specific needs of their users. This is helpful for businesses because it allows them to collect dues on bills from any type of payment app and get instant credit alerts and push real-time receipts to their customers.

Project Objective

Business Value – Cloud Infrastructure Optimization

This project focuses primarily on cloud infrastructure optimization. With Niveus, the client managed to host their infrastructure on GCP, improve performance, and make it more affordable and secure.

The client had hosted their infrastructure on AWS and planned to migrate their workload to GCP. The purpose of the migration was to equip them with an optimized infrastructure consisting of open source solutions to avoid vendor lock-in. They wanted to harness the best of Google Cloud capabilities in terms of Kubernetes, managed services and analytics.

Business Solution

Niveus helped the client with AWS to GCP Migration by:

  • Setup of a strong cloud foundation with secure, scalable, modular, and easy to access resources
  • Enabled stronger security posture using GCP security services
  • Facilitated compliance oriented and audit readiness across projects
  • Implemented Continuous Integration and Continuous Deployment across project based on client’s  CI/CD strategy
  • Equipped the client systems to seamlessly integrate with the migrated data
  • Consulted and trained the client’s Devops/ System Admin team


The migration of Elastic cloud would involve the following steps:

  1. Registered ElasticSearch Snapshot repository in AWS S3 using AWS SDK
  2. Captured incremental Snapshots of the ElasticSearch domain using Elastic curator(Python library) and AWS Lambda functions which can be scheduled to obtain automated snapshots
  3. Registered a Snapshot repository in Elastic Cloud based Kibana with the AWS S3 custom ES repository
  4. Restored the Snapshots from the registered repository using Cloud Functions

Technology Stack

Cloud DNS
Cloud Load Balancing
Kubernetes Engine
Cloud Armor
Cloud SQL
Elastic Cloud

Leverage a quick migration to GCP for better optimized cloud infrastructure

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