Our customer is a company that developed a fintech product for educational organizations. Its purpose is to reduce transaction costs, track financial transactions in real-time, and eliminate bureaucracy from the financial education system.
The client experienced the need to automate the deployment process of microservices and simplify the release process. One of the requirements was to migrate Docker containers to AWS ECS and move existing infrastructure to Terraform.
1. Amazon Route 53 provides DNS configuration.
2. AWS WAF is a web application firewall that protects the Fintech platform against common web exploits.
3. Amazon CloudFront is a fast content delivery network (CDN) that speeds up distribution of static and dynamic web content.
4. Elastic Load Balancing (Application Load Balancer) distributes across the AWS Auto Scaling group of the Fintech platform containers in multiple Availability Zones.
5. On Amazon ECS Cluster, there are running serverless containers with Fintech applications and relevant dependencies which is managed by AWS Fargate service.
6. Amazon ElastiCache for Redis provides a caching layer for the database.
7. The use of Amazon Aurora allows to simplify database administration (including high availability and multi-master configuration).
As a part of the implementation, the team created a CI/CD environment using Jenkins CI to automate the deployment and release process. The CI pipelines include SonarQube scan and static code analysis – they help to improve code quality and reduce the number of bugs. All secrets for the deployment process are stored securely in Jenkins.
The entire infrastructure was migrated to Terraform and got stored in the GitHub repository. The base infrastructure such as VPC, ECS cluster, ALB, etc. is deployed manually with Terraform CLI. The state files for Terraform are stored in AWS S3, so that they can be easily accessible from the local and CI environment. The Jenkins pipeline creates a Docker image, pushes it to the repository, and then uses Terraform
to deploy microservices. Pipelines are triggered automatically on changes in application code. All microservices are running in AWS ECS and managed by AWS Fargate.
Terraform, Amazon ECS Cluster, AWS Fargate, Jenkins CI, Amazon Aurora, Amazon CloudFront, Amazon ElastiCache, Amazon Route 53, AWS WAF, Elastic Load Balancing, ESLint, Sonarqube.
With the delivered solution, the client has got a highly available and resilient to failures microservice architecture that uses AWS ECS and a fully managed and automated deployment process using Jenkins CI. Besides, the infrastructure has been migrated to Terraform which allows to manage and add new resources much easier and more efficiently.
The client had a manual multi-environment infrastructure deployment аnd no continuous integration & continuous deployment automation. That significantly impacted the speed of product development, release, and delivery cadence of new application versions in general. Besides, it required a considerable part of the development team capacity to process it manually.
The client faced the need to automate multi-environment continuous integration and deployment process of microservices, move the front-end part to CDN as well as deploy multi-environment infrastructure as a code.
The client had high costs for development, deployment, and, most importantly, operation of the data platform including Data Lake, Ingestion, and ML Pipelines. The pipelines were mostly running in EC2 instances, which led to the increased cost of operations and required a significant amount of time to deploy and test the pipelines in lower environments.
The client came with a request to automate and unify the deployment process of serverless applications on AWS Lambda. Having manual deployments before, the customer was facing inconveniences and difficulties, including non-uniform environment setup (versions of Serverless, Python, Node, and so on were inconsistent) and inability to control the environment changes in one place.
The client needed to strengthen collaboration within the data scientists team by moving research capabilities into cloud workloads. Besides, they wanted to automate and unify the deployment process of AWS resources. That, in turn, would decrease the time and effort required for a team of data scientists to build and test their models.
CI/CD (Continuous Integration/Continuous Deployment) offers a seamless bridge from development to deployment. Further, in the evolving world of mobile app development, efficiency and adaptability are key.
At Matoffo, we specialize in customizing CI/CD solutions that cater to cross-platform mobile application development. We focus on improving the technical workflow and enhancing our client’s overall business performance and user experience.
Our primary goal is value delivery for the client by resolving technical challenges and assisting them to achieve their objectives. AWS cloud solutions are great toolset which we use to make it happens, primary by outstanding agility, where companies could scale up cloud resources as needed and significantly decrease time from idea to market as well as seamless adopt rapidly growing tech capabilities to transform their businesses to be competitive in the market.
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