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Cloud Structure for SaaS

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Cloud Migration & SaaS, AWS.

The Client

Our client provides SaaS software that helps marketing teams solve their biggest challenges, like business growth, cross-channel campaign effects, and customer satisfaction. Its purpose-built solution analyzes data, provides marketing analytics, manages content, and customize tools for 3,500+ brands in 70+ countries.

Re-architecture Cloud Structure for SaaS

The client's customers needed a potent partner to maintain their application on a modern and flexible cloud base. Thus the client required infrastructure that would be elastic, scalable, and more cost-effective than their existing IBM cloud solution.


To achieve an in-depth understanding of the business and technical requirements, we worked closely with the engineers on the client's side. After performing a full audit on the IBM-based environment, we designed a solution fitting AWS best practices. Then we converted infrastructure to code, fully automate it, and developed server configurations.


We used CloudFormation and DevOps automation to deploy the staging and production of the environment. After we've tested each stage, we integrated all settings back into the automation tools. For keeping database synced, data replication was set up from IBM cloud to AWS. Before the actual migration took place, our QA had tested every component iteratively. We've been using such practice over the years as this methodology is exceptionally effective in delivering successful migrations.


The client can now match the growing needs of its customers with the flexibility and power of the AWS Cloud. With acquired automation and auto-scaling, engineers focus more on developing and less on operations and capacity build-outs. They achieve operational efficiency, bringing down the total cost of infrastructure ownership. Moreover, they've increased the availability and robustness of services offered to their customers.

Tech Stack
Tech Stack
  • AWS
  • EC2
  • RDS
  • Java
  • Spring
  • Spring Cloud
  • Big Data
  • Hadoop
  • Kafka
  • Apache Flink
  • PostgreSQL
  • IBM DB2

The same approach is applicable to various cloud-like or non-cloud based solutions, using dedicated hostings, local VPC, Rackspace, Digital Ocean or any other provides for clients that need to be migrated to AWS, Microsoft® Azure or Google Cloud.

  • Client location:
    New York
  • Team size:
  • Project duration:
    6 months

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