Our Work
Discover How We Deliver Real Results
Driving Business Transformation for Our Clients
Explore a selection of our engagements where we’ve enabled clients to optimize their operations and elevate their businesses. Each case study illustrates the transformative power of our solutions and dedicated expertise.
Migration & Modernization: Transitioning from On-Prem to AWS ECS Fargate by cutting cost & complexity
The Challenge:
The client was burdened by the escalating costs and complexities of maintaining an in-house data center. Their existing infrastructure was populated with numerous legacy applications running on dedicated, yet largely underutilized, virtual machines. This setup not only led to inefficient resource use but also significantly increased operational expenses. Compounding these issues, their continuous integration and deployment processes were managed through an on-premises Jenkins setup, which utilized Nomad workers, adding layers of maintenance and operational bulk.
The imperative for the migration encompassed several key objectives:
- Transition their data center to AWS
- Significantly reduce costs
- Enhanced performance and scalability.
- Reduce maintenance for their CI/CD
The Solution:
To address the client’s need for a cost-effective, scalable, and maintenance-reduced infrastructure, we embarked on a comprehensive modernization and migration strategy. The first step involved containerizing the client’s legacy applications using Docker, which allowed for a seamless transition and compatibility within the cloud environment.
Utilizing AWS CloudFormation as our Infrastructure as Code (IaC) tool, we meticulously coded the entire infrastructure needed for deploying these applications on AWS ECS Fargate. This setup included configurations for auto-scaling, ensuring that resources would dynamically adjust to the application’s needs, thereby optimizing costs and performance.
Recognizing the specific operational hours of the applications—primarily needed from 7 AM to 7 PM on workdays—we implemented a schedule-based scaling solution. This adjustment meant that the applications were active only during necessary times, further reducing the operational costs.
Moreover, we transitioned the CI/CD processes to AWS-native tools, opting for AWS CodePipeline and AWS’s source control solutions. This shift not only streamlined the development and deployment workflows but also resulted in significant cost savings by eliminating the need for expensive third-party source version control tools and reducing the overhead associated with maintaining on-premises CI/CD tools like Jenkins and Nomad.
This integrated approach not only modernized the client’s application environment but also aligned their operations with cloud best practices, ensuring a robust, scalable, and cost-efficient system.
TheResult:
The migration and modernization initiative yielded significant and measurable outcomes. By transitioning to AWS ECS Fargate with Docker containerization, the applications became more resilient and scalable. The auto-scaling feature, coupled with schedule-based activity adjustments, led to a notable reduction in operational costs. Resources were now utilized more efficiently, with the infrastructure scaling up during peak business hours and scaling down when not needed, thus avoiding unnecessary expenses.
The adoption of AWS-native tools for source control and CI/CD processes further enhanced operational efficiency. AWS CodePipeline streamlined the deployment cycles, reducing the time from development to production and ensuring that updates could be rolled out swiftly and reliably. This not only cut down the costs associated with third-party tools and their maintenance but also significantly reduced the complexity of the CI/CD environment.
Moreover, we observed a reduction in the personnel needed to maintain the entire operations and deployments, enhancing operational efficiency and reducing labor costs.
Overall, the project not only met the initial goals of reducing costs and modernizing operations but also provided a robust framework for future growth and scalability. We saw improved application performance, reduced downtime, and a more agile response to market demands, positioning the organization well for continued success and innovation in their sector.
POC on Migrating from Google Cloud to AWS with Snowflake Integration
The Challenge:
The primary concern was the escalating costs associated with client’s current setup, which necessitated a more cost-efficient solution. Additionally, the complexity of their system had increased over time, leading to difficulties in maintenance and scalability.
To address these issues, the client aimed to migrate their operations to AWS, known for its flexible pricing models and robust service offerings. Moreover, they sought to leverage AWS’s capabilities to refactor their existing codebase, thus simplifying their architecture and reducing overhead.
A significant part of this transformation involved transitioning from Google BigQuery to Snowflake. This move was driven by the need for a more dynamic and scalable data warehousing solution that could integrate seamlessly with AWS, enhancing the client’s data processing and analytics capabilities.
The Solution:
Initial Assessment and Planning: The proof-of-concept (POC) began with a thorough assessment of the client’s current infrastructure on Google Cloud to pinpoint potential for cost savings and simplification. A clear, step-by-step plan was crafted to outline how each component would be migrated to AWS in a controlled, minimal-risk environment.
Implementing AWS EKS Fargate for Serverless Proof-of-Concept: AWS EKS Fargate was selected to demonstrate the potential of serverless Kubernetes operations within AWS. This choice allowed us to explore the benefits of managing applications without the complexity of server provisioning and scaling, focusing purely on deploying and managing containerized applications.
Code Refactoring and Integration with Snowflake: The codebase was partially refactored to align with best practices and to demonstrate the efficiency improvements possible with AWS tools and services. A key element of the POC was integrating Snowflake, which served as a scalable and efficient alternative to Google BigQuery, allowing us to test enhanced data warehousing capabilities.
Testing and Evaluation: The POC included rigorous testing to ensure the functionality and integration of all components. This phase was crucial for evaluating the performance, cost-effectiveness, and scalability of the proposed AWS solution.
TheResult:
Cost Optimization and Simplification: The POC effectively showcased cost reductions and simplified operations through AWS migration, utilizing AWS EKS Fargate to demonstrate the efficiency of serverless computing with reduced overhead and improved scalability.
Enhanced Data Management with Snowflake: Snowflake’s integration into AWS enhanced data warehousing and analytics capabilities. It offered dynamic scaling and seamless service integration, meeting the client’s requirements for a robust and adaptable data handling solution.
Architectural Validation: The POC confirmed the suitability of the proposed architectural changes, validating that the new setup could support the client’s operational needs and identifying areas for further enhancement.
Client Confidence and Strategic Insights: Completing the POC provided the client with critical insights and confidence, aiding their decision-making about a full migration. It demonstrated clear benefits and practical feasibility, supporting strategic planning.
A Cloud Re-architecture for a Legacy On-Premises Application: Enhancing Performance & Streamlining Maintenance
The Challenge:
The client’s legacy application was facing multiple critical issues due to its on-premises infrastructure and outdated design principles. Originally heavily integrated with Jenkins and structured around object-oriented programming (OOP), the application suffered from several key limitations:
Complex Configuration Management: The application’s configuration data was stored in a massive JSON file that had grown unwieldy over the years. This file’s size and complex structure made it difficult to manage and read, leading to increased errors and maintenance challenges.
Inefficient Execution Times: The runtime for the application typically ranged from 15-20 minutes, significantly impacted by the availability of Jenkins executors. This dependency created bottlenecks, delaying execution and affecting overall performance.
Duplicate API Calls: The application made frequent calls to a third-party API, and the code handling these calls was duplicated in multiple places within the application. This redundancy not only increased the maintenance overhead but also led to inconsistencies and potential points of failure in interacting with the third-party service.
These challenges hindered the application’s performance and scalability, necessitating a thorough re-architecture to leverage cloud capabilities effectively.
The Solution:
Optimized Configuration Management with DynamoDB: To address the challenge of managing complex configuration data, we designed and implemented a solution using AWS DynamoDB. Leveraging best practices, the configuration data was structured for high availability and scalability, ensuring efficient read/write operations. This approach not only simplified the data management but also enhanced the application’s responsiveness.
Microservices Architecture and AWS Lambda: The monolithic application was deconstructed into smaller, manageable microservices, each running independently on AWS Lambda using Python. This modular approach significantly reduced the complexity of the codebase and improved the maintainability of the application.
Workflow Optimization with AWS Step Functions: To streamline processes and reduce execution times, AWS Step Functions were employed to manage and orchestrate the microservices workflow. By parallelizing independent execution tasks, we drastically cut down the wait times and improved the overall efficiency of the application.
Performance Enhancement with Multi-threading: Inside the AWS Lambda environment, we implemented multi-threading to accelerate processing tasks. By optimizing Lambda functions for memory and CPU usage, we were able to achieve faster data processing rates, making the application more agile and responsive.
Centralized API Management with Lambda Layers: To eliminate redundant API calls and simplify maintenance, all third-party API interactions were consolidated into a single Lambda layer. This layer served as a shared library across different Lambda functions, ensuring consistency and reducing overhead.
Robust CI/CD Pipeline for Infrastructure Management: A continuous integration and continuous deployment (CI/CD) pipeline was established using Infrastructure as Code (IaC) principles. This setup ensured that updates to Lambda functions and layers were systematically managed and deployed, enhancing the reliability and security of the cloud infrastructure.
TheResult:
Dramatic Time Reduction: The application’s runtime was significantly reduced from 15-20 minutes to approximately 1.5 minutes, thanks to efficient microservices execution on AWS Lambda and parallel processing via AWS Step Functions.
Streamlined Configuration Management: Migrating to AWS DynamoDB simplified the handling of configuration data, enhancing accessibility and reducing error rates.
Centralized API Management: Using Lambda layers for API interactions reduced redundancy and simplified maintenance, improving consistency and ease of updates.
Enhanced Infrastructure Scalability and Maintainability: The shift to a microservices architecture and the implementation of a robust CI/CD pipeline enhanced the application’s scalability and ease of maintenance, supporting continuous evolution.
Client Satisfaction: The client reported high satisfaction with the improvements, noting the performance gains and reduced operational overhead, which aligned well with their goals for efficiency and growth.