Searce Helped Razor Group by Leveraging AWS Lambda Which Helped Them Increase the Scalability and Efficiency
Challenges
Razor Group is one of Europe’s leading e-commerce goods companies. They have critical infrastructure currently hosted in the Ohio region.But due to strategic business they wanted to enhance their security and migrate the workload out of the Ohio region to AWS Frankfurt region. Along with that they have below challenges:
- Manual Infrastructure Management: Razor Group manages the provisioning, scaling, and maintenance of servers by monitoring them 24/7. Serverless architectures like Lambda provide a pay-as-you-go model, where customers only pay for actual compute time. As Razor Group uses manual procedures which lead to higher operational costs due to over-provisioning or idle resources.
- Scalability Complexity: As Razor group uses manual approach and during sudden spikes in traffic end users are facing application slowness issues.
- Cost Optimization: Maintaining and managing a dedicated server fleet was expensive, especially during periods of low usage. Razor Group uses higher configuration servers in their dev environment during testing and they run it 24/7, though they do not use it every time.
Searce Solution
Searce Team gathered all the requirements in the form of Deep Dive sessions with Razor Group’s Team to understand the underlying Architecture of the Environment and its features. After series of discussions and gathering the requirements Searce provided a Highly Available, Secure & robust architecture and have implemented the proposed solution. Searce Team leverage AWS Lambda to automate their manual tasks and below are the implementation details.
Implementation
- Function Decomposition: Searce helped Razor Group to decompose their monolithic application into smaller, independent functions, each with a specific purpose.
- Serverless Computing: The decomposed functions were deployed as AWS Lambda functions. Each function was triggered by specific events, such as API requests or scheduled tasks. The Lambda service automatically scaled the infrastructure based on incoming request volumes, eliminating the need for manual scaling.
- Integration and Orchestration: Lambda functions were integrated with other AWS services, such as API Gateway, Amazon S3, and Amazon DynamoDB. This allowed Razor Group to create a fully serverless architecture, ensuring seamless data flow and efficient processing.
- Continuous Deployment: With the help of AWS CodePipeline and AWS CodeCommit, Razor Group implemented a continuous deployment pipeline. Code changes were automatically tested, built, and deployed to Lambda functions, enabling rapid iterations and reducing the time-to-market.
Business Impact
- Scalability: Lambda’s automatic scaling capabilities allowed Razor Group to handle sudden spikes in traffic without provisioning additional servers. This ensured a smooth user experience during high-demand periods and reduced the risk of downtime.
- Cost Savings: By adopting a serverless approach, Razor Group significantly reduced their infrastructure costs. Lambda’s pay-per-invocation pricing model meant they only paid for the actual usage of functions, eliminating the need for idle server capacity.
- Increased Development Speed: With the introduction of Lambda, Razor Group experienced faster development cycles. Developers could independently work on individual functions, test them in isolation, and deploy changes quickly, leading to shorter time-to-market.
- Operational Efficiency: The serverless architecture reduced the operational overhead of managing servers, enabling Razor Group to focus more on building and improving their applications. The seamless integration with other AWS services streamlined data flow and processing.
Conclusion
The adoption of serverless computing with Lambda offers numerous advantages for Razor Group. The pay-as-you-go cost model, auto-scaling capabilities, reduced operational overhead, and event-driven flexibility empower Razor Group’s to build efficient, scalable, and cost-effective solutions.
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