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Cybage Enabled the Microservice-Based Big Data Platform To Be Seamless and Fit for Advanced Growth

Success Story

Services:
  • Test Engineering
  • Cloud

Cybage Enabled the Microservice-Based Big Data Platform To Be Seamless and Fit for Advanced Growth

Software & Hi-Tech
Posted On: 20 March, 2024

About the Client

  • Is a leading player in in-feed, native advertising.
  • Helps mobile-focused marketers acquire, engage, and retain new users, generate sales, and maximize return on investment.
  • Offers integrated platform to scale with Facebook, Twitter, Instagram & Pinterest.

Business Needs

  • Confirm architectural choices of new container-based microservice platform for big data scale.
  • Provide feedback and confirm design improvisation to achieve processing KPI for targeted large-scale data.
  • Derive configurations and tunning for tasks, services, cluster for response, readiness and Just-In-Time scalability.
  • Cluster Capacity Sizing and better resource consumption.
  • Define and confirm interoperability across hybrid deployment and high availability.

Our Solutions

  • Provided end-to-end optimization and cluster sizing.
  • Executed resilience test to minimize multi-failure cases across integrations and offered Multi-AZ Cloud provisions to achieve HA.
  • Scaled Config benchmarks to determine restructured services to take benefit of AWS framework such as utilization of queue, EBS volumes (IOPS and provisioning), etc.
  • Performed Work Flow Model Analysis to optimize heavy processing services to take advantage of container level horizontal scaling.
  • Completed bottom-up multitier config tunning validation across data scale, concurrency scale, cluster scale, and node scale.
  • Defined consumption matrices for better predictability to handle future scalability and provisioning.

Business Impact

  • Improved resource consumption at service level by fixing memory leak issues.
  • Reduced the probability of getting rate limit error from publishers like Facebook.
  • Boosted performance and throughput by making multiple changes to the application.
  • Removed delay time by improving message handling at SQS level.
  • Restructured microservice platform to take advantage of AWS framework such as horizontal container scaling, utilization of queue, EBS volumes (IOPS), etc.
  • Benefited from container level horizon scaling by splitting heavy processing services.

Technology Stack

Cybage Enabled the Microservice-Based Big Data Platform To Be Seamless and Fit for Advanced Growth
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