CASE STUDY
Unified Processing of Small and Big Data Sets
Built a scalable processing engine that unified small and large dataset workflows, reduced infrastructure overhead, optimized performance, and enabled faster business decision-making.
Problem Solved
The client lacked a scalable, consistent approach to processing diverse data volumes, and a data processing and analytics engine capable of handling diverse data sizes, from kilobytes to gigabytes was needed.
A consistent transformation logic was employed to maintain simplicity and ensure effective data processing. The goal was to create a uniform methodology that could easily scale with data needs, providing a reliable and efficient data processing system.
Problem Solving Approach
First, business rules validation was enabled. This process required close collaboration to understand the business logic and domain, ensuring alignment with expectations and timelines.
Next, performance issues were addressed by optimizing data processing. Overhead was reduced by chaining smaller jobs into larger ones and introducing a “fast lane” for small data sets through smaller Spark jobs. Throughput was maximized with resource utilization configurations using dynamic settings based on data size.
Outcome
The system’s complex expansion was successfully supported and improved, exceeding initial expectations. Significant cost reductions and performance optimizations were achieved by continuously upgrading to the latest tools and libraries. The system’s importance and usage expanded significantly, supporting more business decision-making within the corporation. Strong foundations and successful results ensured a long-term development vision and ongoing collaboration.
Improved and successfully supported the complex expansion of the system beyond scope
Achieved significant cost reductions
Introduced performance optimizations by upgrading to the latest tools and libraries
Built a long-term collaboration and development partnership
Key Features Implemented
Dynamic and configuration-driven data processing flow
Deployment pipelines and test suites
Successful monitoring of the system in production
Infrastructure migrations and technology changes
Handling database-related migrations
Technologies
Development
Quality Assurance
DevOps
Project Timeline and Team Structure
The project has been ongoing since 2018, with the team size adapted to meet changing needs. At its largest, the team consisted of 19 members, with an average size of 10-12 people. Roles included Product Owner, Product Delivery Manager, Software Engineers, QA Engineers, and DevOps Engineers delivering Software Development services.
Product Owner
Software Engineers
QA Engineers
DevOps Engineers
Product Delivery Manager
Methodology
An agile development approach using Scrum and Kanban frameworks was employed to manage the project effectively. Quarterly releases were scheduled based on needs but remained flexible to respond promptly to urgent requirements. Kanban visualized DevOps tasks, while Scrum organized business requests and development. Despite the complexity and size of the program, the team adapted to changing needs and maintained strong daily collaboration with remote teams across three locations.
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