CASE STUDY
Big Data Analytics Solution
Built a Big Data processing system to unify diverse inputs into standardized reports, enabling data quality insights and informed decision-making.
Problem Solved
The project involved developing a Big Data processing system capable of handling diverse input files and transforming them into uniform, adaptable reports for end users. This system provides insights into data quality, allows for version comparisons, and supports informed decision-making by highlighting data strengths and weaknesses.
Problem Solving Approach
The roadmap and release planning process involves collaborating with the client every six months to discuss and prioritize upcoming features. Functionality ideas are generated from extensive end-user communication and require client approval before implementation.
Outcome
The developed tool evolved into a critical system for assessing data quality, praised for its stability, scalability, and user-friendliness. The system has continuously expanded over ten years of collaboration with new functionalities and integrations. It now serves Data Analyst teams across the USA, Europe, and Asia, providing a scalable solution that supports diverse data sources and processes.
Developed critical Big Data processing system for assessing data quality
Support for diverse data sources and processes
Key Features Implemented
File Processing
Support for processing any file type regardless of size and complexity, with the ability to convert it to any other file type if needed.
User Control
Users have almost complete control over the layout and can evaluate fields in real-time via a simple and intuitive UI.
Dataset Comparison
Implemented dataset comparison in Spark, allowing the comparison of datasets with over 100 million records at the record and field level. The resulting report provides key insights into significant differences while enabling low-level drill-down. This feature is widely used and integrated into many automated processes due to its reliability and stability.
Technologies
Development
Quality Assurance
DevOps
Project Timeline and Team Structure
The ongoing collaboration with this customer spans over ten years delivering Software Development services. Team size ranges from 10 to 18 members, including roles like Product Owner, Scrum Master, Software Engineers, QA Engineers, DevOps Engineers, and UI/UX Designers, adjusted to meet project needs.
QA Engineers
DevOps Engineers
Product Owner
Software Engineers
UX/UI Designers
Scrum Master
Methodology
An agile approach guides our development cycle, starting with semi-annual planning involving the client and stakeholders. We use an in-house tool for feature tracking, allowing for reliable long-term planning and priority adjustments. On a micro level, two-week sprints are used for deliveries, with planning, scheduled demos, and fixed-date releases managed through Jira.
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