CASE STUDY
Big Data Analysis Automation Platform
Automated centralized platform for large-scale dataset processing, analysis pipelines, and faster data-driven decision-making.
Problem Solved
The project required tools for large dataset manipulation and automated analysis pipeline execution, including job submission to integrated external applications. The application was designed to serve two main user types: those who perform dataset analysis and those who rely on the outputs for data-driven decision-making.
Problem Solving Approach
Key features of the application were developed in close collaboration with users and stakeholders to ensure solutions were both generic and capable of supporting complex data analysis processes.
The requirement for multiple teams to manipulate large datasets simultaneously created a high demand for hardware resources. The current production server is equipped with 32 CPUs (2.1 GHz each) and 14 TB of disk storage. To maximize efficiency, the application was developed with multithreading and parallelism, and some domain-specific functionalities were deployed as separate services accessed through the main application’s interface.
Outcome
The application’s agnostic design allows for flexible input of various dataset types and enables the configuration of analysis pipelines by combining supported features in any order. This flexibility allows the application to handle specific type and structure of input data and meet different analysis needs.
The application is a valuable tool for teams managing large datasets and extracting insights, supporting 10 internal teams and 12 business processes with a growing user base within the client’s organization.
Enabled flexible input and configuration of analysis pipelines, accommodating various dataset types and structures.
Provided a valuable tool for managing large datasets and extracting insights across 10 internal teams.
Support for 12 critical business processes, with a continuously growing user base within the client’s organization.
Significant time saved by automating the analysis process, with analysis pipelines triggered automatically when a new dataset is detected in a predefined input location.
Key Features Implemented
Dataset Preprocessing Automation
Implemented preprocessing workflows including remote storage downloads, decompression, dataset structure comparison, filtering, replacement, merging, and uploads.
Analysis Automation Workflow
Developed preconfigured steps for performing one dataset processing task.
External Application Integration
Integrated external applications to support seamless dataset analysis, processing, and presentation workflows.
Consolidated Results Overview
Delivered centralized analysis result views enabling faster and more informed decision-making.
Technologies
Development
Project Timeline and Team Structure
The project has been in development since 2018 with a team of 7 members, including Software Engineers, QA Engineers, a DevOps Engineer, a UX/UI Designer, and a Product Manager. The team continuously delivers Software Development and Product Design services.
Software Engineers
QA Engineers
DevOps Engineers
UX/UI Designers
Product Owner
Methodology
The Scrum framework has been used throughout the project, with Sprints varying between 2 and 3 weeks. When the team size was reduced and involved 2 Software Engineers, it was collaboratively decided to extend the Sprint length to increase the value of the increment delivered to users as a Production release at the end of each Sprint.
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