CASE STUDY
Real-Time Big Data Processing Platform
Modernized big data platform delivering rapid decision intelligence through scalable processing pipelines, faster data evaluation, and continuous operational insights.
Problem Solved
The client’s existing Big Data platform struggled with slow data quality evaluation processes that often required days or weeks of iterative analysis before decisions could be made. At the same time, an outdated user interface negatively impacted operational efficiency and overall user experience.
The project required modernization of the platform architecture and development of a faster, more scalable data pipeline capable of supporting rapid analysis, real-time insights, and improved operational workflows.
Problem Solving Approach
The project involved creating a specialized backend service using hexagonal architecture and clean code principles. An event-driven approach was adopted, implementing an event bus based on Apache Kafka and utilizing Java’s reactive stack for development. Data processing pipelines were developed using Cassandra, Postgres, and Elasticsearch technologies to ensure efficient and scalable operations.
The strategy focused on enabling rapid data analysis with a fifteen-minute decision turnaround per data record.
Outcome
The new system integrates multiple services, establishing a faster data pipeline and improving both user experience and operational efficiency.
Despite the project’s expanding scope, features were consistently delivered on schedule, leading to a successful deployment with over 500.000 clients onboarded and over 500 million requests processed through our service post-launch.
This platform is a powerful solution for organizations dealing with large datasets, offering rapid data processing, real-time analytics, and continuous feedback. It improves the efficiency of data evaluation and empowers users to maintain and enhance data quality through proactive monitoring and real-time insights.
Faster real-time data evaluation workflows
500,000+ clients onboarded after launch
500+ million requests processed successfully
Key Features Implemented
Scalable and efficient backend architecture
Comprehensive data processing pipeline
Real-time feedback for data quality enhancement
Proactive monitoring and automated responses
Technologies
Development
Project Timeline and Team Structure
The project has been ongoing since 2021 and includes a team of seven Software Engineers, three QA Engineers, a DevOps Engineer and Product Management roles, delivering Software Development services.
Software Engineers
DevOps Engineers
QA Engineers
Product Owner
Methodology
An agile Scrum approach was used for synchronized sprints and release cycles, fostering collaboration with the client’s stakeholders. Transparent communication and a focus on quality helped build a strong, trust-based relationship.
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