Overview

FluxChain is a supply chain intelligence platform that delivers data-driven insights to help organizations manage sustainability risks and conduct end-to-end supply chain due diligence. As data volumes grew across the UK and Hong Kong regions, FluxChain faced performance bottlenecks in data synchronization, model migration, and testing reliability. Bacancy modernized FluxChain’s data processing architecture by replacing legacy Ruby-based workflows with a high-performance Node.js ETL pipeline, introducing Kafka-based distributed processing, and implementing automated testing and data validation. The solution significantly improved data freshness, reliability, and scalability across the platform.

Technical Stack

  • Node.js
  • Type Script
  • SQL
  • Kafka
  • Prisma ORM

    • Industry

    Logistics & Transportation

  • region

    • Region

    Hong Kong

  • project-size

    • Project Size

    Non- Disclosable

Highlights

Completed legacy model migration with improved performance and data validation.

Cut data synchronization time from 24 hours to 30 minutes using a Node.js–based ETL pipeline.

Built an end-to-end testing framework running 250+ Gherkin tests in CI pipelines.

Implemented a Data Correctness Checker for daily data quality monitoring.

Challenges & Solutions

Synchronizing Millions of Records Efficiently

  • Solution: The legacy Ruby on Rails synchronization process took nearly 24 hours to transfer large datasets. Bacancy rebuilt the pipeline using Node.js and added checksum-based validation logic, reducing synchronization time to approximately 30 minutes while maintaining data accuracy.

Maintaining Data Quality During Model Migration

  • Solution: Our data engineer is developing a Data Correctness Checker that performs daily validations to identify missing or inconsistent records. This approach ensures reliable data synchronization and maintains high data quality throughout the ongoing model migration process.

Lack of Reliable End-to-End Testing for Model Changes

  • Solution: Solution: An automated end-to-end testing framework is being implemented using Gherkin, with over 250 tests running in the pipeline. This is helping prevent regressions and ensure consistent system behavior as model migrations continue.

Scaling Distributed Data Processing Reliably

  • Solution: Kafka-based distributed processing was introduced with Monitor Fetcher and Processor roles. This architecture enabled fault tolerance, horizontal scalability, and efficient processing of high-volume data streams.

Core Features

  • Real-Time Data Synchronization & ETL Pipeline
  • Kafka-Based Distributed Processing Architecture
  • Automated Data Healing & Recovery System
  • Database Housekeeping & Performance Optimization
  • Optimized pipelines designed for low-latency processing
  • Reprocessing support for failed or partial synchronizations
  • no.-of-resources

    • No. of Developers

    01

  • time-frame

    • Time Frame

    Auguest 2025 – Ongoing

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