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About Nobel Page

The client is a data engineering and analytics solutions provider based in the U.S., serving professionals across industries such as finance, healthcare, and scientific research.

Their core users include:

Data Analysts & Scientists
IT Professionals & Engineers
Business Intelligence Teams

The client needed a robust backend tool to parse and standardize data from multiple formats into structured, analysis-ready CSV files.

Challenges

Performance or Efficiency Issues

Manual extraction from JSON, XML, and TXT formats was time-consuming and error-prone
Sorting large datasets resulted in high memory usage and long processing times

Access/Security Gaps

Sensitive data manipulation demanded accuracy and controlled error handling to prevent leakage or corruption

Scalability or Maintainability Bottlenecks

Adding new data types or formats required rewriting large portions of the codebase
Lack of modularity made updates and debugging difficult

Integration or Data Flow Issues

No existing framework to define transformation rules across heterogeneous files
Data had to be manually formatted before analysis

UI/UX or Workflow Limitations

Non-technical users lacked the ability to define custom filtering/sorting without coding
Needed configurable workflows that could be reused and tested at scale

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Solution: Feature Mapping & Engineering Strategy

Challenge	Solution	Result
Parse and standardize multiple data formats (JSON, XML, TXT)	Developed a flexible Go-based data reader that auto-detects input format and applies tailored parsers for each	Unified pipeline for multi-format ingestion; reduced parsing time by over 50% compared to manual or legacy scripts
Custom logic needed for different data use cases	Enabled TOML-based configuration files to define extraction rules, filters, and sorting criteria	Business and data analysts can modify logic without changing code; increased adaptability across projects
Sorting massive datasets efficiently	Implemented advanced algorithms like Merge Sort, QuickSort, and external sorting for memory-heavy operations	Sorting time improved by 60% on datasets >1M rows
Filtering and transforming data with accuracy	Built a modular filtering engine with logical operators and conditional parsing	Extracted only relevant subsets of data; reduced CSV clutter and improved analyst productivity
Ensure fault tolerance and debugging clarity	Architected with robust error handling, graceful failovers, and isolated test cases	90% fewer runtime crashes and simplified debugging with unit tests for edge-case formats

Tech Stack:

Go, TOML (config), CSV (output)

Integration Flow: File input → Parser module → Filter/Sort engine → CSV export
Notable Engineering Decision: Configuration-driven architecture for low-code customization
Optimization: Disk-based sorting when memory threshold is exceeded
Testing Framework: Unit test suite covering malformed files, invalid configs, large-scale CSV exports

Architecture Overview

Nobel Page

Measurable Results / ROI

50% faster data parsing across mixed file formats
60% performance gain in large-scale sorting
90% reduction in crashes due to test-driven architecture
Zero rework when changing data rules via TOML configs
High analyst productivity through streamlined workflows

Why ValueCoders?

Go language specialists with a focus on performance engineering
Low-code config-driven development for scalable data handling
Robust testing culture ensuring edge-case resilience
Modular architecture built for reuse, readability, and fast iteration
Collaborative agile delivery with feedback loops and transparent progress tracking

Client Quote

“ValueCoders delivered a smart, high-performance data reader tailored to our exact needs. Their engineering team made it easy to handle JSON, XML, and plain text with minimal effort—and the TOML config system was a game changer for analyst workflows.”
— Lead Data Architect

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