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REST/GraphQL API Data Fetching & Consolidation

Building a Unified Data Layer for Scalable, Cross-Platform Access

Tools Used: Python · REST API · GraphQL · Scheduling Framework

Project Overview

Modern platforms rely on synchronized data across services. I built a Python-powered automation system that fetches data from multiple **REST** and **GraphQL** APIs, consolidating it into a single source of truth for downstream applications.

The system was designed to run on a scheduled basis, dynamically handling diverse endpoints and merging data into a unified structure. This solution supports IT service providers and cross-platform businesses by simplifying data access, improving reliability, and enabling future integrations.

Data Preparation & Exploration

Using Python, I configured dynamic connectors for:

  • REST APIs with varied authentication and pagination schemes
  • GraphQL endpoints with custom queries and schema parsing
  • Batch scheduling for periodic data pulls
  • Retry logic and error handling for robustness

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    I built logic to normalize and merge:

  • JSON responses from REST endpoints
  • Nested GraphQL data structures
  • Metadata tags for source tracking and schema alignment
  • Conflict resolution strategies for overlapping fields

    • This ensured consistent, clean, and queryable data across services.

    System Architecture & Integration

    The system architecture was modular and future-proof:

  • Scheduler Layer: Automates batch fetching at defined intervals
  • Connector Layer: Handles REST and GraphQL endpoints with dynamic configs
  • Merge Engine: Consolidates incoming data into a unified schema
  • Error Handling: Implements retries, logging, and alerting for failures
  • Access Layer: Exposes consolidated data for downstream services

    • The backend was built for extensibility, allowing new APIs to be added with minimal configuration.

    Key Insights & Strategic Recommendations

    The project delivered several strategic benefits:

  • Unified data access across platforms reduced integration complexity
  • Scheduled automation ensured timely updates without manual effort
  • Dynamic endpoint handling enabled rapid onboarding of new services
  • Robust error handling improved reliability and reduced downtime

    • These outcomes positioned the system as a scalable integration layer for future growth.

    Operational Impact

    The project achieved measurable improvements:

  • Reduced manual data fetching and reconciliation by 90 percent
  • Improved data freshness and consistency across platforms
  • Enabled faster onboarding of new APIs and services
  • Provided a foundation for analytics, reporting, and automation

    • Collaboration & Workflow

    I collaborated with multiple stakeholders to ensure alignment:

  • Engineering: To define endpoint schemas and integration logic
  • Data Teams: To validate merged structures and ensure queryability
  • Operations: To configure scheduling and monitor sync health

    • The system was deployed with monitoring hooks and documentation for easy maintenance.

    Lessons Learned

  • Flexibility is key: Dynamic configs simplify multi-API integration
  • Error handling matters: Retry logic and logging prevent silent failures
  • Consolidation drives clarity: A single source of truth improves decision-making