StackTrend Technology Adoption Observatory

A comprehensive data engineering solution for analyzing GitHub repository trends and technology adoption patterns using Microsoft Fabric, Azure OpenAI, and advanced analytics pipelines.

Overview

StackTrend is an intelligent data platform that combines automated data collection, LLM-powered classification, and real-time analytics to provide insights into open source technology ecosystems. The platform monitors GitHub repositories, classifies them using Azure OpenAI, and generates actionable business intelligence through automated data pipelines.

Architecture

The platform implements a modern medallion architecture pattern within Microsoft Fabric, creating a seamless flow from raw data collection to business intelligence dashboards.

Youtube video of project demo I gave at an Microsoft event.

Data Flow

1. Data Collection Layer: GitHub API serves as the primary data source, providing repository metadata, statistics, and activity information
2. Orchestration Layer: Microsoft Fabric Data Factory manages pipeline scheduling, parameter passing, and workflow dependencies
3. Processing Layer: Fabric Notebooks execute PySpark transformations, integrate with Azure OpenAI for intelligent classification, and implement business logic
4. Storage Layer: Lakehouse architecture with Bronze, Silver, and Gold layers using Delta Lake for ACID compliance and schema evolution
5. Semantic Layer: Direct Lake connections provide optimized data models for analytical workloads
6. Presentation Layer: Power BI dashboards deliver real-time insights with automated refresh capabilities

Technical Architecture

Bronze Layer (Raw Data)

• GitHub API responses stored in their original format
• Minimal transformation, preserving data lineage
• Delta Lake format for efficient querying and time travel

Silver Layer (Curated Data)

• Data cleaning, standardization, and quality validation
• Azure OpenAI integration for intelligent repository classification
• Smart classification logic that avoids redundant LLM calls, reducing costs by 90%
• Technology categorization across AI, ML, Data Engineering, Web Development, DevOps, and other domains

Gold Layer (Analytics-Ready)

• Pre-aggregated metrics and KPIs optimized for dashboard performance
• Technology trend analysis, adoption lifecycle classification, and market intelligence
• Repository health scoring and comparative analytics
• Time-series data for historical trend analysis

Key Features

Intelligent Classification System

• LLM-powered repository categorization using Azure OpenAI GPT-4o Mini
• Advanced prompt engineering for accurate technology classification
• Confidence scoring and drift detection for classification quality
• Cost optimization through conditional classification logic

Real-Time Analytics Pipeline

• Automated data collection and processing every 2 hours
• Delta Lake merge operations for efficient data updates
• Cross-lakehouse access patterns for complex analytical queries
• Automated Power BI dataset refresh with pipeline completion triggers

Scalable Data Architecture

• Microsoft Fabric unified platform eliminating traditional data silos
• Lakehouse storage providing both analytical and transactional capabilities
• PySpark processing for big data workloads
• CI/CD integration with GitHub Actions for automated notebook deployment

Business Intelligence Integration

• Pre-built Power BI dashboards for technology trends and repository analytics
• Real-time KPI monitoring with configurable refresh intervals
• Interactive filtering and drill-down capabilities
• Mobile-optimized visualizations for executive reporting

Implementation Highlights

Cost Optimization

• Smart classification reduces Azure OpenAI API calls by approximately 90%
• Delta Lake merge operations minimize storage costs through efficient data updates
• Automated resource scaling based on workload requirements

Data Quality Assurance

• Comprehensive validation rules at each processing layer
• Automated data quality monitoring with anomaly detection
• Schema evolution support for changing data structures
• Audit trails for data lineage and compliance

Operational Excellence

• Zero-dependency implementations for challenging Fabric environments
• Robust error handling with detailed logging and monitoring
• Automated deployment pipelines with environment promotion
• Performance optimization through strategic data partitioning

Technology Stack

• Microsoft Fabric: Unified analytics platform providing lakehouse, notebooks, and orchestration
• Azure OpenAI: GPT-4o Mini for intelligent repository classification
• Delta Lake: ACID-compliant storage layer with time travel capabilities
• Apache Spark: Distributed processing engine for large-scale data transformations
• Power BI: Business intelligence platform with real-time dashboard capabilities
• Python/PySpark: Primary development languages with extensive library ecosystem
• GitHub Actions: CI/CD pipeline for automated deployment and testing