Table of Contents

    Overview

    A leading sports analytics company needed to enhance its data processing and real-time analytics capabilities as its operations scaled. The existing Azure PaaS-based infrastructure faced limitations in processing speed, scalability, and real-time data handling. To address these challenges, the company transitioned to a High-Performance Compute (HPC) system leveraging Apache Software Foundation (ASF) technologies, enabling real-time insights and optimized performance.

    Challenges

    • Azure Data Factory (ADF) Limitations: Lacked key features, making complex workflows difficult.
    • High ADF Costs: The cost of operations using ADF became unsustainable.
    • Slow Processing Speed: Large data volumes resulted in slow query execution and data stitching delays.
    • Scalability Issues: The existing system couldn’t handle increasing transactional and analytical workloads.
    • Real-Time Data Processing : The previous infrastructure could not support real-time queries and analytics.
    • Need for a High-Performance Infrastructure: Required a Hybrid Transactional/Analytical Processing (HTAP) system.

    Solutions

    • Apache Airflow: Replaced ADF, providing Python-based automation for seamless data workflows.
    • Apache Kafka: Enabled real-time event streaming and seamless data integration.
    • Apache Ignite: Used as an HTAP database, significantly speeding up queries by 100x.
    • Apache Spark (Databricks) : Continued to process large-scale aggregates (e.g., player positioning analysis).
    • Azure API Gateway & Kubernetes API Endpoints: Enhanced scalability and serverless infrastructure management.
    MCL_Sports_Analytics_W

    Benefits

    • 100x Faster Query Execution (via Apache Ignite’s in-memory capabilities).
    • Real-Time Data Processing (with Apache Kafka & Ignite integration).
    • Reduced Infrastructure Costs (by replacing Azure ADF with Apache Airflow).
    • Scalable & Flexible Architecture (ASF projects enabled horizontal scalability).
    • Improved Workflow Efficiency (Python-based automation simplified orchestration).

    Conclusion

    By leveraging Azure Data Engineering services, the shipping company transformed its data management and analytics, empowering informed decisions, operational efficiency, and business growth.

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