Designing Resilient Data Pipelines for Heterogeneous Retail Data Sources Using Distributed Big Data Engineering Frameworks

Schnitzler Thomas Musil Journal

Designing Resilient Data Pipelines for Heterogeneous Retail Data Sources Using Distributed Big Data Engineering Frameworks

Authors

Schnitzler Thomas Musil Journal

Senior Data Engineer – Real-Time Retail Analytics & Distributed Data Systems, United Kingdom

Keywords:

Data pipeline, retail analytics, distributed systems, fault tolerance, Apache Spark, Kafka, big data frameworks, heterogeneous data, data resilience, real-time processing

Synopsis

The proliferation of heterogeneous data sources in modern retail environments presents a critical challenge to the design and operation of scalable, fault-tolerant data pipelines. This paper investigates the application of distributed big data engineering frameworks—such as Apache Kafka, Apache Spark, and Apache Flink—in orchestrating resilient data pipelines capable of handling high-velocity, high-volume retail data streams. Our approach includes the integration of real-time ingestion, batch processing, and schema management layers to ensure fault tolerance and consistency. The study explores various failure scenarios and demonstrates how distributed architectures can mitigate downtime and ensure data integrity. A prototype system was implemented and evaluated across multiple failure simulations, showing significant improvement in system availability and data consistency compared to traditional ETL-based pipelines.

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