Designing Distributed Artifact Ingestion Platform for Analytics & Machine Learning

Abstract

Demand for efficient large-scale heterogeneous distributed data ingestion pipelines for transforming and publishing data that is essential for advanced analytics and machine learning models, have gained substantial importance. Services increasingly rely on near-real-time signals to accurately identify or predict customer behavior, sentiments, anomalies facilitating data-driven decision-making. This paper delves into the forefront of distributed and parallel computing, examining the latest advancements in storage, query, and ingestion methodologies. Furthermore, it systematically assesses cutting-edge tools designed for both periodic and real-time analysis of heterogeneous data. “The data quality is more important than the Machine Learning model itself ”. Achieving precision in decision-making or generating precise output from the Machine Learning models necessitates a keen focus on input data quality and consistency. Building a robust ingestion platform for handling hundreds of Gigabytes/Petabytes per day involves a comprehensive understanding of the overarching architecture, the intricacies of involved components, and the unique challenges within these ecosystems. Building a service platform demands thoughtful consideration and resolution of key aspects, including a scalable ingestion handler, a flexible and fault-tolerant data processing library, a highly scalable and resilient event system, an analytics/reporting platform, machine learning platform, and robust application health and security measures. This paper delves into the overall architecture, explicates design choices, and imparts insights into best practices for implementing and maintaining such a platform, leveraging contemporary tools. The discussion encompasses critical aspects of the platform's functionality, emphasizing the need for scalability, fbility, resilience, and security to meet the demands of modern data-driven decision-making scenarios.