Optimized Data Lake Architectures for Scalable ML Workflows

Abstract

The exponential growth of data and the increasing demand for real-time, large-scale machine learning (ML) applications have challenged traditional data storage and processing architectures. Data lakes have emerged as a flexible and cost-effective solution for storing massive amounts of heterogeneous data. However, without optimization, data lakes can become inefficient, leading to performance bottlenecks in ML workflows. This paper presents an in-depth exploration of optimized data lake architectures tailored for scalable ML pipelines. We examine architectural best practices, including the separation of storage and compute, data versioning, metadata management, and the use of open table formats like Delta Lake, Apache Iceberg, and Apache Hudi. Furthermore, we propose a reference architecture that integrates modern orchestration tools, feature stores, and distributed compute engines to streamline the ML lifecycle from data ingestion to model deployment. Performance benchmarks and use cases demonstrate the proposed architecture's effectiveness in improving scalability, maintainability, and end-to-end ML throughput.