novatechflow | Alexander Alten

This article explains how hyper-converged data platforms built with OpenStack provide a flexible and scalable foundation for Hadoop and streaming workloads. It covers the differences between static and on-demand Hadoop clusters, the role of HDFS on block storage, how network design and storage layout affect performance, and why in-memory layers like Alluxio can accelerate analytical and IoT workloads. The piece also outlines best-practice architectures for compute, storage, and networking in modern private and hybrid data platforms. Hyper-converged infrastructures have become a mainstream choice for enterprise data platforms. Back in 2016, more than half of surveyed companies were already adopting HCI. Today, the trend has continued, especially as organizations need elastic compute and storage for Hadoop, Spark, and new streaming workloads. Hyper-Converged Data Platforms for Hadoop and Streaming Workloads Hadoop and modern analytical stacks benefit from flexible resource ...