From Hadoop to Cloud: Why and How to Decouple Storage and Compute in Big Data Platforms

Data Science Tech Brief By HackerNoon

This story was originally published on HackerNoon at: https://hackernoon.com/from-hadoop-to-cloud-why-and-how-to-decouple-storage-and-compute-in-big-data-platforms.
This article reviews the Hadoop architecture, discusses the importance and feasibility of storage-compute decoupling, and explores available market solutions.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data, #open-source, #big-data, #distributed-systems, #distributed-file-systems, #object-storage, #cloud-native, #software-architecture, and more.
This story was written by: @suave. Learn more about this writer by checking @suave's about page, and for more stories, please visit hackernoon.com.
Initially, Hadoop integrated storage and compute, but the emergence of cloud computing led to a separation of these components. Object storage emerged as an alternative to HDFS but had limitations. To complement these limitations, JuiceFS, an open source distributed file system, offers cost-effective solutions for data-intensive scenarios like computation, analysis, and training. The decision to adopt storage-compute separation depends on factors like scalability, performance, cost, and compatibility.