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

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Manage episode 366339410 series 3474670
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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.

  continue reading

126 episodes

Artwork
iconShare
 
Manage episode 366339410 series 3474670
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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.

  continue reading

126 episodes

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