What are the advantages of FusionInsight Miner?

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Compared with conventional data mining platforms, FusionInsight Miner can process larger amounts of data, provide more accurate model prediction without data sampling, process data with more dimensions, and provide more room for service imagination.

Other related questions:
What are the advantages of the FusionInsight Hive?
Hive provides the following advantageous functions: 1. Analyzes massive structured data and summarizes analysis results. 2. Allows complex MapReduce jobs to be compiled in SQL languages. 3. Supports flexible data storage formats, including JavaScript object notation (JSON), comma separated values (CSV), TextFile, RCFile, and SequenceFile.

What are the advantages of FusionInsight Miner in data processing?
Compared with conventional data mining platforms, FusionInsight Miner can process larger amounts of data, provide more accurate model prediction without data sampling, process data with more dimensions, and provide more room for service imagination.

What Are the Features and Advantages of Workspace?

A web-based management console is provided for you to flexibly enable and disable Workspace that works out of the box.

Workspace provides the following features and advantages:

  • Workspace can be provisioned and deployed quickly whereas deploying conventional private desktop clouds takes several days.
  • Administrators can manage hundreds of desktops at the same time using the web page.
  • Multiple desktop package specifications are available for you to choose the most appropriate one (vCPUs, memory, system disks, and data disks).
  • The password can be reset if a user forgets it.
  • Encrypted remote access, isolated user resources, and network and peripheral security control are used to ensure high security of data access.

What are the advantages of Spark?
1. Spark improves the data processing capability by as much as 10 to 100 times over the capabilities of MapReduce. It does this by using distributed memory computing and a Directed Acyclic Graph (DAG) engine. 2. Spark supports multiple development languages including Scala, Java, and Python. It supports dozens of highly abstract operators. This flexibility facilitates the construction of distributed data processing applications. 3. Spark provides one-stop data processing capability by working with SQL, Streaming, MLlib, and GraphX to form data processing stacks. 4. Spark can run in standalone, Mesos, or Yarn mode. It can access HDFS, HBase, and Hive data sources. It supports smooth swift from MapReduce. All of these functions allow Spark to easily fit into the Hadoop ecosystem.

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