14 Ratings
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Score 6.9 out of 100
17 Ratings
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Score 9.4 out of 100

Feature Set Ratings

    Platform Connectivity

    7.5

    Data Science Workbench

    75%

    HPE Ezmeral Data Fabric (MapR)

    Feature Set Not Supported
    N/A
    Cloudera Data Science Workbench ranks higher in 4/4 features

    Connect to Multiple Data Sources

    7.0
    70%
    2 Ratings
    N/A
    0 Ratings

    Extend Existing Data Sources

    8.0
    80%
    2 Ratings
    N/A
    0 Ratings

    Automatic Data Format Detection

    7.0
    70%
    2 Ratings
    N/A
    0 Ratings

    MDM Integration

    8.0
    80%
    2 Ratings
    N/A
    0 Ratings

    Data Exploration

    7.6

    Data Science Workbench

    76%

    HPE Ezmeral Data Fabric (MapR)

    Feature Set Not Supported
    N/A
    Cloudera Data Science Workbench ranks higher in 2/2 features

    Visualization

    7.1
    71%
    2 Ratings
    N/A
    0 Ratings

    Interactive Data Analysis

    8.0
    80%
    2 Ratings
    N/A
    0 Ratings

    Data Preparation

    7.8

    Data Science Workbench

    78%

    HPE Ezmeral Data Fabric (MapR)

    Feature Set Not Supported
    N/A
    Cloudera Data Science Workbench ranks higher in 4/4 features

    Interactive Data Cleaning and Enrichment

    7.0
    70%
    2 Ratings
    N/A
    0 Ratings

    Data Transformations

    8.0
    80%
    2 Ratings
    N/A
    0 Ratings

    Data Encryption

    8.0
    80%
    2 Ratings
    N/A
    0 Ratings

    Built-in Processors

    8.0
    80%
    2 Ratings
    N/A
    0 Ratings

    Platform Data Modeling

    7.6

    Data Science Workbench

    76%

    HPE Ezmeral Data Fabric (MapR)

    Feature Set Not Supported
    N/A
    Cloudera Data Science Workbench ranks higher in 4/4 features

    Multiple Model Development Languages and Tools

    8.0
    80%
    2 Ratings
    N/A
    0 Ratings

    Automated Machine Learning

    7.0
    70%
    1 Rating
    N/A
    0 Ratings

    Single platform for multiple model development

    7.1
    71%
    2 Ratings
    N/A
    0 Ratings

    Self-Service Model Delivery

    8.1
    81%
    2 Ratings
    N/A
    0 Ratings

    Model Deployment

    8.0

    Data Science Workbench

    80%

    HPE Ezmeral Data Fabric (MapR)

    Feature Set Not Supported
    N/A
    Cloudera Data Science Workbench ranks higher in 2/2 features

    Flexible Model Publishing Options

    8.1
    81%
    2 Ratings
    N/A
    0 Ratings

    Security, Governance, and Cost Controls

    7.8
    78%
    2 Ratings
    N/A
    0 Ratings

    Attribute Ratings

    • Cloudera Data Science Workbench is rated higher in 1 area: Likelihood to Recommend

    Likelihood to Recommend

    9.0

    Data Science Workbench

    90%
    3 Ratings
    7.2

    HPE Ezmeral Data Fabric (MapR)

    72%
    4 Ratings

    Support Rating

    7.8

    Data Science Workbench

    78%
    3 Ratings

    HPE Ezmeral Data Fabric (MapR)

    N/A
    0 Ratings

    Likelihood to Recommend

    Cloudera

    Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.
    Read full review

    Hewlett Packard Enterprise

    MapR is more well-suited for people who know what they are doing. I consider MapR the Hadoop distribution professionals use.
    Read full review

    Pros

    Cloudera

    • One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
    • For larger organizations/teams, it lets you be self reliant
    • As it sits on your cluster, it has very easy access of all the data on the HDFS
    • Linking with Github is a very good way to keep the code versions intact
    Read full review

    Hewlett Packard Enterprise

    • MapR had very fast I/O throughput. The write speed was several times faster than what we could achieve with the other Hadoop vendors (Cloudera and Hortonworks). This is because MapR does not use HDFS, which is essentially a "meta filesystem". HDFS is built on top of the filesystem provided by the OS. MapR has their filesystem called MapR-FS, which is a true filesystem and accesses the raw disk drives.
    • The MapR filesystem is very easy to integrate with other Linux filesystems. When working with HDFS from Apache Hadoop, you usually have to use either the HDFS API or various Hadoop/HDFS command line utilities to interact with HDFS. You cannot use command line utilities native to the host operation system, which is usually Linux. At least, it is not easily done without setting up NFS, gateways, etc. With MapR-FS, you can mount the filesystem within Linux and use the standard Unix commands to manipulate files.
    • The HBase distribution provided by MapR is very similar to the Apache HBase distribution. Cloudera and Hortonworks add GUIs and other various tools on top of their HBase distributions. The MapR HBase distribution is very similar to the Apache distribution, which is nice if you are more accustomed to using Apache HBase.
    Read full review

    Cons

    Cloudera

    • Installation is difficult.
    • Upgrades are difficult.
    • Licensing options are not flexible.
    Read full review

    Hewlett Packard Enterprise

    • It takes time to get latest versions of Apache ecosystem tools released as it has to be adapted.
    • When you have issues related to Mapr-FS or Mapr Tables, its hard to figure them out by ourselves.
    • Sometime new ecosystem tools versions are released without proper QA.
    Read full review

    Pricing Details

    Data Science Workbench

    Starting Price

    Editions & Modules

    Data Science Workbench editions and modules pricing
    EditionModules

    Footnotes

      Offerings

      Free Trial
      Free/Freemium Version
      Premium Consulting/Integration Services

      Entry-level set up fee?

      No setup fee

      Additional Details

      HPE Ezmeral Data Fabric (MapR)

      Starting Price

      Editions & Modules

      HPE Ezmeral Data Fabric (MapR) editions and modules pricing
      EditionModules

      Footnotes

        Offerings

        Free Trial
        Free/Freemium Version
        Premium Consulting/Integration Services

        Entry-level set up fee?

        No setup fee

        Additional Details

        Support Rating

        Cloudera

        Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.
        Read full review

        Hewlett Packard Enterprise

        No answers on this topic

        Alternatives Considered

        Cloudera

        Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
        Read full review

        Hewlett Packard Enterprise

        I don't believe there is as much support for MapR yet compared to other more widely known products.
        Read full review

        Return on Investment

        Cloudera

        • Paid off for demonstration purposes.
        Read full review

        Hewlett Packard Enterprise

        • Increased employee efficiency for sure. Our clients have various levels of expertise in their deployment and user teams, and we never receive complaints about MapR.
        • MapR is used by one of our financial services clients who uses it for fraud detection and user pattern analysis. They are able to turn around data much faster than they previously had with in-house applications
        Read full review

        Add comparison