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DataTorrent [EOL] vs HPE Ezmeral Data Fabric comparison

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DataTorrent [EOL]

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HPE Ezmeral Data Fabric

Read 12 HPE Ezmeral Data Fabric reviews
1,653 views|1,034 comparisons
100% willing to recommend
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Buyer's Guide
Hadoop
April 2024
Executive Summary

We performed a comparison between DataTorrent [EOL] and HPE Ezmeral Data Fabric based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.
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Featured Review
Use DataTorrent [EOL]?
Arnab Chatterjee
It's flexible and easily accessible across multiple locations, but the upgrade process is complicated
Quotes From Members
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Pricing and Cost Advice
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  • "HPE is flexible with you if you are an existing customer. They offer different models that might be beneficial for your organization. It all depends on how you negotiate."
  • "The tool's price is cheap and based on a usage basis. The solution's licensing costs are yearly and there are no extra costs."
  • "There is a need for my company to pay for the licensing costs of the solution."

    • More HPE Ezmeral Data Fabric Pricing and Cost Advice →

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    What do you like most about HPE Ezmeral Data Fabric?
    Top Answer:It is a stable solution...It is a scalable solution.
    Read all 5 answers   →
    What needs improvement with HPE Ezmeral Data Fabric?
    Top Answer:There are some drawbacks in HPE Ezmeral Data Fabric when it comes to the interoperability part. HPE Ezmeral Data Fabric is not compatible with third-party tools. For example, HPE Ezmeral Data Fabric… more »
    Read all 5 answers   →
    What is your primary use case for HPE Ezmeral Data Fabric?
    Top Answer:The main purpose of HPE Ezmeral Data Fabric for me is that it acts as a database. In my company, we store our data with the help of HPE Ezmeral Data Fabric. It is possible to use Spark engine with HPE… more »
    Read all 4 answers   →
    Ranking
    Unranked
    In Hadoop
    5th
    out of 22 in Hadoop
    Views
    1,653
    Comparisons
    1,034
    Reviews
    4
    Average Words per Review
    550
    Rating
    7.8
    Comparisons
    Also Known As
    MapR, MapR Data Platform
    Learn More
    DataTorrent
    Hewlett Packard Enterprise
    Video Not Available
    Overview
    Information Not Available

    Forward-leaning companies win market share because they leverage data more effectively than their competitors. Unlock the potential of your data assets with HPE Ezmeral Data Fabric (formerly MapR Data Platform). Empower your data science, analytics, and business teams by simplifying data management on a globally distributed scale. All with enterprise-grade reliability, security, and performance.

    Sample Customers
    GE Predix Cloud, PubMatic, Capital One, SilverLink Sensor Network
    Valence Health, Goodgame Studios, Pico, Terbium Labs, sovrn, Harte Hanks, Quantium, Razorsight, Novartis, Experian, Dentsu ix, Pontis Transitions, DataSong, Return Path, RAPP, HP
    Top Industries
    No Data Available
    VISITORS READING REVIEWS
    Financial Services Firm17%
    Computer Software Company17%
    Manufacturing Company7%
    Comms Service Provider7%
    Company Size
    No Data Available
    REVIEWERS
    Small Business36%
    Large Enterprise64%
    VISITORS READING REVIEWS
    Small Business24%
    Midsize Enterprise11%
    Large Enterprise65%
    Buyer's Guide
    Hadoop
    April 2024
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    Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: April 2024.
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    DataTorrent [EOL] doesn't meet the minimum requirements to be ranked in Hadoop while HPE Ezmeral Data Fabric is ranked 5th in Hadoop with 12 reviews. DataTorrent [EOL] is rated 0.0, while HPE Ezmeral Data Fabric is rated 8.0. On the other hand, the top reviewer of HPE Ezmeral Data Fabric writes "It's flexible and easily accessible across multiple locations, but the upgrade process is complicated". DataTorrent [EOL] is most compared with , whereas HPE Ezmeral Data Fabric is most compared with Cloudera Distribution for Hadoop, Amazon EMR, IBM Spectrum Computing, MongoDB and BlueData.

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    We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.