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Apache Spark vs HPE Ezmeral Data Fabric comparison

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Apache Spark

Read 12 Apache Spark reviews
11,458 views|9,075 comparisons
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HPE Ezmeral Data Fabric

2,309 views|1,439 comparisons
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Buyer's Guide
Hadoop
November 2022
Executive Summary

We performed a comparison between Apache Spark and HPE Ezmeral Data Fabric based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon and others in Hadoop.
To learn more, read our detailed Hadoop Report (Updated: November 2022).
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Featured Review
NitinKumar
Easy to code, fast, open-source, very scalable, and great for big data
Spark has been at the forefront of data processing engine. I have used Apache Spark for multiple projects for different clients. It is an excellent... Read more →
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Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pricing and Cost Advice
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

    • More Apache Spark Pricing and Cost Advice →

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    Questions from the Community
    Apache Spark without Hadoop -- Is this recommended?
    Top Answer:I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Read all 3 answers   →
    What do you like most about Apache Spark?
    Top Answer:There's a lot of functionality.
    Read all 21 answers   →
    What is your experience regarding pricing and costs for Apache Spark?
    Top Answer:It's an open-source product. I don't know much about the licensing aspect.
    Read all 10 answers   →
    What do you like most about HPE Ezmeral Data Fabric?
    Top Answer:The model creation was very interesting, especially with the libraries provided by the platform.
    What needs improvement with HPE Ezmeral Data Fabric?
    Top Answer:The interface part, what I'm calling the integration part, could be improved. Although it was able to connect to Hadoop, pocket files and so on. For example, providing some API endpoints for capturing… more »
    What is your primary use case for HPE Ezmeral Data Fabric?
    Top Answer:Our primary use case is for creating preview models.
    Ranking
    1st
    out of 22 in Hadoop
    Views
    11,458
    Comparisons
    9,075
    Reviews
    12
    Average Words per Review
    393
    Rating
    8.1
    7th
    out of 22 in Hadoop
    Views
    2,309
    Comparisons
    1,439
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    Comparisons
    Also Known As
    MapR, MapR Data Platform
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    Apache
    Hewlett Packard Enterprise
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    Overview

    Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

    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.

    Offer
    Learn more about Apache Spark
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    Learn more about HPE Ezmeral Data Fabric
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    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    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
    REVIEWERS
    Computer Software Company29%
    Financial Services Firm29%
    Marketing Services Firm7%
    Non Profit7%
    VISITORS READING REVIEWS
    Financial Services Firm19%
    Computer Software Company17%
    Comms Service Provider12%
    Retailer6%
    VISITORS READING REVIEWS
    Computer Software Company21%
    Financial Services Firm20%
    Comms Service Provider13%
    Manufacturing Company7%
    Company Size
    REVIEWERS
    Small Business42%
    Midsize Enterprise23%
    Large Enterprise35%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise12%
    Large Enterprise73%
    REVIEWERS
    Small Business38%
    Midsize Enterprise13%
    Large Enterprise50%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise13%
    Large Enterprise70%
    Buyer's Guide
    Hadoop
    November 2022
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    Apache Spark is ranked 1st in Hadoop with 12 reviews while HPE Ezmeral Data Fabric is ranked 7th in Hadoop. Apache Spark is rated 8.0, while HPE Ezmeral Data Fabric is rated 0.0. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and SAP HANA, whereas HPE Ezmeral Data Fabric is most compared with Cloudera Distribution for Hadoop, BlueData, IBM Spectrum Computing, Amazon EMR and Hortonworks Data Platform.

    See our list of best Hadoop vendors.

    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.