Compare MapR vs. Spark SQL

MapR is ranked 4th in Hadoop with 2 reviews while Spark SQL is ranked 7th in Hadoop with 1 review. MapR is rated 8.6, while Spark SQL is rated 8.0. The top reviewer of MapR writes "Because of their POSIX compliant file system, they can support read/write files over the WORM storage of their competitors". On the other hand, the top reviewer of Spark SQL writes "A good stable and scalable solution for processing big data". MapR is most compared with Amazon EMR, Hortonworks Data Platform and Cloudera Distribution for Hadoop, whereas Spark SQL is most compared with Apache Spark, Informatica Big Data Parser and AtScale.
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MapR Logo
Read 2 MapR reviews.
2,824 views|1,600 comparisons
Spark SQL Logo
637 views|499 comparisons
Most Helpful Review
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Find out what your peers are saying about Apache, Cloudera, Hortonworks and others in Hadoop. Updated: November 2019.
378,397 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
The model creation was very interesting, especially with the libraries provided by the platform.

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The stability was fine. It behaved as expected.

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Cons
Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful.

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In the next release, maybe the visualization of some command-line features could be added.

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Ranking
4th
out of 24 in Hadoop
Views
2,824
Comparisons
1,600
Reviews
2
Average Words per Review
716
Avg. Rating
8.5
7th
out of 24 in Hadoop
Views
637
Comparisons
499
Reviews
1
Average Words per Review
226
Avg. Rating
8.0
Top Comparisons
Compared 22% of the time.
Compared 20% of the time.
Compared 27% of the time.
Compared 15% of the time.
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MapR
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Apache
Overview
MapR MapReduce software makes Apache Hadoop more affordable and easier to use for big data analytics, business intelligence, distributed computing, and more.Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.
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Sample Customers
Valence Health, Goodgame Studios, Pico, Terbium Labs, sovrn, Harte Hanks, Quantium, Razorsight, Novartis, Experian, Dentsu ix, Pontis Transitions, DataSong, Return Path, RAPP, HP
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Find out what your peers are saying about Apache, Cloudera, Hortonworks and others in Hadoop. Updated: November 2019.
378,397 professionals have used our research since 2012.
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.
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