Compare Apache Spark vs. MapR

Apache Spark is ranked 1st in Hadoop with 7 reviews while MapR is ranked 4th in Hadoop with 2 reviews. Apache Spark is rated 8.0, while MapR is rated 8.6. The top reviewer of Apache Spark writes "Fast performance and has an easy initial setup". On the other hand, 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". Apache Spark is most compared with Spring Boot, AWS Lambda and Azure Stream Analytics, whereas MapR is most compared with Amazon EMR, Hortonworks Data Platform and Cloudera Distribution for Hadoop. See our Apache Spark vs. MapR report.
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11,254 views|9,257 comparisons
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Read 2 MapR reviews.
2,824 views|1,600 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Spark vs. MapR and other solutions. Updated: November 2019.
376,855 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:

The most valuable feature of this solution is its capacity for processing large amounts of data.I found the solution stable. We haven't had any problems with it.The scalability has been the most valuable aspect of the solution.Features include machine learning, real time streaming, and data processing.The fault tolerant feature is provided.It provides a scalable machine learning library.With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware.

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

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When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster.The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive.It should support more programming languages.Needs to provide an internal schedule to schedule spark jobs with monitoring capability.Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing.

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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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out of 24 in Hadoop
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out of 24 in Hadoop
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Top Comparisons
Compared 32% of the time.
Compared 12% of the time.
Compared 22% of the time.
Compared 20% of the time.
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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

MapR MapReduce software makes Apache Hadoop more affordable and easier to use for big data analytics, business intelligence, distributed computing, and more.
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Sample Customers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab,, Baidu, Alibaba Taobao, EURECOM, Hitachi SolutionsValence Health, Goodgame Studios, Pico, Terbium Labs, sovrn, Harte Hanks, Quantium, Razorsight, Novartis, Experian, Dentsu ix, Pontis Transitions, DataSong, Return Path, RAPP, HP
Top Industries
Financial Services Firm29%
Software R&D Company29%
Non Profit14%
Marketing Services Firm14%
Software R&D Company30%
Comms Service Provider13%
Financial Services Firm10%
Media Company8%
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Find out what your peers are saying about Apache Spark vs. MapR and other solutions. Updated: November 2019.
376,855 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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