Compare Apache Spark vs. MapR

Apache Spark is ranked 1st in Hadoop with 11 reviews while MapR is ranked 5th in Hadoop with 1 review. Apache Spark is rated 8.0, while MapR is rated 8.0. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, the top reviewer of MapR writes "Enables us to create preview models and has good scalability and stability ". Apache Spark is most compared with Spring Boot, Azure Stream Analytics and AWS Lambda, whereas MapR is most compared with Amazon EMR, Cloudera Distribution for Hadoop and Apache Spark.
Cancel
You must select at least 2 products to compare!
Apache Spark Logo
11,062 views|9,293 comparisons
MapR Logo
Read 1 MapR review.
2,738 views|1,549 comparisons
Most Helpful Review
Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: March 2020.
405,659 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 processing time is very much improved over the data warehouse solution that we were using.The main feature that we find valuable is that it is very fast.The features we find most valuable are the machine learning, data learning, and Spark Analytics.I feel the streaming is its best feature.The solution is very stable.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.

Read more »

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

Read more »

Cons
I would like to see integration with data science platforms to optimize the processing capability for these tasks.We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources.The solution needs to optimize shuffling between workers.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.

Read more »

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

Read more »

report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
405,659 professionals have used our research since 2012.
Ranking
1st
out of 24 in Hadoop
Views
11,062
Comparisons
9,293
Reviews
11
Average Words per Review
305
Avg. Rating
8.0
5th
out of 24 in Hadoop
Views
2,738
Comparisons
1,549
Reviews
2
Average Words per Review
715
Avg. Rating
8.5
Top Comparisons
Compared 35% of the time.
Compared 9% of the time.
Compared 22% of the time.
Compared 18% of the time.
Learn
Apache
MapR
Video Not Available
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

MapR MapReduce software makes Apache Hadoop more affordable and easier to use for big data analytics, business intelligence, distributed computing, and more.
Offer
Learn more about Apache Spark
Learn more about MapR
Sample Customers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, 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
REVIEWERS
Software R&D Company29%
Financial Services Firm29%
Non Profit14%
Marketing Services Firm14%
VISITORS READING REVIEWS
Software R&D Company34%
Media Company12%
Comms Service Provider11%
Financial Services Firm8%
No Data Available
Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: March 2020.
405,659 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.