Compare Amazon EMR vs. Spark SQL

Amazon EMR is ranked 4th in Hadoop with 1 review while Spark SQL which is ranked 12th in Hadoop with 1 review. Amazon EMR is rated 9.0, while Spark SQL is rated 9.0. The top reviewer of Amazon EMR writes "Ability to easily and quickly resize the cluster is what really makes it stand out". On the other hand, the top reviewer of Spark SQL writes "A good stable and scalable solution for processing big data". Amazon EMR is most compared with Hortonworks Data Platform, Cloudera Distribution for Hadoop and Apache Spark, whereas Spark SQL is most compared with Apache Spark, AtScale and Informatica Big Data Parser.
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Amazon EMR Logo
5,090 views|2,361 comparisons
Spark SQL Logo
808 views|520 comparisons
Most Helpful Review
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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:

Pros
The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions.

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The stability was fine. It behaved as expected.

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Cons
There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange.

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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
5,090
Comparisons
2,361
Reviews
1
Average Words per Review
1,386
Avg. Rating
9.0
12th
out of 24 in Hadoop
Views
808
Comparisons
520
Reviews
0
Average Words per Review
0
Avg. Rating
N/A
Top Comparisons
Compared 11% of the time.
Compared 23% of the time.
Compared 20% of the time.
Also Known As
Amazon Elastic MapReduce
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Apache
Overview
Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data. Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances.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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