Compare Amazon EMR vs. Spark SQL

Amazon EMR is ranked 8th in Hadoop while Spark SQL is ranked 7th in Hadoop with 1 review. Amazon EMR is rated 0, while Spark SQL is rated 8.0. 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, Informatica Big Data Parser and AtScale.

Amazon EMR

Spark SQL

Amazon EMR

2,946 views|2,422 comparisons

Spark SQL

Read 1 Spark SQL review.

645 views|511 comparisons

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Ranking
8th out of 24 in Hadoop Views 2,946 Comparisons 2,422 Reviews 0 Average Words per Review 0 Avg. Rating N/A	7th out of 24 in Hadoop Views 645 Comparisons 511 Reviews 1 Average Words per Review 226 Avg. Rating 8.0
Top Comparisons
Hortonworks Data Platform vs. Amazon EMR Compared 37% of the time. Cloudera Distribution for Hadoop vs. Amazon EMR Compared 26% of the time. Apache Spark vs. Amazon EMR Compared 16% of the time. See more Amazon EMR competitors » + Add more products to compare	Apache Spark vs. Spark SQL Compared 27% of the time. Informatica Big Data Parser vs. Spark SQL Compared 18% of the time. AtScale vs. Spark SQL Compared 16% of the time. See more Spark SQL competitors » + Add more products to compare
Also Known As
Amazon Elastic MapReduce
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Amazon	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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Sample Customers
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