Compare Spark SQL vs. Splice Machine

Spark SQL is ranked 7th in Hadoop with 2 reviews while Splice Machine is ranked 14th in Hadoop. Spark SQL is rated 7.6, while Splice Machine is rated 0. The top reviewer of Spark SQL writes "An excellent solution that continues to mature but needs graphing capabilities". On the other hand, Spark SQL is most compared with Informatica Big Data Parser, Apache Spark and AtScale Adaptive Analytics (A3), whereas Splice Machine is most compared with JethroData and AtScale Adaptive Analytics (A3).
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Spark SQL Logo
627 views|502 comparisons
Splice Machine Logo
421 views|266 comparisons
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Ranking
7th
out of 24 in Hadoop
Views
627
Comparisons
502
Reviews
1
Average Words per Review
220
Avg. Rating
8.0
14th
out of 24 in Hadoop
Views
421
Comparisons
266
Reviews
0
Average Words per Review
0
Avg. Rating
N/A
Top Comparisons
Compared 20% of the time.
Compared 63% of the time.
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Overview
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.Splice Machine is an application platform built to power predictive applications that manage operational processes, large-scale IoT infrastructures, analytical processing and machine learning. It is a Hybrid RDBMS that can be delivered as a cloud service, or used on premise through our Enterprise Edition or Community Edition.
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Find out what your peers are saying about Apache, Cloudera, Hortonworks and others in Hadoop. Updated: February 2020.
398,890 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.