Compare Netezza Analytics vs. Spark SQL

Netezza Analytics is ranked 4th in Hadoop with 5 reviews while Spark SQL is ranked 7th in Hadoop with 3 reviews. Netezza Analytics is rated 7.4, while Spark SQL is rated 6.6. The top reviewer of Netezza Analytics writes "A high-performance solution to create reports from our data mart, but the scalability needs improvement". On the other hand, the top reviewer of Spark SQL writes "GUI could be improved. Useful for speedily processing big data". Netezza Analytics is most compared with Apache Spark, Cloudera Distribution for Hadoop and Spark SQL, whereas Spark SQL is most compared with Informatica Big Data Parser, Apache Spark and AtScale Adaptive Analytics (A3). See our Netezza Analytics vs. Spark SQL report.
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Netezza Analytics Logo
670 views|405 comparisons
Spark SQL Logo
627 views|492 comparisons
Find out what your peers are saying about Netezza Analytics vs. Spark SQL and other solutions. Updated: March 2020.
406,860 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 need for administration involvement is quite limited on the solution.The most valuable feature is the performance.For me, as an end-user, everything that I do on the solution is simple, clear, and understandable.The performance of the solution is its most valuable feature. The solution is easy to administer as well. It's very user-friendly. On the technical side, the architecture is simple to understand and you don't need too many administrators to handle the solution.

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The speed of getting data.Overall the solution is excellent.The stability was fine. It behaved as expected.

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Cons
I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life.The Analytics feature should be simplified.The solution could implement more reporting tools and networking utilities.The hardware has a risk of failure. They need to improve this.This product is being discontinued from IBM, and I would like to have some kind of upgrade available.The most valuable features of this solution are robustness and support.

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Anything to improve the GUI would be helpful.The solution needs to include graphing capabilities. Including financial charts would help improve everything overall.In the next release, maybe the visualization of some command-line features could be added.

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Pricing and Cost Advice
The annual licensing fees are twenty-two percent of the product cost.

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Ranking
4th
out of 24 in Hadoop
Views
670
Comparisons
405
Reviews
5
Average Words per Review
419
Avg. Rating
7.4
7th
out of 24 in Hadoop
Views
627
Comparisons
492
Reviews
3
Average Words per Review
344
Avg. Rating
6.7
Top Comparisons
Compared 66% of the time.
Compared 14% of the time.
Compared 22% of the time.
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IBM
Apache
Overview
IBM Netezza Analytics is an embedded, purpose-built, advanced analytics platform that empowers analytic enterprises to meet and exceed their business demands. As features, it can predict with more accuracy, deliver predictions faster and respond rapidly to changes.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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Find out what your peers are saying about Netezza Analytics vs. Spark SQL and other solutions. Updated: March 2020.
406,860 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.