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Netezza Analytics vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #11, while Apache is ranked #4 with an average rating of 8.3. IBM holds a 1.5% mindshare in H, compared to Apache’s 9.9% mindshare. Additionally, 76% of IBM users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.
Netezza Analytics
Read 11 Netezza Analytics reviews
  • 175 Views
  • 85 Comparison Views
76% willing to recommend
Spark SQL
Read 14 Spark SQL reviews
  • 1,046 Views
  • 777 Comparison Views
85% willing to recommend
 

Comparison Buyer's Guide

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Executive Summary
We performed a comparison between Netezza Analytics and Spark SQL based on real PeerSpot user reviews.

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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To learn more, read our detailed Netezza Analytics vs. Spark SQL Report (Updated: December 2024).
Buyer's Guide
Netezza Analytics vs. Spark SQL
December 2024
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Categories and Ranking

Netezza Analytics
Ranking in Hadoop
11th
Average Rating
7.4
Number of Reviews
11
Ranking in other categories
No ranking in other categories
Spark SQL
Ranking in Hadoop
4th
Average Rating
7.8
Reviews Sentiment
7.6
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the Hadoop category, the mindshare of Netezza Analytics is 1.5%, up from 1.2% compared to the previous year. The mindshare of Spark SQL is 9.9%, down from 12.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

Asish Govind - PeerSpot reviewer
Stable with limited administration needed but it's coming up on its end of life
I'm not sure if Netezza offers a cloud version of the solution or not, but if they don't they should. Most companies are focused on moving towards the cloud. If it was on a cloud it would offer certain scalability and performance aspects it can't offer as a physical appliance. I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life.
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Lucas Dreyer - PeerSpot reviewer
Processing solution used for data engineering and transformation with the ability to process large datasets
It takes a bit of time to get used to using this solution versus Panda as it has a steep learning curve. You need quite a high level of skill with SQL in general to use this solution. If SQL is not someone's primary language, they might find it difficult to get used to. This solution could be improved if there was a bridge between Panda and Spark SQL such as translating from Panda operations to SQL and then working with those queries that are generated. In a future release, it would be useful to have a real time dashboard versus batch updates to Power BI.
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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 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."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"Speed contributes to large capacity."
"The need for administration involvement is quite limited on the solution."
"It is a back end for our SSIS, MicroStrategy,, Tableau. All of these are connecting to get the data. To do so we are also using our analytics which is built on the data."
"The most valuable feature is the performance."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"Overall the solution is excellent."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"I find the Thrift connection valuable."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
"This solution is useful to leverage within a distributed ecosystem."
More Spark SQL pros
 

Cons

"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"The Analytics feature should be simplified."
"The most valuable features of this solution are robustness and support."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"The solution could implement more reporting tools and networking utilities."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
More Netezza Analytics cons
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"In the next release, maybe the visualization of some command-line features could be added."
"There should be better integration with other solutions."
"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."
"Anything to improve the GUI would be helpful."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"There are many inconsistencies in syntax for the different querying tasks."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
More Spark SQL cons
 

Pricing and Cost Advice

"The annual licensing fees are twenty-two percent of the product cost."
"Expensive to maintain compared to other solutions."
"For me, mainly, it reduces my costs. It's not only the appliance cost. There are also support costs and a maintenance costs. It does reduce the costs very drastically."
"There is no license or subscription for this solution."
"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."
"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."
"The solution is open-sourced and free."
"The solution is bundled with Palantir Foundry at no extra charge."
"We use the open-source version, so we do not have direct support from Apache."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
27%
Computer Software Company
15%
Manufacturing Company
7%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

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What do you like most about Spark SQL?
Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.
See all answers
What is your experience regarding pricing and costs for Spark SQL?
We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.
See all answers
What needs improvement with Spark SQL?
In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...
See all answers
 

Comparisons

No data available
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HPE Ezmeral Data Fabric vs Spark SQL Logo
HPE Ezmeral Data Fabric vs Spark SQL
Compared 11% of the time
IBM Db2 Big SQL vs Spark SQL Logo
IBM Db2 Big SQL vs Spark SQL
Compared 9% of the time
More Spark SQL Competitors
 

Product Reports

Buyer's Guide
Netezza Analytics
December 2024
Netezza Analytics reportDownload Netezza Analytics product report
Buyer's Guide
Spark SQL
November 2024
Spark SQL reportDownload Spark SQL product report
 

Learn More

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.
 

Sample Customers

A leading online advertising network
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Buyer's Guide
Netezza Analytics vs. Spark SQL
December 2024
Free Report: Netezza Analytics vs. Spark SQL
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