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IBM Netezza Performance Server vs Spark SQL comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Nov 30, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

IBM Netezza Performance Server
Ranking in Hadoop
7th
Average Rating
7.8
Reviews Sentiment
6.9
Number of Reviews
45
Ranking in other categories
Data Warehouse (13th)
Spark SQL
Ranking in Hadoop
5th
Average Rating
7.8
Reviews Sentiment
7.6
Number of Reviews
15
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2026, in the Hadoop category, the mindshare of IBM Netezza Performance Server is 5.0%, up from 2.0% compared to the previous year. The mindshare of Spark SQL is 6.6%, down from 10.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Spark SQL6.6%
IBM Netezza Performance Server5.0%
Other88.4%
Hadoop
 

Featured Reviews

Shiv Subramaniam Koduvayur - PeerSpot reviewer
Project Manager at MAF Retail
Parallel data processing streamlines operations while cost and cloud integration challenge adoption
The cost of the solution is on the more expensive side, which is a concern for me. Additionally, its promotion and interaction with cloud applications are limited. The cloud version is only available in AWS, and in the Middle East, it is not well-developed in the Azure environment. For the cost to be reduced, it should match competitors. Many features need to be incorporated on the cloud.
Kemal Duman - PeerSpot reviewer
Team Lead, Data Engineering at Nesine.com
Data pipelines have run faster and support flexible batch and streaming transformations
We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource management in Spark SQL should be better. It consumes more resources, which is normal. The main reason we switched from Spark is memory and CPU consumption. The major reason is the resource problem because the number of streaming jobs has been increasing in our company. That is why we considered resource management as a priority. Because of the resource consumption, I would say the development of Spark SQL is better. For development purposes, it is a top product and not difficult to work with, but resources are the major problem. We changed to Flink regardless of development time. Development time is less in Spark compared with Flink.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The most valuable feature is the performance."
"Speed contributes to large capacity."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"The data governance prospect... from what I've seen, that is a really powerful tool as well, to help with data lineage and keeping track of that."
"Distribution concurrency control."
"The need for administration involvement is quite limited on the solution."
"The underlying hardware that IBM provides with this appliance is made for a specific purpose, to serve performance on a large amount of data, and to do analytics as well. It is faster, when you compare it to any other product."
"The benefit is really because of the additional speed that we have and, truth be told, the more updated ETL processes and the revamped scheduler in general."
"The stability was fine. It behaved as expected."
"It is a stable solution."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"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."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"This solution is useful to leverage within a distributed ecosystem."
"Data validation and ease of use are the most valuable features."
 

Cons

"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"The hardware has a risk of failure. They need to improve this."
"We are not able to scale. The only way to scale is to get another appliance, but we have a customers who would need us to hydrate the data between the two appliances, and Netezza does not do that."
"IBM Netezza Performance Server could improve its interface, support for big data, and APA-based connectivity should be available."
"The Analytics feature should be simplified."
"Concurrency limit needs to be increased somewhat."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"There are many inconsistencies in syntax for the different querying tasks."
"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."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"Anything to improve the GUI would be helpful."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"There should be better integration with other solutions."
"SparkUI could have more advanced versions of the performance and the queries and all."
"I've experienced some incompatibilities when using the Delta Lake format."
 

Pricing and Cost Advice

"The solution has a yearly licensing fee, and users have to pay extra for support."
"Expensive to maintain compared to other solutions."
"The pricing is very expensive. It has a lot CPUs with a lot of components in it. It also has built-in redundancy for resiliency reasons."
"Netezza is a costly solution. It does serve a specific purpose but it's costlier than what's available in the market, if you go to the cloud."
"The annual licensing fees are twenty-two percent of the product cost."
"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."
"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."
"There is no license or subscription for this solution."
"The solution is bundled with Palantir Foundry at no extra charge."
"The solution is open-sourced and free."
"We use the open-source version, so we do not have direct support from Apache."
"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."
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Comparison Review

it_user232068 - PeerSpot reviewer
Senior Data Architect at a pharma/biotech company with 1,001-5,000 employees
Aug 5, 2015
Netezza vs. Teradata
Original published at https://www.linkedin.com/pulse/should-i-choose-net Two leading Massively Parallel Processing (MPP) architectures for Data Warehousing (DW) are IBM PureData System for Analytics (formerly Netezza) and Teradata. I thought talking about the similarities and differences…
 

Top Industries

By visitors reading reviews
Financial Services Firm
23%
Manufacturing Company
8%
Healthcare Company
5%
Insurance Company
5%
Financial Services Firm
16%
University
16%
Retailer
13%
Healthcare Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise5
Large Enterprise33
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise6
Large Enterprise4
 

Questions from the Community

What needs improvement with IBM Netezza Performance Server?
The cost of the solution is on the more expensive side, which is a concern for me. Additionally, its promotion and interaction with cloud applications are limited. The cloud version is only availab...
What advice do you have for others considering IBM Netezza Performance Server?
The solution has generally received positive feedback from me and is recommended for continued use by end users. However, the product cost is high compared to others in the market, and this cost ha...
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Also Known As

Netezza Performance Server, Netezza, Netezza Analytics
No data available
 

Overview

 

Sample Customers

Seattle Childrens Hospital, Carphone Warehouse, Vanderbilt University School of Medicine, Battelle, Start Today Co. Ltd., Kelley Blue Book, Trident Marketing, Elisa Corporation, Catalina Marketing, iBasis, Barnes & Noble, Qualcomm, MediaMath, Acxiom, iBasis, Foxwoods
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Find out what your peers are saying about IBM Netezza Performance Server vs. Spark SQL and other solutions. Updated: December 2025.
881,082 professionals have used our research since 2012.