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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 February 2026, in the Hadoop category, the mindshare of IBM Netezza Performance Server is 5.8%, up from 1.9% compared to the previous year. The mindshare of Spark SQL is 6.1%, down from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Spark SQL6.1%
IBM Netezza Performance Server5.8%
Other88.1%
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."
"Distribution concurrency control."
"The most valuable feature would be the fact that it has been running for awhile in an appliance format."
"The performance is most important to me, and it helps our ability to make business decisions quickly."
"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."
"We are able to execute very complex queries. Over 90 percent of our query executions are one second or less. We do millions of queries everyday."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"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."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"I find the Thrift connection valuable."
"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 stability was fine. It behaved as expected."
"The speed of getting data."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
 

Cons

"The product cost is high compared to others in the market, and this cost has become unbearable for me."
"The hardware has a risk of failure. They need to improve this."
"The solution could implement more reporting tools and networking utilities."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"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."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"SparkUI could have more advanced versions of the performance and the queries and all."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"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."
"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."
"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."
 

Pricing and Cost Advice

"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."
"Expensive to maintain compared to other solutions."
"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."
"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."
"The solution has a yearly licensing fee, and users have to pay extra for support."
"There is no license or subscription for this solution."
"The solution is bundled with Palantir Foundry at no extra charge."
"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."
"We use the open-source version, so we do not have direct support from Apache."
"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."
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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
22%
Manufacturing Company
8%
Comms Service Provider
5%
Healthcare Company
5%
Financial Services Firm
15%
University
15%
Retailer
12%
Healthcare Company
7%
 

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: February 2026.
881,707 professionals have used our research since 2012.