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IBM Netezza Performance Server vs Spark SQL comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 1, 2026

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
6th
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 March 2026, in the Hadoop category, the mindshare of IBM Netezza Performance Server is 6.2%, up from 1.7% 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 Mindshare Distribution
ProductMindshare (%)
Spark SQL6.1%
IBM Netezza Performance Server6.2%
Other87.7%
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 would be the fact that it has been running for awhile in an appliance format."
"The need for administration involvement is quite limited on 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."
"Parallel data processing is a significant feature for me."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"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 performance is most important to me, and it helps our ability to make business decisions quickly."
"I find the Thrift connection valuable."
"This solution is useful to leverage within a distributed ecosystem."
"The speed of getting data."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"Data validation and ease of use are the most valuable features."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"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."
 

Cons

"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"The product cost is high compared to others in the market, and this cost has become unbearable for me."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"The scalability is not as expected. The capacity in the black box is not enough."
"The solution could implement more reporting tools and networking utilities."
"LIke Teradata, we can’t add a node/SPU to the existing appliance."
"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."
"In the next release, maybe the visualization of some command-line features could be added."
"SparkUI could have more advanced versions of the performance and the queries and all."
"It would be useful if Spark SQL integrated with some data visualization tools."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"This solution could be improved by adding monitoring and integration for the EMR."
"I've experienced some incompatibilities when using the Delta Lake format."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
 

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."
"The annual licensing fees are twenty-two percent of the product cost."
"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."
"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."
"The solution has a yearly licensing fee, and users have to pay extra for support."
"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 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 use the open-source version, so we do not have direct support from Apache."
"There is no license or subscription for this solution."
"The solution is open-sourced and free."
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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
20%
Manufacturing Company
9%
Comms Service Provider
7%
Performing Arts
5%
Financial Services Firm
18%
University
14%
Retailer
12%
Healthcare Company
9%
 

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...
What needs improvement with Spark SQL?
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 manageme...
What is your primary use case for Spark SQL?
Spark SQL has been in our stack for less than one year, though some of our colleagues are using it. It is a useful product for transformation jobs. We generally use Spark SQL for batch processing. ...
What advice do you have for others considering Spark SQL?
Regarding the Catalyst query optimizer, I think we are using it. We were using it in the past, but I am not certain if we use it now. We used it a long time ago. I rate my experience with Spark SQL...
 

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: March 2026.
884,873 professionals have used our research since 2012.