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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

"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"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 would be the fact that it has been running for awhile in an appliance format."
"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."
"Parallel data processing is a significant feature for me."
"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."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"The most valuable feature is the performance."
"It is a stable solution."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"Data validation and ease of use are the most valuable features."
"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 stability was fine. It behaved as expected."
"The speed of getting data."
"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."
 

Cons

"The product cost is high compared to others in the market, and this cost has become unbearable for me."
"In terms of features that I would like to see, one is the ability to actually scale out an architecture. Right now, if you buy one, it's fixed. There is no scale-up availability at all."
"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."
"LIke Teradata, we can’t add a node/SPU to the existing appliance."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"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."
"Concurrency limit needs to be increased somewhat."
"This solution could be improved by adding monitoring and integration for the EMR."
"I've experienced some incompatibilities when using the Delta Lake format."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"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."
"SparkUI could have more advanced versions of the performance and the queries and all."
"There are many inconsistencies in syntax for the different querying tasks."
"It would be useful if Spark SQL integrated with some data visualization tools."
"Anything to improve the GUI would be helpful."
 

Pricing and Cost Advice

"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."
"Expensive to maintain compared to other solutions."
"The solution has a yearly licensing fee, and users have to pay extra for support."
"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 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 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."
"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."
"The solution is open-sourced and free."
"The solution is bundled with Palantir Foundry at no extra charge."
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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
17%
University
13%
Retailer
11%
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.
884,122 professionals have used our research since 2012.