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IBM Netezza Performance Server vs Spark SQL comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Aug 25, 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 (12th)
Spark SQL
Ranking in Hadoop
5th
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 October 2025, in the Hadoop category, the mindshare of IBM Netezza Performance Server is 1.9%, up from 1.7% compared to the previous year. The mindshare of Spark SQL is 9.4%, down from 10.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Spark SQL9.4%
IBM Netezza Performance Server1.9%
Other88.7%
Hadoop
 

Featured Reviews

Shiv Subramaniam Koduvayur - PeerSpot reviewer
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.
SurjitChoudhury - PeerSpot reviewer
Offers the flexibility to handle large-scale data processing
My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

Quotes from Members

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

Pros

"IBM Netezza Performance Server is a cost-effective solution."
"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."
"Speed contributes to large capacity."
"The most valuable features of the IBM Netezza Performance Server are the NPS server because of the reduced maintenance and overall good performance."
"The most valuable feature would be the fact that it has been running for awhile in an appliance format."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"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."
"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 stability was fine. It behaved as expected."
"I find the Thrift connection valuable."
"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."
"It is a stable solution."
"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."
"Overall the solution is excellent."
"This solution is useful to leverage within a distributed ecosystem."
 

Cons

"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."
"The most valuable features of this solution are robustness and support."
"The solution could implement more reporting tools and networking utilities."
"IBM Netezza Performance Server could improve its interface, support for big data, and APA-based connectivity should be available."
"The hardware has a risk of failure. They need to improve this."
"LIke Teradata, we can’t add a node/SPU to the existing appliance."
"Concurrency limit needs to be increased somewhat."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"It would be useful if Spark SQL integrated with some data visualization tools."
"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."
"There should be better integration with other solutions."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"I've experienced some incompatibilities when using the Delta Lake format."
"In the next release, maybe the visualization of some command-line features could be added."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
 

Pricing and Cost Advice

"Expensive to maintain compared to other solutions."
"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."
"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 solution has a yearly licensing fee, and users have to pay extra for support."
"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."
"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."
"We use the open-source version, so we do not have direct support from Apache."
"The solution is bundled with Palantir Foundry at no extra charge."
"The solution is open-sourced and free."
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Comparison Review

it_user232068 - PeerSpot reviewer
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%
Insurance Company
8%
Manufacturing Company
7%
Computer Software Company
5%
Financial Services Firm
17%
University
12%
Retailer
11%
Manufacturing 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 Enterprise5
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 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.
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 ...
What is your primary use case for Spark SQL?
I employ Spark SQL for various tasks. Initially, I gathered data from databases, SAP systems, and external sources via SFTP, storing it in blob storage. Using Spark SQL within Jupyter notebooks, I ...
 

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: September 2025.
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