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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 August 2025, in the Hadoop category, the mindshare of IBM Netezza Performance Server is 1.7%, up from 1.6% compared to the previous year. The mindshare of Spark SQL is 10.3%, down from 11.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Spark SQL10.3%
IBM Netezza Performance Server1.7%
Other88.0%
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

"Distribution concurrency control."
"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."
"Speed contributes to large capacity."
"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 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."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"The performance is most important to me, and it helps our ability to make business decisions quickly."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"Data validation and ease of use are the most valuable features."
"The stability was fine. It behaved as expected."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"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."
"This solution is useful to leverage within a distributed ecosystem."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
 

Cons

"Oracle Exadata's security features, like TDE encryption, are missing in IBM Netezza Performance Server."
"LIke Teradata, we can’t add a node/SPU to the existing appliance."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"Our main problem with it is concurrency. When there are too many users running Netezza at the same time, this is when we have the most complaints."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"IBM Netezza Performance Server could improve its interface, support for big data, and APA-based connectivity should be available."
"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."
"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."
"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."
"There should be better integration with other solutions."
"There are many inconsistencies in syntax for the different querying tasks."
"This solution could be improved by adding monitoring and integration for the EMR."
"In the next release, maybe the visualization of some command-line features could be added."
"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."
"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."
"Expensive to maintain compared to other solutions."
"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 is bundled with Palantir Foundry at no extra charge."
"There is no license or subscription for this solution."
"We use the open-source version, so we do not have direct support from Apache."
"The solution is open-sourced and free."
"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."
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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%
Manufacturing Company
8%
Computer Software Company
7%
Insurance Company
7%
Financial Services Firm
17%
University
11%
Manufacturing Company
10%
Retailer
10%
 

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 do you like most about IBM Netezza Performance Server?
IBM Netezza Performance Server is a cost-effective solution.
What is your experience regarding pricing and costs for IBM Netezza Performance Server?
The solution has a yearly licensing fee, and users have to pay extra for support.
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 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 is your experience regarding pricing and costs for Spark SQL?
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.
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 ...
 

Also Known As

Netezza Performance Server, Netezza, Netezza Analytics
No data available
 

Overview

 

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