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Apache Spark vs IBM Netezza Performance Server 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:
 

ROI

Sentiment score
6.6
Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.
Sentiment score
7.1
IBM Netezza Performance Server improves data query speed and efficiency, enhancing business performance and cost savings through compression.
 

Customer Service

Sentiment score
5.9
Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.
Sentiment score
6.4
IBM Netezza Performance Server praised for knowledgeable support; mixed feedback on resolution speed post-acquisition, yet communication remains consistent.
Technical support is very costly for me, accounting for twenty-five to thirty percent of the product cost.
 

Scalability Issues

Sentiment score
7.5
Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.
Sentiment score
6.1
IBM Netezza struggles with scalability, requiring extra hardware for expansion, prompting users to consider cloud alternatives for growth.
It is provided as a pre-configured box, and scaling is not an option.
 

Stability Issues

Sentiment score
7.5
Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.
Sentiment score
7.8
IBM Netezza Performance Server is stable, offering high uptime and reliable performance, with occasional issues due to maintenance or compatibility.
MapReduce needs to perform numerous disk input and output operations, while Apache Spark can use memory to store and process data.
 

Room For Improvement

Apache Spark requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.
IBM Netezza struggles with scalability, user interface, query performance, big data support, and high costs, needing better tools and integration.
The cloud version is only available in AWS, and in the Middle East, it is not well-developed in the Azure environment.
 

Setup Cost

Apache Spark is cost-effective but may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.
IBM Netezza offers high performance and low maintenance but is considered costly, especially for mid-sized organizations, with significant licensing fees.
 

Valuable Features

Apache Spark offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.
IBM Netezza Performance Server delivers fast analytics, ease of use, robust support, and efficient data warehousing with minimal maintenance.
Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code.
It operates as a high-speed data warehouse, which is essential for handling big data.
 

Categories and Ranking

Apache Spark
Ranking in Hadoop
1st
Average Rating
8.4
Reviews Sentiment
7.3
Number of Reviews
67
Ranking in other categories
Compute Service (3rd), Java Frameworks (2nd)
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)
 

Mindshare comparison

As of September 2025, in the Hadoop category, the mindshare of Apache Spark is 19.3%, up from 19.4% compared to the previous year. The mindshare of IBM Netezza Performance Server is 1.7%, down from 1.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark19.3%
IBM Netezza Performance Server1.7%
Other79.0%
Hadoop
 

Featured Reviews

Omar Khaled - PeerSpot reviewer
Empowering data consolidation and fast decision-making with efficient big data processing
I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.
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.
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867,370 professionals have used our research since 2012.
 

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
26%
Computer Software Company
11%
Manufacturing Company
7%
Comms Service Provider
7%
Financial Services Firm
22%
Insurance Company
8%
Manufacturing Company
7%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business27
Midsize Enterprise15
Large Enterprise32
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise5
Large Enterprise33
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
Regarding Apache Spark, I have only used Apache Spark Structured Streaming, not the machine learning components. I am uncertain about specific improvements needed today. However, after five years, ...
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...
 

Also Known As

No data available
Netezza Performance Server, Netezza, Netezza Analytics
 

Overview

 

Sample Customers

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
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
Find out what your peers are saying about Apache Spark vs. IBM Netezza Performance Server and other solutions. Updated: September 2025.
867,370 professionals have used our research since 2012.