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Apache Spark vs Netezza Analytics comparison

Apache and IBM are both solutions in the Hadoop category. Apache is ranked #1 with an average rating of 8.6, while IBM is ranked #8. Apache holds a 18.3% mindshare in H, compared to IBM’s 1.3% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 76% of IBM users who would recommend it.
Apache Spark
Read 66 Apache Spark reviews
  • 1,064 Views
  • 784 Comparison Views
90% willing to recommend
Netezza Analytics
Read 11 Netezza Analytics reviews
  • 176 Views
  • 53 Comparison Views
76% willing to recommend
 

Comparison Buyer's Guide

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Executive Summary
We performed a comparison between Apache Spark and Netezza Analytics based on real PeerSpot user reviews.

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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To learn more, read our detailed Apache Spark vs. Netezza Analytics Report (Updated: June 2025).
Buyer's Guide
Apache Spark vs. Netezza Analytics
June 2025
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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

Apache Spark
Ranking in Hadoop
1st
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
66
Ranking in other categories
Compute Service (4th), Java Frameworks (2nd)
Netezza Analytics
Ranking in Hadoop
8th
Average Rating
7.4
Number of Reviews
11
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the Hadoop category, the mindshare of Apache Spark is 18.3%, down from 20.4% compared to the previous year. The mindshare of Netezza Analytics is 1.3%, down from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

Dunstan Matekenya - PeerSpot reviewer
Open-source solution for data processing with portability
Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.
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Shiv Subramaniam Koduvayur - PeerSpot reviewer
A robust solution with good support, but a better GUI for database management is needed
The biggest lesson that I have learned from using this solution is that a lot of evaluation should be done before starting. Also, we needed to put a lot of effort into understanding the different functions that the product offers. This allows you to best leverage the capability of the product. I would rate this solution a seven out of ten.
Read full review

Quotes from Members

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

Pros

"The fault tolerant feature is provided."
"Features include machine learning, real time streaming, and data processing."
"The data processing framework is good."
"The product's initial setup phase was easy."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The good performance. The nice graphical management console. The long list of ML algorithms."
More Apache Spark pros
"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."
"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."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"Speed contributes to large capacity."
"The need for administration involvement is quite limited on the solution."
"The most valuable feature is the performance."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
 

Cons

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"It should support more programming languages."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"The product could improve the user interface and make it easier for new users."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"They could improve the issues related to programming language for the platform."
More Apache Spark cons
"The most valuable features of this solution are robustness and support."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"The hardware has a risk of failure. They need to improve this."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"The solution could implement more reporting tools and networking utilities."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
More Netezza Analytics cons
 

Pricing and Cost Advice

"Apache Spark is an expensive solution."
"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."
"They provide an open-source license for the on-premise version."
"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"We are using the free version of the solution."
"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
"It is an open-source platform. We do not pay for its subscription."
More Apache Spark 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."
"Expensive to maintain compared to other solutions."
"The annual licensing fees are twenty-two percent of the product cost."
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
12%
Manufacturing Company
7%
Comms Service Provider
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
See all answers
What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
See all answers
What needs improvement with Apache Spark?
There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...
See all answers
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Spark SQL vs Apache Spark Logo
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Compared 7% of the time
Cloudera Distribution for Hadoop vs Apache Spark Logo
Cloudera Distribution for Hadoop vs Apache Spark
Compared 6% of the time
More Apache Spark Competitors
No data available
 

Product Reports

Buyer's Guide
Apache Spark
June 2025
Apache Spark reportDownload Apache Spark product report
Buyer's Guide
Netezza Analytics
June 2025
Netezza Analytics reportDownload Netezza Analytics product report
 

Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache
IBM Netezza Analytics is an embedded, purpose-built, advanced analytics platform that empowers analytic enterprises to meet and exceed their business demands. As features, it can predict with more accuracy, deliver predictions faster and respond rapidly to changes.
IBM
 

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
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Buyer's Guide
Apache Spark vs. Netezza Analytics
June 2025
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