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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 19.2% mindshare in H, compared to IBM’s 1.4% 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
  • 4,837 Views
  • 1,064 Comparison Views
90% willing to recommend
Netezza Analytics
Read 11 Netezza Analytics reviews
  • 207 Views
  • 176 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: July 2025).
Buyer's Guide
Apache Spark vs. Netezza Analytics
July 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 August 2025, in the Hadoop category, the mindshare of Apache Spark is 19.2%, down from 20.2% compared to the previous year. The mindshare of Netezza Analytics is 1.4%, up from 1.4% 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.
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Quotes from Members

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

Pros

"This solution provides a clear and convenient syntax for our analytical tasks."
"I found the solution stable. We haven't had any problems with it."
"There's a lot of functionality."
"The product is useful for analytics."
"I feel the streaming is its best feature."
"The product’s most valuable features are lazy evaluation and workload distribution."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
More Apache Spark pros
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"The need for administration involvement is quite limited on 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."
"The most valuable feature is the performance."
"Speed contributes to large capacity."
"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."
 

Cons

"The migration of data between different versions could be improved."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"The solution needs to optimize shuffling between workers."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
More Apache Spark cons
"The most valuable features of this solution are robustness and support."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"The solution could implement more reporting tools and networking utilities."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."
"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"The Analytics feature should be simplified."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
More Netezza Analytics cons
 

Pricing and Cost Advice

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"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."
"The solution is affordable and there are no additional licensing costs."
"It is an open-source platform. We do not pay for its subscription."
"We are using the free version of the solution."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"Spark is an open-source solution, so there are no licensing costs."
"The product is expensive, considering the setup."
More Apache Spark pricing and cost advice
"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 annual licensing fees are twenty-two percent of the product cost."
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Top Industries

By visitors reading reviews
Financial Services Firm
26%
Computer Software Company
10%
Manufacturing Company
7%
Comms Service Provider
7%
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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Product Reports

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