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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 17.7% mindshare in H, compared to IBM’s 1.5% 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
  • 1,153 Views
  • 860 Comparison Views
90% willing to recommend
Netezza Analytics
Read 11 Netezza Analytics reviews
  • 173 Views
  • 57 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 June 2025, in the Hadoop category, the mindshare of Apache Spark is 17.7%, down from 21.1% compared to the previous year. The mindshare of Netezza Analytics is 1.5%, up from 1.2% 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

"The deployment of the product is easy."
"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"There's a lot of functionality."
"Features include machine learning, real time streaming, and data processing."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"Spark can handle small to huge data and is suitable for any size of company."
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."
"Speed contributes to large capacity."
"The most valuable feature is the performance."
"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."
"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."
"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
 

Cons

"Apache Spark lacks geospatial data."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"The solution needs to optimize shuffling between workers."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The main concern is the overhead of Java when distributed processing is not necessary."
More Apache Spark cons
"The most valuable features of this solution are robustness and support."
"The solution could implement more reporting tools and networking utilities."
"The hardware has a risk of failure. They need to improve this."
"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."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"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

"The solution is affordable and there are no additional licensing costs."
"Spark is an open-source solution, so there are no licensing costs."
"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."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"They provide an open-source license for the on-premise version."
"It is an open-source solution, it is free of charge."
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
27%
Computer Software Company
13%
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
Ask a question
Earn 20 points
 

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Compared 10% of the time
Spark SQL vs Apache Spark Logo
Spark SQL vs Apache Spark
Compared 7% of the time
Cloudera Distribution for Hadoop vs Apache Spark Logo
Cloudera Distribution for Hadoop vs Apache Spark
Compared 7% 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
May 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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