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 #9. 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 67 Apache Spark reviews

4,468 Views
983 Comparison Views

90% willing to recommend

Netezza Analytics

Read 11 Netezza Analytics reviews

206 Views
167 Comparison Views

76% willing to recommend

Apache Spark

Netezza Analytics

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.

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

7.3

Number of Reviews

Ranking in other categories

Compute Service (4th), Java Frameworks (2nd)

Netezza Analytics

Ranking in Hadoop

9th

Average Rating

7.4

Number of Reviews

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

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

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.

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Shiv Subramaniam Koduvayur

Project Manager at MAF Retail

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 product’s most valuable features are lazy evaluation and workload distribution."

"The product's initial setup phase was easy."

"It provides a scalable machine learning library."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

"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."

"The most valuable feature of this solution is its capacity for processing large amounts of data."

"The processing time is very much improved over the data warehouse solution that we were using."

"Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms."

More Apache Spark pros

"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."

"The need for administration involvement is quite limited on the solution."

"Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more."

"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."

"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."

"The most valuable feature is the performance."

"Speed contributes to large capacity."

Cons

"The basic improvement would be to have integration with these solutions."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"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)."

"The initial setup was not easy."

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."

More Apache Spark cons

"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."

"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."

"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."

"The hardware has a risk of failure. They need to improve this."

"The Analytics feature should be simplified."

"The most valuable features of this solution are robustness and support."

"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

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"The solution is affordable and there are no additional licensing costs."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"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."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"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 an expensive solution."

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

26%

Computer Software Company

10%

Manufacturing Company

Comms Service Provider

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.

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What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

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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...

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Comparisons

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Compared 23% of the time

AWS Batch vs Apache Spark

Compared 10% of the time

SAP HANA vs Apache Spark

Compared 9% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

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

August 2025

Download Apache Spark product report

Buyer's Guide

Netezza Analytics

August 2025

Download 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

Free Report: Apache Spark vs. Netezza Analytics

Find out what your peers are saying about Apache Spark vs. Netezza Analytics and other solutions. Updated: July 2025.

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