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 #10. Apache holds a 17.5% 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 65 Apache Spark reviews

1,404 Views
1,090 Comparison Views

90% willing to recommend

Netezza Analytics

Read 11 Netezza Analytics reviews

177 Views
65 Comparison Views

76% willing to recommend

Apache Spark

Netezza Analytics

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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: April 2025).

Buyer's Guide

Apache Spark vs. Netezza Analytics

April 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

7.7

Number of Reviews

Ranking in other categories

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

Netezza Analytics

Ranking in Hadoop

10th

Average Rating

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Hadoop category, the mindshare of Apache Spark is 17.5%, down from 21.4% compared to the previous year. The mindshare of Netezza Analytics is 1.4%, up from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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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.

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 product's deployment phase is easy."

"Apache Spark provides a very high-quality implementation of distributed data processing."

"It provides a scalable machine learning library."

"The solution has been very stable."

"Apache Spark can do large volume interactive data analysis."

"This solution provides a clear and convenient syntax for our analytical tasks."

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

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 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 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."

"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."

"Speed contributes to large capacity."

Cons

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

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

"There were some problems related to the product's compatibility with a few Python libraries."

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"One limitation is that not all machine learning libraries and models support it."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"The migration of data between different versions could be improved."

"Apache Spark lacks geospatial data."

More Apache Spark cons

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

"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."

"The solution could implement more reporting tools and networking utilities."

"Administration of this product is too tough. It's very complex because of the tools which it's missing."

"The Analytics feature should be simplified."

"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."

"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

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

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"We are using the free version of the solution."

"The product is expensive, considering the setup."

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

"Spark is an open-source solution, so there are no licensing costs."

"Apache Spark is an expensive solution."

"It is an open-source solution, it is free of charge."

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."

"The annual licensing fees are twenty-two percent of the product cost."

"Expensive to maintain compared to other solutions."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

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.

See all answers

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

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

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Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

Netezza Analytics

April 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

A leading online advertising network

Buyer's Guide

Apache Spark vs. Netezza Analytics

April 2025

Free Report: Apache Spark vs. Netezza Analytics

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