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 8.7, while IBM is ranked #11. 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 65 Apache Spark reviews

1,728 Views
1,331 Comparison Views

90% willing to recommend

Netezza Analytics

Read 11 Netezza Analytics reviews

159 Views
73 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: January 2025).

Buyer's Guide

Apache Spark vs. Netezza Analytics

January 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

11th

Average Rating

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2025, in the Hadoop category, the mindshare of Apache Spark is 17.7%, down from 20.8% compared to the previous year. The mindshare of Netezza Analytics is 1.5%, up from 1.0% 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 solution is scalable."

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

"The product's deployment phase is easy."

"The most valuable feature of Apache Spark is its flexibility."

"The most valuable feature of Apache Spark is its ease of use."

"I found the solution stable. We haven't had any problems with it."

"I feel the streaming is its best feature."

"Features include machine learning, real time streaming, and data processing."

More Apache Spark pros

"Speed contributes to large capacity."

"The most valuable feature is the performance."

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

"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."

Cons

"At the initial stage, the product provides no container logs to check the activity."

"Apache Spark provides very good performance The tuning phase is still tricky."

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

"The solution’s integration with other platforms should be improved."

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"The Spark solution could improve in scheduling tasks and managing dependencies."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

More Apache Spark cons

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

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

"The Analytics feature should be simplified."

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

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

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

"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

"It is an open-source platform. We do not pay for its subscription."

"Apache Spark is an open-source tool."

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

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"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."

"They provide an open-source license for the on-premise version."

"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."

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

More Apache Spark pricing and cost advice

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Retailer

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 main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...

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Product Reports

Buyer's Guide

Apache Spark

February 2025

Download Apache Spark product report

Buyer's Guide

Netezza Analytics

January 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

January 2025

Free Report: Apache Spark vs. Netezza Analytics

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