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: March 2025).

Buyer's Guide

Apache Spark vs. Netezza Analytics

March 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.

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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

"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."

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

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

"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."

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

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

"The deployment of the product is easy."

"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."

"The most valuable feature is the performance."

"Speed contributes to large capacity."

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

"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."

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

Cons

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

"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)."

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

"The main concern is the overhead of Java when distributed processing is not necessary."

"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."

"They could improve the issues related to programming language for the platform."

"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."

More Apache Spark cons

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

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

"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."

"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."

More Netezza Analytics cons

Pricing and Cost Advice

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

"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."

"Apache Spark is an expensive solution."

"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."

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

"The product is expensive, considering the setup."

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

"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."

"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

28%

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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Comparisons

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Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

Spark SQL vs Netezza Analytics

Compared 100% of the time

More Netezza Analytics Competitors

Product Reports

Buyer's Guide

Apache Spark

March 2025

Download Apache Spark product report

Buyer's Guide

Netezza Analytics

March 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

March 2025

Free Report: Apache Spark vs. Netezza Analytics

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