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 #11. Apache holds a 17.8% mindshare in H, compared to IBM’s 1.3% 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

1,580 Views
1,196 Comparison Views

90% willing to recommend

Netezza Analytics

Read 11 Netezza Analytics reviews

171 Views
68 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

11th

Average Rating

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2025, in the Hadoop category, the mindshare of Apache Spark is 17.8%, down from 21.2% compared to the previous year. The mindshare of Netezza Analytics is 1.3%, 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

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

"We use Spark to process data from different data sources."

"The deployment of the product is easy."

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

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

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

"Spark can handle small to huge data and is suitable for any size of company."

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

More Apache Spark pros

"Speed contributes to large capacity."

"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."

"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."

"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."

Cons

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."

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

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

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

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

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

More Apache Spark cons

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

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

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

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

"This product is being discontinued from IBM, and I would like to have some kind of upgrade available."

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

More Netezza Analytics cons

Pricing and Cost Advice

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

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

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

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

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

"Apache Spark is an open-source tool."

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

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

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

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...

See all answers

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Product Reports

Buyer's Guide

Apache Spark

March 2025

Download Apache Spark product report

Buyer's Guide

Netezza Analytics

February 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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