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Netezza Analytics vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #8, while Apache is ranked #5 with an average rating of 8.7. IBM holds a 1.3% mindshare in H, compared to Apache’s 10.5% mindshare. Additionally, 76% of IBM users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.
Netezza Analytics
Read 11 Netezza Analytics reviews
  • 207 Views
  • 176 Comparison Views
76% willing to recommend
Spark SQL
Read 14 Spark SQL reviews
  • 725 Views
  • 660 Comparison Views
85% willing to recommend
 

Comparison Buyer's Guide

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Executive Summary
We performed a comparison between Netezza Analytics and Spark SQL 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.

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To learn more, read our detailed Netezza Analytics vs. Spark SQL Report (Updated: July 2025).
Buyer's Guide
Netezza Analytics vs. Spark SQL
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

Netezza Analytics
Ranking in Hadoop
8th
Average Rating
7.4
Number of Reviews
11
Ranking in other categories
No ranking in other categories
Spark SQL
Ranking in Hadoop
5th
Average Rating
7.8
Reviews Sentiment
7.6
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the Hadoop category, the mindshare of Netezza Analytics is 1.3%, down from 1.3% compared to the previous year. The mindshare of Spark SQL is 10.5%, down from 11.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

Shiv Subramaniam Koduvayur - PeerSpot reviewer
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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SurjitChoudhury - PeerSpot reviewer
Offers the flexibility to handle large-scale data processing
My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.
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

"Speed contributes to large capacity."
"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."
"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."
"The most valuable feature is the performance."
"Data validation and ease of use are the most valuable features."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
"Overall the solution is excellent."
"It is a stable solution."
"The stability was fine. It behaved as expected."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
More Spark SQL pros
 

Cons

"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
"Administration of this product is too tough. It's very complex because of the tools which it's missing."
"The hardware has a risk of failure. They need to improve this."
"The most valuable features of this solution are robustness and support."
"The Analytics feature should be simplified."
"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."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
More Netezza Analytics cons
"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."
"SparkUI could have more advanced versions of the performance and the queries and all."
"In the next release, maybe the visualization of some command-line features could be added."
"This solution could be improved by adding monitoring and integration for the EMR."
"Anything to improve the GUI would be helpful."
"There are many inconsistencies in syntax for the different querying tasks."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."
More Spark SQL cons
 

Pricing and Cost Advice

"Expensive to maintain compared to other solutions."
"The annual licensing fees are twenty-two percent of the product cost."
"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."
"We use the open-source version, so we do not have direct support from Apache."
"The solution is bundled with Palantir Foundry at no extra charge."
"There is no license or subscription for this solution."
"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."
"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."
"The solution is open-sourced and free."
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861,524 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
No data available
Financial Services Firm
18%
Computer Software Company
11%
Retailer
9%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

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What do you like most about Spark SQL?
Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.
See all answers
What is your experience regarding pricing and costs for Spark SQL?
We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.
See all answers
What needs improvement with Spark SQL?
In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...
See all answers
 

Comparisons

No data available
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Apache Spark vs Spark SQL
Compared 50% of the time
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SAP HANA vs Spark SQL
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IBM Db2 Big SQL vs Spark SQL Logo
IBM Db2 Big SQL vs Spark SQL
Compared 11% of the time
IBM Analytics Engine vs Spark SQL Logo
IBM Analytics Engine vs Spark SQL
Compared 10% of the time
Amazon EMR vs Spark SQL Logo
Amazon EMR vs Spark SQL
Compared 9% of the time
More Spark SQL Competitors
 

Product Reports

Buyer's Guide
Netezza Analytics
June 2025
Netezza Analytics reportDownload Netezza Analytics product report
Buyer's Guide
Spark SQL
June 2025
Spark SQL reportDownload Spark SQL product report
 

Overview

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
Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.
Apache
 

Sample Customers

A leading online advertising network
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Buyer's Guide
Netezza Analytics vs. Spark SQL
July 2025
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