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Netezza Analytics vs Spark SQL comparison

IBM and Apache are both solutions in the Hadoop category. IBM is ranked #11, while Apache is ranked #4 with an average rating of 8.3. IBM holds a 1.4% mindshare in H, compared to Apache’s 9.9% 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
  • 190 Views
  • 89 Comparison Views
76% willing to recommend
Spark SQL
Read 14 Spark SQL reviews
  • 1,139 Views
  • 835 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: October 2024).
Buyer's Guide
Netezza Analytics vs. Spark SQL
October 2024
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Categories and Ranking

Netezza Analytics
Ranking in Hadoop
11th
Average Rating
7.4
Number of Reviews
11
Ranking in other categories
No ranking in other categories
Spark SQL
Ranking in Hadoop
4th
Average Rating
7.8
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Hadoop category, the mindshare of Netezza Analytics is 1.4%, up from 1.0% compared to the previous year. The mindshare of Spark SQL is 9.9%, down from 12.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

Asish Govind - PeerSpot reviewer
Dec 10, 2019
Stable with limited administration needed but it's coming up on its end of life
We're currently trying to create a data warehouse on the solution The appliance is quite quiet. The need for administration involvement is quite limited on the solution. I'm not sure if Netezza offers a cloud version of the solution or not, but if they don't they should. Most companies are…
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SurjitChoudhury - PeerSpot reviewer
Nov 23, 2023
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.
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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

"For me, as an end-user, everything that I do on the solution is simple, clear, and understandable."
"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."
"Speed contributes to large capacity."
"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."
"The speed of getting data."
"The stability was fine. It behaved as expected."
"I find the Thrift connection valuable."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"This solution is useful to leverage within a distributed ecosystem."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"It is a stable solution."
More Spark SQL pros
 

Cons

"In-DB processing with SAS Analytics, since this is supposed to be an analytics server so the expectation is there."
"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."
"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."
"I'm not sure of IBM's roadmap currently, as the solution is coming up on its end of life."
"Disaster recovery support. Because it was an appliance, and if you wanted to support disaster recovery, you needed to buy two."
More Netezza Analytics cons
"Anything to improve the GUI would be helpful."
"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."
"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."
"There are many inconsistencies in syntax for the different querying tasks."
"It would be useful if Spark SQL integrated with some data visualization tools."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"In the next release, maybe the visualization of some command-line features could be added."
More Spark SQL cons
 

Pricing and Cost Advice

"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."
"Expensive to maintain compared to other solutions."
"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."
"The solution is bundled with Palantir Foundry at no extra charge."
"There is no license or subscription for this solution."
"We use the open-source version, so we do not have direct support from Apache."
"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."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
25%
Computer Software Company
17%
Manufacturing Company
6%
University
6%
 

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.
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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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Compared 11% of the time
SAP HANA vs Spark SQL Logo
SAP HANA vs Spark SQL
Compared 10% of the time
IBM Db2 Big SQL vs Spark SQL Logo
IBM Db2 Big SQL vs Spark SQL
Compared 10% of the time
More Spark SQL Competitors
 

Product Reports

Buyer's Guide
Netezza Analytics
October 2024
Netezza Analytics reportDownload Netezza Analytics product report
Buyer's Guide
Spark SQL
October 2024
Spark SQL reportDownload Spark SQL product report
 

Learn More

IBM
Apache
 

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.
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.
 

Sample Customers

A leading online advertising network
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Buyer's Guide
Netezza Analytics vs. Spark SQL
October 2024
Free Report: Netezza Analytics vs. Spark SQL
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