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Apache Spark vs QueryIO comparison

 

Comparison Buyer's Guide

Executive Summary

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
6.9
Number of Reviews
69
Ranking in other categories
Compute Service (6th), Java Frameworks (2nd)
QueryIO
Ranking in Hadoop
12th
Average Rating
8.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2026, in the Hadoop category, the mindshare of Apache Spark is 13.9%, down from 17.6% compared to the previous year. The mindshare of QueryIO is 2.7%, up from 0.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark13.9%
QueryIO2.7%
Other83.4%
Hadoop
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
MR
Manager of Process & Systems / Solutions Architect / BI Developer at HENKEL FRANCE
Stable with good connectivity and good integration capabilities
Data cleansing is not intuitive and user-friendly. When things have errors, you have to hunt them down as opposed to the solution simply showing you intuitively where to find it. I would recommend that they look at that Tableau Prep tool and see how it is pieced together. That's a great data cleansing tool. If Microsoft has something like that, then we wouldn't even have to look at some of the other options. There needs to be some simplification of the user interface. Right now it's too complicated. There isn't a way to put controls on the solution, so anyone can use any part of it, and sometimes novices will go and try to create things, but not know enough about what is official and what is published. It would be ideal if we could segment off certain sections so that not everyone had access to the whole solution. I'd like to see something more of a mapping tool so that you could see how the reports are connected, similar to Tableau Prep and Naim. That would make for a pretty useful diagnostics check. People would be better able to understand the linkage between your datasets. It would be nice if the solution offered some templates. It would make it even more plug and play, and give people a good jumping-off point. After that, they could explore other bells and whistles as they get further into understanding the solution. The solution should work in some virtualization. It would be a good added feature. If this product had those things then I wouldn't need to use other products.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"Spark, as a tool, is easy to work with as you can work with Python, Scala, and Java."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"Overall, it offers everything that I can imagine right now."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"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."
"The solution is scalable."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"It's so readily available and there's information online to educate yourself on the product."
"Anyone who has even a little bit of knowledge of the solution can begin to create things. You don't have to be technical to use the solution."
 

Cons

"There were some problems related to the product's compatibility with a few Python libraries."
"The initial set-up is quite complex because you have to set-up many different configuration parameters that are deployment-specific."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"The setup I worked on was really complex."
"The solution must improve its performance."
"The solution needs to optimize shuffling between workers."
"There needs to be some simplification of the user interface."
"Technical support is not that great. It's more like a study session than support."
 

Pricing and Cost Advice

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"Spark is an open-source solution, so there are no licensing costs."
"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."
"It is an open-source solution, it is free of charge."
"It is an open-source platform. We do not pay for its subscription."
"Apache Spark is an open-source tool."
"The solution is affordable and there are no additional licensing costs."
"They provide an open-source license for the on-premise version."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Manufacturing Company
9%
Construction Company
8%
Comms Service Provider
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise33
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...
What is your primary use case for Apache Spark?
I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...
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Overview

 

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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Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: May 2026.
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