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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 (5th), 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 March 2026, in the Hadoop category, the mindshare of Apache Spark is 13.3%, down from 18.6% compared to the previous year. The mindshare of QueryIO is 2.7%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark13.3%
QueryIO2.7%
Other84.0%
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

"The product's deployment phase is easy."
"There's a lot of functionality."
"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."
"I found the solution stable. We haven't had any problems with it."
"The best features in Apache Spark that I appreciate are the fast database access, the data transformation, and the data exchange."
"We have 1000x improvement in performance over other techniques."
"The main feature that we find valuable is that it is very fast."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"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

"The migration of data between different versions could be improved."
"I do not know exactly what was the reason to move away from Apache Spark or the underlying database system, but it was simply a decision driven by the customer."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"There were some problems related to the product's compatibility with a few Python libraries."
"Dynamic DataFrame options are not yet available."
"Apache Spark lacks geospatial data."
"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."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"There needs to be some simplification of the user interface."
 

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."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"They provide an open-source license for the on-premise version."
"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."
"The product is expensive, considering the setup."
"It is an open-source solution, it is free of charge."
"The solution is affordable and there are no additional licensing costs."
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Top Industries

By visitors reading reviews
Financial Services Firm
23%
Manufacturing Company
8%
Computer Software Company
7%
Comms Service Provider
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise32
No data available
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
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...
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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: March 2026.
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