Try our new research platform with insights from 80,000+ expert users

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
68
Ranking in other categories
Compute Service (5th), Java Frameworks (2nd)
QueryIO
Ranking in Hadoop
13th
Average Rating
8.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2026, in the Hadoop category, the mindshare of Apache Spark is 13.9%, down from 18.2% compared to the previous year. The mindshare of QueryIO is 2.4%, up from 0.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark13.9%
QueryIO2.4%
Other83.7%
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 fault tolerant feature is provided."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The most valuable feature of Apache Spark is its flexibility."
"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."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"Provides a lot of good documentation compared to other solutions."
"The solution has been very stable."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"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

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"Apache Spark provides very good performance The tuning phase is still tricky."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The solution needs to optimize shuffling between workers."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."
"There needs to be some simplification of the user interface."
 

Pricing and Cost Advice

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."
"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"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."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"Spark is an open-source solution, so there are no licensing costs."
"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."
"Apache Spark is an expensive solution."
Information not available
report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
881,082 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
9%
Manufacturing 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 Enterprise15
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?
Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...
Ask a question
Earn 20 points
 

Comparisons

No data available
 

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
Information Not Available
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: January 2026.
881,082 professionals have used our research since 2012.