Apache Spark vs QueryIO comparison

Apache and QueryIO are both solutions in the Hadoop category. Apache is ranked #1 with an average rating of 8.6, while QueryIO is ranked #13. Apache holds a 18.3% mindshare in H, compared to QueryIO’s 0.8% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 100% of QueryIO users who would recommend it.

Apache Spark

Read 66 Apache Spark reviews

1,064 Views
784 Comparison Views

90% willing to recommend

QueryIO

Read 1 QueryIO review

76 Views
32 Comparison Views

100% willing to recommend

Apache Spark

QueryIO

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Executive Summary

We performed a comparison between Apache Spark and QueryIO based on real PeerSpot user reviews.

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

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Buyer's Guide

Hadoop

June 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

Apache Spark

Ranking in Hadoop

1st

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Compute Service (4th), Java Frameworks (2nd)

QueryIO

Ranking in Hadoop

13th

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Hadoop category, the mindshare of Apache Spark is 18.3%, down from 20.4% compared to the previous year. The mindshare of QueryIO is 0.8%, up from 0.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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Marco Reyes

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.

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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

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"The product is useful for analytics."

"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

"The product’s most valuable features are lazy evaluation and workload distribution."

"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."

More Apache Spark pros

"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

"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."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"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."

"The main concern is the overhead of Java when distributed processing is not necessary."

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

"They could improve the issues related to programming language for the platform."

"One limitation is that not all machine learning libraries and models support it."

More Apache Spark cons

"There needs to be some simplification of the user interface."

Pricing and Cost Advice

"The product is expensive, considering the setup."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"Spark is an open-source solution, so there are no licensing costs."

"Apache Spark is an expensive solution."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"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."

"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."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

12%

Manufacturing Company

Comms Service Provider

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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.

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What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

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What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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Product Reports

Buyer's Guide

Apache Spark

June 2025

Download Apache Spark product report

Buyer's Guide

Hadoop

June 2025

Download QueryIO product report

Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

QueryIO is a Hadoop-based SQL and Big Data Analytics solution, used to store, structure, analyze and visualize vast amounts of structured and unstructured Big Data. It is especially well suited to enable users to process unstructured Big Data, give it a structure and support querying and analysis of this Big Data using standard SQL syntax. QueryIO enables you to leverage the vast and mature infrastructure built around SQL and relational databases and utilize it for your Big Data Analytics needs.

QueryIO

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: June 2025.

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