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 19.2% mindshare in H, compared to QueryIO’s 0.7% 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

4,837 Views
1,064 Comparison Views

90% willing to recommend

QueryIO

Read 1 QueryIO review

85 Views
76 Comparison Views

100% willing to recommend

Apache Spark

QueryIO

Comparison Buyer's Guide

Download the report

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.

To learn more, read our detailed Hadoop Report (Updated: July 2025).

Buyer's Guide

Hadoop

July 2025

Download the complete report

Helped 864,053 peers since 2012

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 August 2025, in the Hadoop category, the mindshare of Apache Spark is 19.2%, down from 20.2% compared to the previous year. The mindshare of QueryIO is 0.7%, down from 0.8% 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.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"The product's initial setup phase was easy."

"Provides a lot of good documentation compared to other solutions."

"The main feature that we find valuable is that it is very fast."

"The most valuable feature of Apache Spark is its flexibility."

"ETL and streaming capabilities."

"Apache Spark provides a very high-quality implementation of distributed data processing."

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

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

"The migration of data between different versions could be improved."

"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

"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's not easy to install."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

"We are building our own queries on Spark, and it can be improved in terms of query handling."

"Apache Spark should add some resource management improvements to the algorithms."

More Apache Spark cons

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

Pricing and Cost Advice

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"It is an open-source solution, it is free of charge."

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

"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."

"They provide an open-source license for the on-premise version."

"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 an open-source tool."

"The product is expensive, considering the setup."

More Apache Spark pricing and cost advice

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which Hadoop solutions are best for your needs.

See recommendations

864,053 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

10%

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.

See all answers

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

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

See all answers

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...

See all answers

Ask a question

Earn 20 points

Comparisons

Spring Boot vs Apache Spark

Compared 24% of the time

AWS Batch vs Apache Spark

Compared 10% of the time

SAP HANA vs Apache Spark

Compared 9% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

More Apache Spark Competitors

No data available

Product Reports

Buyer's Guide

Apache Spark

July 2025

Download Apache Spark product report

Buyer's Guide

Hadoop

July 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

Information Not Available

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: July 2025.

DOWNLOAD NOW

864,053 professionals have used our research since 2012.

See our list of best Hadoop vendors.

We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.