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 #16. Apache holds a 17.7% mindshare in H, compared to QueryIO’s 0.4% 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 65 Apache Spark reviews

1,627 Views
1,270 Comparison Views

90% willing to recommend

QueryIO

Read 1 QueryIO review

46 Views
30 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

February 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

7.7

Number of Reviews

Ranking in other categories

Compute Service (3rd), Java Frameworks (2nd)

QueryIO

Ranking in Hadoop

16th

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2025, in the Hadoop category, the mindshare of Apache Spark is 17.7%, down from 20.8% compared to the previous year. The mindshare of QueryIO is 0.4%, down from 0.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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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

"Features include machine learning, real time streaming, and data processing."

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

"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."

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

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

"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."

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

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 solution must improve its performance."

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."

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

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"It should support more programming languages."

"Apache Spark's GUI and scalability could be improved."

"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

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

"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."

"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."

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

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

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"The solution is affordable and there are no additional licensing costs."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

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?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

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What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

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Compared 8% of the time

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More Apache Spark Competitors

No data available

Product Reports

Buyer's Guide

Apache Spark

February 2025

Download Apache Spark product report

Buyer's Guide

Hadoop

February 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: February 2025.

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