Apache Spark vs Spark SQL comparison

Apache Spark and Spark SQL are both solutions in the Hadoop category. Apache Spark is ranked #1 with an average rating of 8.7, while Spark SQL is ranked #5 with an average rating of 8.0. Apache Spark holds a 13.4% mindshare in H, compared to Spark SQL’s 6.1% mindshare. Additionally, 90% of Apache Spark users are willing to recommend the solution, compared to 86% of Spark SQL users who would recommend it.

Apache Spark

Read 68 Apache Spark reviews

3,663 Views
879 Comparison Views

90% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

674 Views
600 Comparison Views

86% willing to recommend

Apache Spark

Spark SQL

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Executive Summary

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

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Spark vs. Spark SQL Report (Updated: February 2026).

Buyer's Guide

Apache Spark vs. Spark SQL

February 2026

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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

6.9

Number of Reviews

Ranking in other categories

Compute Service (5th), Java Frameworks (2nd)

Spark SQL

Ranking in Hadoop

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the Hadoop category, the mindshare of Apache Spark is 13.4%, down from 18.4% compared to the previous year. The mindshare of Spark SQL is 6.1%, down from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Apache Spark	13.4%
Spark SQL	6.1%
Other	80.5%

Hadoop

Featured Reviews

Devindra Weerasooriya

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.

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Kemal Duman

Team Lead, Data Engineering at Nesine.com

Data pipelines have run faster and support flexible batch and streaming transformations

We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource management in Spark SQL should be better. It consumes more resources, which is normal. The main reason we switched from Spark is memory and CPU consumption. The major reason is the resource problem because the number of streaming jobs has been increasing in our company. That is why we considered resource management as a priority. Because of the resource consumption, I would say the development of Spark SQL is better. For development purposes, it is a top product and not difficult to work with, but resources are the major problem. We changed to Flink regardless of development time. Development time is less in Spark compared with Flink.

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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

"The solution has been very stable."

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."

"Apache Spark, specifically PySpark and the tools available there, have been quite helpful in my event analysis work."

"I found the solution stable. We haven't had any problems with it."

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

"The good performance. The nice graphical management console. The long list of ML algorithms."

"The product's deployment phase is easy."

"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

More Apache Spark pros

"Overall the solution is excellent."

"It is a stable solution."

"The solution is easy to understand if you have basic knowledge of SQL commands."

"The speed of getting data."

"The stability was fine. It behaved as expected."

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

"This solution is useful to leverage within a distributed ecosystem."

"Data validation and ease of use are the most valuable features."

More Spark SQL pros

Cons

"The product could improve the user interface and make it easier for new users."

"I would like to see integration with data science platforms to optimize the processing capability for these tasks."

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

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"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."

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

"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."

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

More Apache Spark cons

"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."

"There should be better integration with other solutions."

"It would be useful if Spark SQL integrated with some data visualization tools."

"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

"Anything to improve the GUI would be helpful."

"In the next release, maybe the visualization of some command-line features could be added."

"There are many inconsistencies in syntax for the different querying tasks."

More Spark SQL cons

Pricing and Cost Advice

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

"It is an open-source platform. We do not pay for its subscription."

"The product is expensive, considering the setup."

"We are using the free version of the solution."

"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."

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

"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."

"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."

More Apache Spark pricing and cost advice

"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."

"The solution is open-sourced and free."

"We use the open-source version, so we do not have direct support from Apache."

"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."

"The solution is bundled with Palantir Foundry at no extra charge."

"There is no license or subscription for this solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

Manufacturing Company

University

Financial Services Firm

15%

University

15%

Retailer

12%

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	15
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	6
Large Enterprise	4

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?

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...

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Product Reports

Buyer's Guide

Apache Spark

January 2026

Download Apache Spark product report

Buyer's Guide

Spark SQL

January 2026

Download Spark SQL 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

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

Apache

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

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Buyer's Guide

Apache Spark vs. Spark SQL

February 2026

Free Report: Apache Spark vs. Spark SQL

Find out what your peers are saying about Apache Spark vs. Spark SQL and other solutions. Updated: February 2026.

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