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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Helped 881,707 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

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 most valuable feature of this solution is its capacity for processing large amounts of data."

"The processing time is very much improved over the data warehouse solution that we were using."

"The deployment of the product is easy."

"Apache Spark's ability to handle both batch and streaming data is the most valuable feature for me as it offers solid real-time processing capability, making it more efficient in managing data analytics."

"The solution is scalable."

"I feel the streaming is its best feature."

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

"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

"The speed of getting data."

"Overall the solution is excellent."

"The performance is one of the most important features. It has an API to process the data in a functional manner."

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

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

"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."

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

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

More Spark SQL pros

Cons

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

"The Spark solution could improve in scheduling tasks and managing dependencies."

"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."

"Very often in many of my experiments, the data set has had to be partitioned, and there have been issues in handling very large data sets, with most of my work done using Python machine learning libraries, requiring chunking, and speed of prediction has been an issue of concern in some experiments where we have had to shut down processes due to CPU requirements, then restart with different Apache configurations, and resourcing support is a major determinant if I were to name a constraint in terms of running machine learning experiments."

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

"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."

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

More Apache Spark cons

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

"There should be better integration with other solutions."

"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."

"Anything to improve the GUI would be helpful."

"I've experienced some incompatibilities when using the Delta Lake format."

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

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

"SparkUI could have more advanced versions of the performance and the queries and all."

More Spark SQL cons

Pricing and Cost Advice

"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 not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

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

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

"The product is expensive, considering the setup."

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

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

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

More Apache Spark pricing and cost advice

"The solution is open-sourced and free."

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

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

"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 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."

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

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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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