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. Apache Spark holds a 13.9% mindshare in H, compared to Spark SQL’s 6.6% 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

3,530 Views
812 Comparison Views

90% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

637 Views
554 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: December 2025).

Buyer's Guide

Apache Spark vs. Spark SQL

December 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

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 January 2026, in the Hadoop category, the mindshare of Apache Spark is 13.9%, down from 18.2% compared to the previous year. The mindshare of Spark SQL is 6.6%, down from 10.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Apache Spark	13.9%
Spark SQL	6.6%
Other	79.5%

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

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

"The solution is scalable."

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

"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 deployment of the product is easy."

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

"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."

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

More Apache Spark pros

"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."

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

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

"I find the Thrift connection valuable."

"It is a stable solution."

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

"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."

"The speed of getting data."

More Spark SQL pros

Cons

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

"At the initial stage, the product provides no container logs to check the activity."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

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

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

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

"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."

More Apache Spark cons

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

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

"There should be better integration with other solutions."

"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

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

"Anything to improve the GUI would be helpful."

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

"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."

More Spark SQL cons

Pricing and Cost Advice

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

"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."

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

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

"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."

"Apache Spark is an open-source tool."

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

More Apache Spark pricing and cost advice

"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."

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

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

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

Manufacturing Company

Comms Service Provider

Financial Services Firm

16%

University

16%

Retailer

13%

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

December 2025

Free Report: Apache Spark vs. Spark SQL

Find out what your peers are saying about Apache Spark vs. Spark SQL and other solutions. Updated: December 2025.

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