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 8.8, while Spark SQL is ranked #5. Apache Spark holds a 15.5% mindshare in H, compared to Spark SQL’s 7.4% mindshare. Additionally, 90% of Apache Spark users are willing to recommend the solution, compared to 85% of Spark SQL users who would recommend it.

Apache Spark

Read 68 Apache Spark reviews

3,558 Views
783 Comparison Views

90% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

628 Views
553 Comparison Views

85% willing to recommend

Apache Spark

Spark SQL

Comparison Buyer's Guide

Download the report

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

Download the complete report

Helped 879,259 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 (3rd), 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 December 2025, in the Hadoop category, the mindshare of Apache Spark is 15.5%, down from 17.8% compared to the previous year. The mindshare of Spark SQL is 7.4%, down from 9.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Apache Spark	15.5%
Spark SQL	7.4%
Other	77.1%

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.

Read full review

Sahil Taneja

Principal Consultant/Manager at Tenzing

Easy to use and do not require a learning curve

Spark SQL can improve the documentation they have provided. It can be a bit unclear at times. They could improve the documentation a bit more so that we can understand it more easily. Moreover, they could improve SparkUI to have more advanced versions of the performance and the queries and all.

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

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"The solution has been very stable."

"The most valuable feature of Apache Spark is its ease of use."

"This solution provides a clear and convenient syntax for our analytical tasks."

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

"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."

"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 most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

More Apache Spark pros

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

"Overall the solution is excellent."

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

"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."

"It is a stable solution."

"I find the Thrift connection valuable."

More Spark SQL pros

Cons

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

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

"The solution’s integration with other platforms should be improved."

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"There were some problems related to the product's compatibility with a few Python libraries."

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

"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."

More Apache Spark cons

"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."

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

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

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

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

"Anything to improve the GUI would be helpful."

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

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."

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

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

"The product is expensive, considering the setup."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"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."

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

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

More Apache Spark pricing and cost advice

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

"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."

"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."

"The solution is open-sourced and free."

See which vendors are best for you

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

See recommendations

879,259 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

11%

Manufacturing Company

Comms Service Provider

Financial Services Firm

16%

University

14%

Retailer

11%

Manufacturing 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	5
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.

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?

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...

See all answers

Ask a question

Earn 20 points

Comparisons

Spring Boot vs Apache Spark

Compared 18% of the time

AWS Lambda vs Apache Spark

Compared 8% of the time

AWS Batch vs Apache Spark

Compared 8% of the time

SAP HANA vs Apache Spark

Compared 6% of the time

Pentaho Business Analytics vs Apache Spark

Compared 3% of the time

More Apache Spark Competitors

HPE Data Fabric vs Spark SQL

Compared 20% of the time

SAP HANA vs Spark SQL

Compared 18% of the time

Amazon EMR vs Spark SQL

Compared 15% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 15% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Apache Spark

December 2025

Download Apache Spark product report

Buyer's Guide

Spark SQL

December 2025

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.

DOWNLOAD NOW

879,259 professionals have used our research since 2012.

See our Apache Spark vs. Spark SQL report.

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.