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 8.6, while Spark SQL is ranked #5 with an average rating of 8.5. Apache Spark holds a 17.8% mindshare in H, compared to Spark SQL’s 9.8% 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 65 Apache Spark reviews

1,580 Views
1,196 Comparison Views

90% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

884 Views
657 Comparison Views

85% 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: March 2025).

Buyer's Guide

Apache Spark vs. Spark SQL

March 2025

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Helped 842,296 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

7.7

Number of Reviews

Ranking in other categories

Compute Service (4th), 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 March 2025, in the Hadoop category, the mindshare of Apache Spark is 17.8%, down from 21.2% compared to the previous year. The mindshare of Spark SQL is 9.8%, down from 11.7% 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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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.

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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

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

"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."

"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."

"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."

"ETL and streaming capabilities."

"The solution is very stable."

"The solution has been very stable."

"The product is useful for analytics."

More Apache Spark pros

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

"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."

"The speed of getting data."

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

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

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

"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."

More Spark SQL pros

Cons

"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."

"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."

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

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

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

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

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

"The setup I worked on was really complex."

More Apache Spark cons

"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."

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

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

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

"This solution could be improved by adding monitoring and integration for the EMR."

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

"There should be better integration with other solutions."

"Anything to improve the GUI would be helpful."

More Spark SQL cons

Pricing and Cost Advice

"Apache Spark is an open-source tool."

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

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

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

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

"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."

"The product is expensive, considering the setup."

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."

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

28%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

23%

Computer Software Company

15%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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...

See all answers

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...

See all answers

What do you like most about Spark SQL?

Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.

See all answers

What is your experience regarding pricing and costs for Spark SQL?

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.

See all answers

What needs improvement with Spark SQL?

In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...

See all answers

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Compared 12% of the time

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Compared 8% of the time

AWS Lambda vs Apache Spark

Compared 6% of the time

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HPE Ezmeral Data Fabric vs Spark SQL

Compared 12% of the time

SAP HANA vs Spark SQL

Compared 10% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 10% of the time

Netezza Analytics vs Spark SQL

Compared 9% of the time

IBM Analytics Engine vs Spark SQL

Compared 8% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Apache Spark

March 2025

Download Apache Spark product report

Buyer's Guide

Spark SQL

March 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

March 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: March 2025.

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