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 10.5% 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 66 Apache Spark reviews

1,404 Views
1,090 Comparison Views

90% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

824 Views
603 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: April 2025).

Buyer's Guide

Apache Spark vs. Spark SQL

April 2025

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Helped 849,686 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 May 2025, in the Hadoop category, the mindshare of Apache Spark is 17.8%, down from 21.4% compared to the previous year. The mindshare of Spark SQL is 10.5%, down from 12.1% 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

"Features include machine learning, real time streaming, and data processing."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"ETL and streaming capabilities."

"The deployment of the product is easy."

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

"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."

"Provides a lot of good documentation compared to other solutions."

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

More Apache Spark pros

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

"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."

"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."

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

"The speed of getting data."

"It is a stable solution."

"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

"The setup I worked on was really complex."

"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."

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

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

"It's not easy to install."

"The solution must improve its performance."

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

"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."

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."

"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."

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

"There should be better integration with other solutions."

"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."

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

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

More Spark SQL cons

Pricing and Cost Advice

"It is an open-source solution, it is free of charge."

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

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

"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."

"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."

"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."

More Apache Spark pricing and cost advice

"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 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 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."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

22%

Computer Software Company

16%

Manufacturing Company

Retailer

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

Comparisons

Spring Boot vs Apache Spark

Compared 26% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

AWS Lambda vs Apache Spark

Compared 6% of the time

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SAP HANA vs Spark SQL

Compared 14% of the time

HPE Ezmeral Data Fabric vs Spark SQL

Compared 13% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 13% of the time

IBM Analytics Engine vs Spark SQL

Compared 10% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

Spark SQL

April 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

April 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: April 2025.

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