Apache Spark vs Spark SQL comparison

Apache Spark and Spark SQL are both solutions in the Hadoop category. Apache Spark is ranked #2 with an average rating of 8.8, while Spark SQL is ranked #5 with an average rating of 7.0. Apache Spark holds a 19.0% mindshare in H, compared to Spark SQL’s 9.4% 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 67 Apache Spark reviews

3,754 Views
788 Comparison Views

90% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

593 Views
522 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: September 2025).

Buyer's Guide

Apache Spark vs. Spark SQL

September 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

2nd

Average Rating

8.4

Reviews Sentiment

6.9

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 October 2025, in the Hadoop category, the mindshare of Apache Spark is 19.0%, up from 18.7% compared to the previous year. The mindshare of Spark SQL is 9.4%, down from 10.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Apache Spark	19.0%
Spark SQL	9.4%
Other	71.6%

Hadoop

Featured Reviews

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Empowering data consolidation and fast decision-making with efficient big data processing

I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.

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SurjitChoudhury

Data engineer at Cocos pt

Offers the flexibility to handle large-scale data processing

My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

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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

"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 tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

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

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

"The most valuable feature of Apache Spark is its flexibility."

More Apache Spark pros

"I find the Thrift connection valuable."

"The speed of getting data."

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

"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."

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

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

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

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

More Spark SQL pros

Cons

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

"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 logging for the observability platform could be better."

"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."

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

"It's not easy to install."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"The solution needs to optimize shuffling between workers."

More Apache Spark cons

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

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

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

"There should be better integration with other solutions."

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

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

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

"Anything to improve the GUI would be helpful."

More Spark SQL cons

Pricing and Cost Advice

"Apache Spark is an expensive 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."

"The product is expensive, considering the setup."

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

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

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

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

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

More Apache Spark pricing and cost advice

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

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

"The solution is open-sourced and free."

"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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

11%

Manufacturing Company

Comms Service Provider

Financial Services Firm

17%

University

12%

Retailer

11%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	27
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.

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What needs improvement with Apache Spark?

Regarding Apache Spark, I have only used Apache Spark Structured Streaming, not the machine learning components. I am uncertain about specific improvements needed today. However, after five years, ...

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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.

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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 ...

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What is your primary use case for Spark SQL?

I employ Spark SQL for various tasks. Initially, I gathered data from databases, SAP systems, and external sources via SFTP, storing it in blob storage. Using Spark SQL within Jupyter notebooks, I ...

See all answers

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Compared 7% of the time

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Compared 21% of the time

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Compared 17% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Apache Spark

September 2025

Download Apache Spark product report

Buyer's Guide

Spark SQL

September 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

September 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: September 2025.

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