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 with an average rating of 7.0. Apache Spark holds a 19.3% 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 67 Apache Spark reviews

4,144 Views
870 Comparison Views

90% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

639 Views
562 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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Helped 867,370 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

7.3

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 September 2025, in the Hadoop category, the mindshare of Apache Spark is 19.3%, up from 19.4% compared to the previous year. The mindshare of Spark SQL is 9.8%, down from 10.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Apache Spark	19.3%
Spark SQL	9.8%
Other	70.9%

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

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

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

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

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"The deployment of the product is easy."

"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."

"I feel the streaming is its best feature."

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

More Apache Spark pros

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

"The speed of getting data."

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

"Overall the solution is excellent."

"I find the Thrift connection valuable."

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

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

"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 solution needs to optimize shuffling between workers."

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

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

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"Apache Spark lacks geospatial data."

"Apache Spark should add some resource management improvements to the algorithms."

"Apache Spark's GUI and scalability could be improved."

"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

"Anything to improve the GUI would be helpful."

"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."

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

"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."

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

"There should be better integration with other solutions."

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

More Spark SQL cons

Pricing and Cost Advice

"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."

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

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

"Apache Spark is an expensive solution."

"The product is expensive, considering the setup."

"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."

"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."

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

More Apache Spark pricing and cost advice

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

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

"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

18%

University

11%

Retailer

10%

Healthcare 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.

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

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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Product Reports

Buyer's Guide

Apache Spark

September 2025

Download Apache Spark product report

Buyer's Guide

Spark SQL

August 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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