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 8.7. Apache Spark holds a 18.3% 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

1,064 Views
784 Comparison Views

90% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

660 Views
440 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: June 2025).

Buyer's Guide

Apache Spark vs. Spark SQL

June 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

1st

Average Rating

8.4

Reviews Sentiment

7.4

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 July 2025, in the Hadoop category, the mindshare of Apache Spark is 18.3%, down from 20.4% compared to the previous year. The mindshare of Spark SQL is 10.5%, down from 11.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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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

"It provides a scalable machine learning library."

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."

"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."

"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."

"The solution has been very stable."

"Apache Spark can do large volume interactive data analysis."

"The product is useful for analytics."

"The solution is very stable."

More Apache Spark pros

"It is a stable solution."

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

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

"The speed of getting data."

"Overall the solution is excellent."

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

"The performance is one of the most important features. It has an API to process the data in a functional manner."

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

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."

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

"Apache Spark lacks geospatial data."

"Dynamic DataFrame options are not yet available."

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."

"The solution must improve its performance."

"The main concern is the overhead of Java when distributed processing is not necessary."

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

More Apache Spark cons

"There should be better integration with other solutions."

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

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

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

"Anything to improve the GUI would be helpful."

"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."

More Spark SQL cons

Pricing and Cost Advice

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

"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."

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

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

"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."

"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."

More Apache Spark pricing and cost advice

"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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

12%

Manufacturing Company

Comms Service Provider

Financial Services Firm

18%

Computer Software Company

12%

Retailer

Healthcare 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.

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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?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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

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Compared 12% of the time

SAP HANA vs Apache Spark

Compared 10% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

AWS Lambda vs Apache Spark

Compared 6% of the time

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SAP HANA vs Spark SQL

Compared 12% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 11% of the time

IBM Analytics Engine vs Spark SQL

Compared 10% of the time

Amazon EMR vs Spark SQL

Compared 9% of the time

HPE Ezmeral Data Fabric vs Spark SQL

Compared 8% of the time

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Product Reports

Buyer's Guide

Apache Spark

June 2025

Download Apache Spark product report

Buyer's Guide

Spark SQL

June 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

June 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: June 2025.

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