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Apache Spark vs Spark SQL comparison

 

Comparison Buyer's Guide

Executive Summary

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
65
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
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Hadoop category, the mindshare of Apache Spark is 17.5%, down from 21.4% compared to the previous year. The mindshare of Spark SQL is 9.8%, down from 11.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

Ilya Afanasyev - PeerSpot reviewer
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.
Sahil Taneja - PeerSpot reviewer
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"Apache Spark can do large volume interactive data analysis."
"The main feature that we find valuable is that it 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."
"The processing time is very much improved over the data warehouse solution that we were using."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"The product is useful for analytics."
"It provides a scalable machine learning library."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"The stability was fine. It behaved as expected."
"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."
"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."
"The speed of getting data."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
 

Cons

"The solution’s integration with other platforms should be improved."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"There were some problems related to the product's compatibility with a few Python libraries."
"Apache Spark should add some resource management improvements to the algorithms."
"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."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"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."
"I've experienced some incompatibilities when using the Delta Lake format."
"SparkUI could have more advanced versions of the performance and the queries and all."
"In the next release, maybe the visualization of some command-line features could be added."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"This solution could be improved by adding monitoring and integration for the EMR."
"Anything to improve the GUI would be helpful."
 

Pricing and Cost Advice

"They provide an open-source license for the on-premise version."
"The product is expensive, considering the setup."
"Apache Spark is an expensive solution."
"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."
"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."
"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 solution is open-sourced and free."
"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."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
28%
Computer Software Company
13%
Manufacturing Company
8%
Comms Service Provider
5%
Financial Services Firm
23%
Computer Software Company
15%
Retailer
8%
Manufacturing Company
7%
 

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.
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...
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...
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.
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.
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 ...
 

Comparisons

 

Overview

 

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
Find out what your peers are saying about Apache Spark vs. Spark SQL and other solutions. Updated: March 2025.
845,040 professionals have used our research since 2012.