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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

"ETL and streaming capabilities."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The most valuable feature of Apache Spark is its flexibility."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"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 provides a scalable machine learning library."
"The solution is scalable."
"Data validation and ease of use are the most valuable features."
"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 speed of getting data."
"This solution is useful to leverage within a distributed ecosystem."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"I find the Thrift connection valuable."
"The solution is easy to understand if you have basic knowledge of SQL commands."
 

Cons

"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."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"The migration of data between different versions could be improved."
"The solution must improve its performance."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"Apache Spark's GUI and scalability could be improved."
"The solution’s integration with other platforms should be improved."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"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."
"In the next release, maybe the visualization of some command-line features could be added."
"There should be better integration with other solutions."
"This solution could be improved by adding monitoring and integration for the EMR."
"I've experienced some incompatibilities when using the Delta Lake format."
"It would be useful if Spark SQL integrated with some data visualization tools."
"There are many inconsistencies in syntax for the different querying tasks."
 

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."
"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."
"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."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"The solution is affordable and there are no additional licensing costs."
"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."
"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
"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."
"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."
"The solution is open-sourced and free."
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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.
842,767 professionals have used our research since 2012.