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 8.7, while Spark SQL is ranked #4 with an average rating of 8.4. Apache Spark holds a 18.4% mindshare in H, compared to Spark SQL’s 10.4% 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 64 Apache Spark reviews

1,728 Views
1,331 Comparison Views

90% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

965 Views
733 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: January 2025).

Buyer's Guide

Apache Spark vs. Spark SQL

January 2025

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Helped 831,265 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

7.7

Number of Reviews

Ranking in other categories

Compute Service (4th), Java Frameworks (2nd)

Spark SQL

Ranking in Hadoop

4th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2025, in the Hadoop category, the mindshare of Apache Spark is 18.4%, down from 21.5% compared to the previous year. The mindshare of Spark SQL is 10.4%, down from 12.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

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.

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Sahil Taneja

Principal Consultant/Manager at Tenzing

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.

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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 crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."

"The scalability has been the most valuable aspect of the solution."

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

"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."

"The product's initial setup phase was easy."

"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 good performance. The nice graphical management console. The long list of ML algorithms."

"ETL and streaming capabilities."

More Apache Spark pros

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

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

"I find the Thrift connection valuable."

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

"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."

"Overall the solution is excellent."

"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

"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."

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

"The solution’s integration with other platforms should be improved."

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

"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."

More Apache Spark cons

"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."

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

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

"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."

"There should be better integration with other solutions."

"In the next release, maybe the visualization of some command-line features could be added."

"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."

More Spark SQL cons

Pricing and Cost Advice

"Apache Spark is an expensive solution."

"It is an open-source solution, it is free of charge."

"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."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

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

"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."

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."

"There is no license or subscription for this solution."

"The solution is open-sourced and free."

"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 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."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

University

Financial Services Firm

28%

Computer Software Company

15%

Retailer

Manufacturing 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.

See all answers

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...

See all answers

What needs improvement with Apache Spark?

The main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...

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

Spring Boot vs Apache Spark

Compared 28% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 9% of the time

Azure Stream Analytics vs Apache Spark

Compared 5% of the time

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HPE Ezmeral Data Fabric vs Spark SQL

Compared 12% of the time

Netezza Analytics vs Spark SQL

Compared 11% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 10% of the time

SAP HANA vs Spark SQL

Compared 10% of the time

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Product Reports

Buyer's Guide

Apache Spark

January 2025

Download Apache Spark product report

Buyer's Guide

Spark SQL

December 2024

Download Spark SQL product report

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Apache

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

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.

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

January 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: January 2025.

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