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Compare Apache Spark vs. Spark SQL

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Apache Spark Logo
9,924 views|7,995 comparisons
Spark SQL Logo
687 views|285 comparisons
Featured Review
Find out what your peers are saying about Apache Spark vs. Spark SQL and other solutions. Updated: November 2021.
552,305 professionals have used our research since 2012.
Quotes From Members

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

Pros
"The processing time is very much improved over the data warehouse solution that we were using.""I feel the streaming is its best feature.""The solution is very stable.""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.""The solution has been very stable.""The features we find most valuable are the machine learning, data learning, and Spark Analytics.""I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library.""The main feature that we find valuable is that it is very fast."

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"It is a stable solution.""The performance is one of the most important features. It has an API to process the data in a functional manner.""The speed of getting data.""Data validation and ease of use are the most valuable features.""Overall the solution is excellent."

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Cons
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate.""The logging for the observability platform could be better.""I would like to see integration with data science platforms to optimize the processing capability for these tasks.""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.""The solution needs to optimize shuffling between workers.""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's not easy to install.""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."

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"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 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.""Anything to improve the GUI would be helpful.""The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

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

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"The solution is open-sourced and free."

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Questions from the Community
Top Answer: I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
Top Answer: The solution has been very stable.
Top Answer: We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud.
Top Answer: Data validation and ease of use are the most valuable features.
Top Answer: There should be better integration with other solutions.
Ranking
1st
out of 22 in Hadoop
Views
9,924
Comparisons
7,995
Reviews
10
Average Words per Review
487
Rating
8.6
3rd
out of 22 in Hadoop
Views
687
Comparisons
285
Reviews
5
Average Words per Review
297
Rating
7.0
Comparisons
Learn More
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.
Offer
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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
Top Industries
REVIEWERS
Financial Services Firm40%
Computer Software Company20%
Marketing Services Firm10%
Non Profit10%
VISITORS READING REVIEWS
Computer Software Company23%
Comms Service Provider20%
Financial Services Firm11%
Media Company9%
VISITORS READING REVIEWS
Computer Software Company31%
Comms Service Provider28%
Financial Services Firm8%
Media Company5%
Company Size
REVIEWERS
Small Business38%
Midsize Enterprise21%
Large Enterprise41%
No Data Available
Find out what your peers are saying about Apache Spark vs. Spark SQL and other solutions. Updated: November 2021.
552,305 professionals have used our research since 2012.

Apache Spark is ranked 1st in Hadoop with 10 reviews while Spark SQL is ranked 3rd in Hadoop with 5 reviews. Apache Spark is rated 8.6, while Spark SQL is rated 7.0. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, the top reviewer of Spark SQL writes "GUI could be improved. Useful for speedily processing big data". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and SAP HANA, whereas Spark SQL is most compared with IBM Db2 Big SQL, Amazon EMR, Informatica Big Data Parser, Netezza Analytics and AtScale Adaptive Analytics (A3). See our Apache Spark vs. Spark SQL report.

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