Amazon EMR vs Spark SQL comparison

Amazon Web Services (AWS) and Apache are both solutions in the Hadoop category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while Apache is ranked #4 with an average rating of 8.3. Amazon Web Services (AWS) holds a 14.7% mindshare in H, compared to Apache’s 9.9% mindshare. Additionally, 85% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

Amazon EMR

Read 22 Amazon EMR reviews

1,373 Views
1,187 Comparison Views

85% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

1,046 Views
777 Comparison Views

85% willing to recommend

Amazon EMR

Spark SQL

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon EMR 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 Amazon EMR vs. Spark SQL Report (Updated: December 2024).

Buyer's Guide

Amazon EMR vs. Spark SQL

December 2024

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Categories and Ranking

Amazon EMR

Ranking in Hadoop

3rd

Average Rating

7.8

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (11th)

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 December 2024, in the Hadoop category, the mindshare of Amazon EMR is 14.7%, down from 18.7% compared to the previous year. The mindshare of Spark SQL is 9.9%, down from 12.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Prashant Singh

Vice President -Product Management at Paytm

Easy to manage and reliable but the cost is hard to control

The cost is increasing. We are looking into how we can optimize the cost part of EMR. We're doing a comparison between Cloudera running on AWS and running AWS EMR. We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part.

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Lucas Dreyer

Data Engineer at BBD

Processing solution used for data engineering and transformation with the ability to process large datasets

It takes a bit of time to get used to using this solution versus Panda as it has a steep learning curve. You need quite a high level of skill with SQL in general to use this solution. If SQL is not someone's primary language, they might find it difficult to get used to. This solution could be improved if there was a bridge between Panda and Spark SQL such as translating from Panda operations to SQL and then working with those queries that are generated. In a future release, it would be useful to have a real time dashboard versus batch updates to Power BI.

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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 allows users to access the data through a web interface."

"We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot."

"Amazon EMR is a good solution that can be used to manage big data."

"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."

"The solution helps us manage huge volumes of data."

"Amazon EMR has multiple connectors that can connect to various data sources."

"The initial setup is pretty straightforward."

"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."

More Amazon EMR pros

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

"Overall the solution is excellent."

"This solution is useful to leverage within a distributed ecosystem."

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

"I find the Thrift connection valuable."

"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."

"It is a stable solution."

"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."

More Spark SQL pros

Cons

"The product's features for storing data in static clusters could be better."

"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."

"Amazon EMR can improve by adding some features, such as megastore services and HiveServer2. Additionally, the user interface could be better, similar to what Apache service provides, cross-platform services."

"There is no need to pay extra for third-party software."

"The legacy versions of the solution are not supported in the new versions."

"Spark jobs take longer on Amazon EMR compared to previous experiences."

"There is room for improvement in pricing."

"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."

More Amazon EMR cons

"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."

"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."

"Anything to improve the GUI would be helpful."

"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."

"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

"There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."

"You don't need to pay for licensing on a yearly or monthly basis, you only pay for what you use, in terms of underlying instances."

"Amazon EMR's price is reasonable."

"I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you tens of thousands of dollars. That's happened to us twice, so I'm sensitive to it. We're still trying to work on that. Our smallest client probably spends a hundred thousand dollars yearly on licensing, while our largest is well over a million."

"There is no need to pay extra for third-party software."

"The cost of Amazon EMR is very high."

"The price of the solution is expensive."

"Amazon EMR is not very expensive."

More Amazon EMR pricing and cost advice

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

"The solution is open-sourced and free."

"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."

"We use the open-source version, so we do not have direct support from Apache."

"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

25%

Computer Software Company

13%

Manufacturing Company

Educational Organization

Financial Services Firm

27%

Computer Software Company

15%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Amazon EMR?

Amazon EMR is a good solution that can be used to manage big data.

See all answers

What is your experience regarding pricing and costs for Amazon EMR?

The cost of Amazon EMR is a little bit expensive, especially considering the support package, which includes a gold package.

See all answers

What needs improvement with Amazon EMR?

Spark jobs take longer on Amazon EMR compared to previous experiences. This aspect could be improved to make them more efficient.

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

Snowflake vs Amazon EMR

Compared 37% of the time

Cloudera Distribution for Hadoop vs Amazon EMR

Compared 29% of the time

Azure Data Factory vs Amazon EMR

Compared 7% of the time

Amazon Redshift vs Amazon EMR

Compared 7% of the time

Apache Spark vs Amazon EMR

Compared 4% of the time

More Amazon EMR Competitors

Apache Spark vs Spark SQL

Compared 59% of the time

SAP HANA vs Spark SQL

Compared 11% of the time

HPE Ezmeral Data Fabric vs Spark SQL

Compared 11% of the time

Netezza Analytics vs Spark SQL

Compared 9% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 9% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Amazon EMR

December 2024

Download Amazon EMR product report

Buyer's Guide

Spark SQL

November 2024

Download Spark SQL product report

Also Known As

Amazon Elastic MapReduce

No data available

Learn More

Amazon Web Services (AWS)

Apache

Overview

Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data. Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances.

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

Yelp

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Buyer's Guide

Amazon EMR vs. Spark SQL

December 2024

Free Report: Amazon EMR vs. Spark SQL

Find out what your peers are saying about Amazon EMR vs. Spark SQL and other solutions. Updated: December 2024.

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