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Amazon EMR vs Spark SQL comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Amazon EMR
Ranking in Hadoop
3rd
Average Rating
7.8
Number of Reviews
21
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
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Hadoop category, the mindshare of Amazon EMR is 14.4%, down from 18.9% compared to the previous year. The mindshare of Spark SQL is 9.9%, down from 12.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

Quan Vu - PeerSpot reviewer
Provides efficient data processing features and has good scalability
We need to have a data pipeline tool to ensure consistent data processing for the initial setup. We create a framework, read the code, and execute it in a data catalog. The size of the maintenance team depends on the project and the use cases. Usually, one backup team of four or five DevOps executives takes care of the backend and database. We need to separate our environments into production and development. We use GitHub for source control, Jenkins for the deployment pipeline, and a standard CI/CD tool to deploy code changes into production. We need to develop a deployment framework so developers only need to provide the code for their projects. The underlying engine then deploys the code, reads it, addresses the EMR filter, executes it, and completes the data processing.
SurjitChoudhury - PeerSpot reviewer
Offers the flexibility to handle large-scale data processing
My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

Quotes from Members

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

Pros

"The solution is pretty simple to set up."
"Amazon EMR is a good solution that can be used to manage big data."
"The security of the managed workflow and the managed services are the best features for us. Since we inherited their security model and it's all managed services, those are the key benefits for our clients."
"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."
"Amazon EMR's most valuable features are processing speed and data storage capacity."
"It allows users to access the data through a web interface."
"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"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."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"The speed of getting data."
"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 stability was fine. It behaved as expected."
"It is a stable solution."
"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 solution is easy to understand if you have basic knowledge of SQL commands."
 

Cons

"The initial setup was time-consuming."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"The legacy versions of the solution are not supported in the new versions."
"The most complicated thing is configuring to the cluster and ensure it's running correctly."
"There is no need to pay extra for third-party software."
"There is room for improvement in pricing."
"The product must add some of the latest technologies to provide more flexibility to the users."
"Modules and strategies should be better handled and notified early in advance."
"This solution could be improved by adding monitoring and integration for the EMR."
"There should be better integration with other solutions."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"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."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"I've experienced some incompatibilities when using the Delta Lake format."
"Anything to improve the GUI would be helpful."
"It would be useful if Spark SQL integrated with some data visualization tools."
 

Pricing and Cost Advice

"Amazon EMR is not very expensive."
"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."
"The product is not cheap, but it is not expensive."
"The cost of Amazon EMR is very high."
"There is no need to pay extra for third-party software."
"Amazon EMR's price is reasonable."
"The price of the solution is expensive."
"There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."
"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."
"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."
"There is no license or subscription for this solution."
"The solution is open-sourced and free."
"The solution is bundled with Palantir Foundry at no extra charge."
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Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
13%
Manufacturing Company
9%
Educational Organization
7%
Financial Services Firm
25%
Computer Software Company
17%
Manufacturing Company
6%
Retailer
6%
 

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.
What is your experience regarding pricing and costs for Amazon EMR?
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...
What needs improvement with Amazon EMR?
The solution can become expensive if you are not careful.
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

 

Also Known As

Amazon Elastic MapReduce
No data available
 

Overview

 

Sample Customers

Yelp
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Find out what your peers are saying about Amazon EMR vs. Spark SQL and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.