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Amazon EMR vs Apache Spark 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)
Apache Spark
Ranking in Hadoop
1st
Average Rating
8.4
Reviews Sentiment
7.7
Number of Reviews
64
Ranking in other categories
Compute Service (4th), Java Frameworks (2nd)
 

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 Apache Spark is 18.2%, down from 21.9% 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 batch processing of data and in-memory processing in Spark greatly enhances performance
Spark supports real-time data processing through Spark Streaming. It allows for batch processing of data. If you have immediate data, like chat information, that needs to be processed in real-time, Spark Streaming is used. For data that can be evaluated later, batch processing with Apache Spark is suitable. Mostly, batch processing is utilized in our organization, but for streaming data processing, tools like Kafka are often integrated. In-memory processing in Spark greatly enhances performance, making it a hundred times faster than the previous MapReduce methods. This improvement is achieved through optimization techniques like caching, broadcasting, and partitioning, which help in optimizing queries for faster processing.

Quotes from Members

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

Pros

"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."
"This is the best tool for hosts and it's really flexible and scalable."
"The project management is very streamlined."
"It allows users to access the data through a web interface."
"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."
"It has a variety of options and support systems."
"The solution is scalable."
"Amazon EMR is a good solution that can be used to manage big data."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The solution is scalable."
"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."
"The solution is very stable."
"I found the solution stable. We haven't had any problems with it."
"The fault tolerant feature is provided."
 

Cons

"The legacy versions of the solution are not supported in the new versions."
"Modules and strategies should be better handled and notified early in advance."
"There is no need to pay extra for third-party software."
"The product must add some of the latest technologies to provide more flexibility to the users."
"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."
"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."
"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."
"There is room for improvement in pricing."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"The solution’s integration with other platforms should be improved."
"The logging for the observability platform could be better."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"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."
"Apache Spark should add some resource management improvements to the algorithms."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
 

Pricing and Cost Advice

"Amazon EMR's price is reasonable."
"The product is not cheap, but it is not 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."
"There is no need to pay extra for third-party software."
"There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."
"Amazon EMR is not very expensive."
"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."
"The price of the solution is expensive."
"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."
"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."
"The solution is affordable and there are no additional licensing costs."
"It is an open-source platform. We do not pay for its subscription."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"We are using the free version of the solution."
"Apache Spark is an open-source tool."
"It is an open-source solution, it is free of 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
27%
Computer Software Company
13%
Manufacturing Company
8%
University
5%
 

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 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 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...
 

Also Known As

Amazon Elastic MapReduce
No data available
 

Overview

 

Sample Customers

Yelp
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Find out what your peers are saying about Amazon EMR vs. Apache Spark and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.