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Amazon EC2 Auto Scaling vs Apache Spark comparison

 

Comparison Buyer's Guide

Executive Summary

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

Amazon EC2 Auto Scaling
Ranking in Compute Service
3rd
Average Rating
9.0
Reviews Sentiment
8.2
Number of Reviews
46
Ranking in other categories
No ranking in other categories
Apache Spark
Ranking in Compute Service
5th
Average Rating
8.4
Reviews Sentiment
7.7
Number of Reviews
66
Ranking in other categories
Hadoop (1st), Java Frameworks (2nd)
 

Mindshare comparison

As of May 2025, in the Compute Service category, the mindshare of Amazon EC2 Auto Scaling is 10.5%, down from 13.4% compared to the previous year. The mindshare of Apache Spark is 11.3%, up from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Compute Service
 

Featured Reviews

Erick  Karanja - PeerSpot reviewer
Scaling is as easy as hitting a button and setup is straightforward
AWS has already made improvements. In the past, if you provisioned a large EC2 instance and underutilized it, you still paid a premium. Now, AWS encourages using Kubernetes, where you primarily pay for the compute power you actually use in production. There is room for improvement. You might end up paying a high price if you're not careful and you provision a server that's underutilized. AWS has left it to engineers to figure out solutions. If you find the cost too high, you can move to Kubernetes, which might be a better solution for you than large EC2 instances. So, the improvements need to come from the user side, not the provider. Software engineers and engineering teams need to know their limits with EC2 instances. They need to recognize when it's time to transition their applications to Kubernetes. This means building with the cloud in mind from the start, making it easier to move solutions to the cloud without suffering upgrades and integration issues.
Ilya Afanasyev - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"Having a load balancer in between is very helpful when you have huge traffic."
"The initial setup is straightforward."
"With the ability to set up rules based on demand, network, or traffic, the service offers a necessary level of adaptability."
"The solution incorporates ease of maintenance and reduction in operational overhead and costs. Patching is also easy."
"The feature I found most valuable was the vertical and horizontal scaling."
"The most valuable features are that it is stable, flexible, and reliable."
"The solution includes many features for configuring networks and VPCs."
"One of the most important benefits is that a company can optimize resources because Auto Scaling deploys resources when needed. For example, for Black Friday, a company can deploy 100 servers for a couple of days. When Black Friday is over, the company can delete those servers."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The solution is very stable."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"Apache Spark can do large volume interactive data analysis."
"The processing time is very much improved over the data warehouse solution that we were using."
"The fault tolerant feature is provided."
"The product is useful for analytics."
 

Cons

"Automation is very hard."
"There is room for improvement in the pricing model."
"If your EC2 instance doesn't boot up, you're in the dark about what's happening. It would be amazing if you could get a view of the console to see the status. There's something called the AWS Console, which is a web portal. I would like to see a virtual screen of an instance that hasn't started properly, so I can see where it crashed."
"The launch configuration feature doesn't work properly. It needs to improve the load configuration feature along with launch templates. The tool needs to tag feature as well."
"It should work for the cloud, cloud monitoring features, and DevOps processes. It should automatically enable features for downscaling and upscaling."
"Its stability and scalability need improvement."
"The licensing cost is expensive."
"The spinning up in the solution can be much faster...The product should have a faster scalability option."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"Dynamic DataFrame options are not yet available."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"It's not easy to install."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"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."
 

Pricing and Cost Advice

"The pricing is not fixed and it is based on usage."
"Its price is affordable for enterprise customers."
"The product is expensive."
"As far back as I can remember, I have experience with two types of subscriptions. The first was my personal AWS base, and the second was a corporate license. I can't say much about the corporate license, but I recall they sent the bill every month for the personal subscription, though I could be mistaken."
"There is no specific pricing for Amazon EC2 Auto Scaling, but we have to pay for the number of machines getting scaled up."
"Amazon EC2 Auto Scaling uses a pay-as-you-go pricing model."
"Licensing fees are paid on a yearly basis."
"It's cost-effective."
"Spark is an open-source solution, so there are no licensing costs."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"We are using the free version of the solution."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"The solution is affordable and there are no additional licensing costs."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"It is an open-source solution, it is free of charge."
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Top Industries

By visitors reading reviews
Financial Services Firm
26%
Computer Software Company
15%
Government
7%
Real Estate/Law Firm
6%
Financial Services Firm
26%
Computer Software Company
13%
Manufacturing Company
8%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon EC2 Auto Scaling?
The solution removes the need for hardware. We can easily create servers or machines. Just by clicking or specifying our requirements, like memory size or disk space, it's set up for us. The tool e...
What is your experience regarding pricing and costs for Amazon EC2 Auto Scaling?
The pricing of Amazon EC2 Auto Scaling is moderate. It's not too expensive because we only pay for what we use. While there are cheaper options, the services provided are worth the cost. Previously...
What needs improvement with Amazon EC2 Auto Scaling?
While Amazon EC2 Auto Scaling is continually updated and has improved over time, the dashboard has become more complex and tricky for new users. The interface was easier to navigate in earlier vers...
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?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...
 

Also Known As

AWS RAM
No data available
 

Overview

 

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
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 EC2 Auto Scaling vs. Apache Spark and other solutions. Updated: April 2025.
851,604 professionals have used our research since 2012.