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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
4th
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

"The product is flexible."
"Auto-scaling is a good feature."
"It uses features like target tracking scaling policy, which automatically maintains CPU utilization levels."
"The initial setup is straightforward."
"The solution includes many features for configuring networks and VPCs."
"Most of what I've deployed are CI/CD pipelines. AWS is scalable. You can always increase or adjust the resources to meet the specific requirements. I also like choosing an instance in any location, preferably the closest one. We don't have any AWS locations in South Africa, but the latency is about the same as hosting in Europe."
"The solution is highly scalable."
"Can handle traffic spikes so the system doesn't overload."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"The product's initial setup phase was easy."
"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."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"We use Spark to process data from different data sources."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
 

Cons

"The product's technical support needs to be better."
"There should be an AWS instance in South Africa, where the latency would be even lower. It might happen soon since AWS has recently opened more data centres in Nigeria. AWS may extend its reach to South Africa, and offer hosted CLI servers there. Most of the problems with AWS are not to do with the solution itself but with configuration. It is something on design, more or less."
"As we are transitioning to managing containerized applications, the solution could improve by adding more managed container services as a feature in the solution."
"The licensing cost is expensive."
"Sometimes the configuration is not intuitive."
"The pricing could be reduced."
"Its stability and scalability need improvement."
"The tool must provide proper guidelines to troubleshoot connectivity issues."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"The solution must improve its performance."
"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."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."
"Apache Spark should add some resource management improvements to the algorithms."
"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."
"Dynamic DataFrame options are not yet available."
 

Pricing and Cost Advice

"The pricing is not fixed and it is based on usage."
"Amazon EC2 Auto Scaling uses a pay-as-you-go pricing model."
"Licensing fees are paid on a yearly basis."
"The product is quite 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."
"The solution is not expensive."
"Its price is affordable for enterprise customers."
"AWS offered some credits, so we have been able to enjoy some of those benefits. The pricing was fair."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
"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."
"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."
"The product is expensive, considering the setup."
"Apache Spark is an open-source tool."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
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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
27%
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?
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 Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...
 

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.
849,686 professionals have used our research since 2012.