Amazon EC2 Auto Scaling vs Apache Spark comparison

Amazon Web Services (AWS) and Apache are both solutions in the Compute Service category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.8, while Apache is ranked #4 with an average rating of 8.6. Amazon Web Services (AWS) holds a 11.2% mindshare in CS, compared to Apache’s 11.3% mindshare. Additionally, 100% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 90% of Apache users who would recommend it.

Amazon EC2 Auto Scaling

Read 44 Amazon EC2 Auto Scaling reviews

1,768 Views
1,531 Comparison Views

100% willing to recommend

Apache Spark

Read 65 Apache Spark reviews

1,646 Views
1,251 Comparison Views

90% willing to recommend

Amazon EC2 Auto Scaling

Apache Spark

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon EC2 Auto Scaling and Apache Spark based on real PeerSpot user reviews.

Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon EC2 Auto Scaling vs. Apache Spark Report (Updated: March 2025).

Buyer's Guide

Amazon EC2 Auto Scaling vs. Apache Spark

March 2025

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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

8.8

Reviews Sentiment

8.2

Number of Reviews

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

Ranking in other categories

Hadoop (1st), Java Frameworks (2nd)

Mindshare comparison

As of March 2025, in the Compute Service category, the mindshare of Amazon EC2 Auto Scaling is 11.2%, down from 11.9% compared to the previous year. The mindshare of Apache Spark is 11.3%, up from 9.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

Erick Karanja

Technical Lead at Cellulant Kenya

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.

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Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

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.

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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

"Amazon EC2 Auto Scaling has good integration."

"Sometimes, Auto Scaling is more beneficial, and sometimes, Reserved Instances are preferred, especially for longer-term usage."

"The most valuable features include the availability of various services like compute, memory, and database services in the AWS landscape."

"Can handle traffic spikes so the system doesn't overload."

"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."

"The product is flexible."

"The tool is a simple solution. You can deploy a new virtual machine in minutes. You can select options like memory and core number and connect storage. It's great because it's simple and fast. You can build a virtual machine in minutes, do your experiments, and then uninstall it without paying for it when it's not in use."

"Service for launching or terminating Amazon EC2 instances, with good scalability and stability."

More Amazon EC2 Auto Scaling pros

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."

"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."

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"The fault tolerant feature is provided."

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."

More Apache Spark pros

Cons

"The price could always be a bit better."

"I would like to see the security portal improved in the future."

"EC2 is doing what it is intended to do, and I have no specific improvements to suggest."

"The support to manage the processes could be better."

"The tool must provide proper guidelines to troubleshoot connectivity issues."

"There is a need for improvement in understanding the pricing structure, as it is complex and depends on several factors such as the location of data centers."

"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."

"Amazon EC2 Auto Scaling offers various benefits but lacks certain features for fine-grained customization compared to other cloud providers like GCP. Users are constrained by predefined instance families in EC2 when selecting instance types for scaling. Unlike GCP, where users can independently scale resources such as memory or CPU, EC2 doesn't offer this flexibility."

More Amazon EC2 Auto Scaling cons

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

More Apache Spark cons

Pricing and Cost Advice

"Compared to the performance, the price is quite high. I would rate it a ten because it is expensive. There are additional costs including bandwidth costs, data transfer costs, and load balancing costs."

"I have not explored the price of the solution extensively, but from what I have seen the price is alright."

"The price of this product could be a little bit lower."

"Licensing fees are paid on a yearly basis."

"When we want to use more services, we need to pay more. It's a monthly subscription, rather than licensed-based. Pricing or fees for Amazon EC2 Auto Scaling could be improved."

"The product's pricing depends on the traffic and workload."

"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's licensing is based on a pay-as-you-go model. You only pay for the resources you use, whether it's RAM, processing power, or storage. So, it's calculated based on the time you use those resources, typically billed in hours or minutes."

More Amazon EC2 Auto Scaling pricing and cost advice

"Apache Spark is an expensive solution."

"Apache Spark is an open-source tool."

"The product is expensive, considering the setup."

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

"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."

"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."

"They provide an open-source license for the on-premise version."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

24%

Computer Software Company

15%

Government

University

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

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...

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What is your experience regarding pricing and costs for Amazon EC2 Auto Scaling?

The pricing structure from AWS is really complex and depends on factors like the region and specific services used. Prices can vary significantly even within the same service across different locat...

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What needs improvement with Amazon EC2 Auto Scaling?

There is a need for improvement in understanding the pricing structure, as it is complex and depends on several factors such as the location of data centers.

See all answers

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

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...

See all answers

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...

See all answers

Comparisons

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Compared 67% of the time

AWS Lambda vs Amazon EC2 Auto Scaling

Compared 22% of the time

AWS Batch vs Amazon EC2 Auto Scaling

Compared 7% of the time

Apache NiFi vs Amazon EC2 Auto Scaling

Compared 4% of the time

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Spring Boot vs Apache Spark

Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Amazon EC2 Auto Scaling

March 2025

Download Amazon EC2 Auto Scaling product report

Buyer's Guide

Apache Spark

March 2025

Download Apache Spark product report

Also Known As

AWS RAM

No data available

Overview

Amazon EC2 Auto Scaling helps you maintain application availability and allows you to automatically add or remove EC2 instances according to conditions you define. ... Dynamic scaling responds to changing demand and predictive scaling automatically schedules the right number of EC2 instances based on predicted demand.

Amazon Web Services (AWS)

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

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

Buyer's Guide

Amazon EC2 Auto Scaling vs. Apache Spark

March 2025

Free Report: Amazon EC2 Auto Scaling vs. Apache Spark

Find out what your peers are saying about Amazon EC2 Auto Scaling vs. Apache Spark and other solutions. Updated: March 2025.

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