Amazon EC2 vs Apache Spark comparison

Amazon Web Services (AWS) and Apache are both solutions in the Compute Service category. Amazon Web Services (AWS) is ranked #5 with an average rating of 8.8, while Apache is ranked #3 with an average rating of 8.6. Amazon Web Services (AWS) holds a 6.3% mindshare in CS, compared to Apache’s 11.3% mindshare. Additionally, 98% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 90% of Apache users who would recommend it.

Amazon EC2

Read 65 Amazon EC2 reviews

1,375 Views
967 Comparison Views

98% willing to recommend

Apache Spark

Read 65 Apache Spark reviews

1,769 Views
1,353 Comparison Views

90% willing to recommend

Amazon EC2

Apache Spark

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Executive Summary

We performed a comparison between Amazon EC2 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 vs. Apache Spark Report (Updated: January 2025).

Buyer's Guide

Amazon EC2 vs. Apache Spark

January 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

Ranking in Compute Service

5th

Average Rating

8.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark

Ranking in Compute Service

3rd

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

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

Mindshare comparison

As of February 2025, in the Compute Service category, the mindshare of Amazon EC2 is 6.3%, down from 6.8% compared to the previous year. The mindshare of Apache Spark is 11.3%, up from 8.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

KatlegoMabila

Senior AWS Account Manager at Silicon Overdrive

Offers customization and flexibility with great support

Scalability depends on whether the client wants to scale up or scale down. It decreases resources based on demand. The great aspect of scalability is the flexibility to allow business success to optimize resource solutions and cost efficiency. Another crucial aspect of scalability is auto-scaling. When you have the opportunity to auto-scale, it can't always be available for everything. If you have chosen to integrate with auto-scaling, it's marvellous and doesn't require additional effort. Auto-scaling gives you the edge by using the capacity you have efficiently, scaling up or down as needed. These flexibilities within the EC2 feature instances of AWS play a crucial role in helping me utilize AWS EC2 Intelligent efficiently.

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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 is highly scalable."

"The solution offers good access policies."

"Amazon EC2 has plenty of scalability options to decrease or increase the number we need. Based on our bandwidth usage we may go for a higher or downgrade level. We can do it ourselves."

"The serverless architecture solutions are most valuable, and the ability to start with little cost, and then expand as needed."

"My favorite feature of this solution is the flexibility of instance types, which allows for the cost to be tailored to the usage amount and type."

"The amount of bandwidth has been most valuable."

"The most valuable feature of Amazon EC2 is the virtual machines that are available."

"The ability to bring up servers and then do the computation and deposit means we don't have to maintain a data center. Everything is virtual and the security is also taken care of. It helps us to achieve compliance. Being a small startup with the security features that AWS provides helps us with compliance."

More Amazon EC2 pros

"This solution provides a clear and convenient syntax for our analytical tasks."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

"The product's deployment phase is easy."

"The product’s most valuable features are lazy evaluation and workload distribution."

"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."

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

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"It provides a scalable machine learning library."

More Apache Spark pros

Cons

"Lacks sufficient tools to help users minimize their costs."

"The solution is pretty expensive."

"The support could be more responsive."

"Built-in and/or integration with other services to proactively identify potential failures before they occur."

"One of the challenges is the AMI upgrades."

"If the solution was cheaper, if the price was less, it would be better."

"They can build automatic features for ENSS or network drive. They have the Control-M feature. Similarly, they should have a feature for the network drive that can be mapped. I have not seen such a feature. They have a lot of products but those are quite costly. There is no cheaper option available for the EC2 instance for syncing two drives. If these features are available, it would be good."

"We found Amazon EC2 to be pricey."

More Amazon EC2 cons

"They could improve the issues related to programming language for the platform."

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

"The solution’s integration with other platforms should be improved."

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

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

"It should support more programming languages."

"It's not easy to install."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

More Apache Spark cons

Pricing and Cost Advice

"The license fee for Amazon EC2 is higher than its competitors."

"I use the free tier, although I have paid for some services that are not free. The overall cost of this solution depends on the services you use."

"EC2 pricing is somewhat transparent, in that AWS provides pricing for all instance types. However, the number of pricing options can be confusing."

"The pricing of this solution is variable. There is an open-source variant that is accessible via the public cloud, and then tiers that range in price depending on the level and amount of usage that is required."

"The price of Amazon EC2 could improve. The Google Cloud Platform is more cost-effective."

"Amazon EC2 has a pay-as-you-use cost model."

"The tool's pricing is not cheap."

"It is not an expensive solution."

More Amazon EC2 pricing and cost advice

"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."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"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."

"We are using the free version of the solution."

"It is an open-source platform. We do not pay for its subscription."

"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."

"Spark is an open-source solution, so there are no licensing costs."

"The product is expensive, considering the setup."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

19%

Financial Services Firm

16%

Retailer

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?

The scalability and elasticity are helpful.

See all answers

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

I'm going to mention again that there is quite a bit of complexity within the pricing of EC2 instances. I'm just going to give it six out of ten since there are various standards, upfront and commi...

See all answers

What needs improvement with Amazon EC2?

There is not much to be improved or enhanced. One of the things that need to be looked into is the complex pricing, which is rather intense. Sometimes, clients don't understand the structures. Thes...

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

AWS Fargate vs Amazon EC2

Compared 48% of the time

AWS Batch vs Amazon EC2

Compared 24% of the time

AWS Lambda vs Amazon EC2

Compared 13% of the time

More Amazon EC2 Competitors

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

Amazon Corretto vs Apache Spark

Compared 2% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Amazon EC2

February 2025

Download Amazon EC2 product report

Buyer's Guide

Apache Spark

February 2025

Download Apache Spark product report

Also Known As

Amazon Elastic Compute Cloud, EC2

No data available

Overview

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.

Amazon EC2’s simple web service interface allows you to obtain and configure capacity with minimal friction. It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment. Amazon EC2 reduces the time required to obtain and boot new server instances to minutes, allowing you to quickly scale capacity, both up and down, as your computing requirements change. Amazon EC2 changes the economics of computing by allowing you to pay only for capacity that you actually use. Amazon EC2 provides developers the tools to build failure resilient applications and isolate them from common failure scenarios.

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

Netflix, Expedia, TimeInc., Novaris, airbnb, Lamborghini

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 vs. Apache Spark

January 2025

Free Report: Amazon EC2 vs. Apache Spark

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

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