AWS Fargate vs Apache Spark comparison

Apache and Amazon Web Services (AWS) are both solutions in the Compute Service category. Apache is ranked #4 with an average rating of 8.6, while Amazon Web Services (AWS) is ranked #2 with an average rating of 8.5. Apache holds a 11.3% mindshare in CS, compared to Amazon Web Services (AWS)’s 15.0% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 100% of Amazon Web Services (AWS) users who would recommend it.

Apache Spark

Read 65 Apache Spark reviews

1,646 Views
1,251 Comparison Views

90% willing to recommend

AWS Fargate

Read 17 AWS Fargate reviews

3,700 Views
2,137 Comparison Views

100% willing to recommend

Apache Spark

AWS Fargate

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache Spark and AWS Fargate 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 AWS Fargate vs. Apache Spark Report (Updated: March 2025).

Buyer's Guide

AWS Fargate vs. Apache Spark

March 2025

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Helped 842,296 peers since 2012

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

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)

AWS Fargate

Ranking in Compute Service

2nd

Average Rating

8.6

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2025, in the Compute Service category, the mindshare of Apache Spark is 11.3%, up from 9.2% compared to the previous year. The mindshare of AWS Fargate is 15.0%, down from 16.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

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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Subrata Mukherjee

Senior Technical Architect; Head of Platform at Blenheim Chalcot IT Services India

Boost demand response with cost-efficient serverless architecture

We are a venture builder company, and if we select AWS for our product. Our design is based on a serverless architecture model. ECS Fargate is the most convenient way in terms of scalability, integration, and cost control Thanks to the serverless model and easy integration features, a few…

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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

"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."

"The solution has been very stable."

"The solution is scalable."

"The fault tolerant feature is provided."

"Apache Spark can do large volume interactive data analysis."

"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

More Apache Spark pros

"AWS Fargate is a fully managed service, and it's very easy to use compared to other services like Elastic Beanstalk Service (EBS)."

"By using a server's compute resources, one can observe the resource metrics. With AWS, one can determine when servers will be used based on CloudWatch results. For example, CloudWatch informs the application and service platform when the hit ratio has reached the threshold value."

"We appreciate the simple use of containers within this solution, it makes managing the containers quick and easy."

"The most valuable feature of AWS Fargate is its ability to run on demand without the constant run time of basic resources."

"Fargate itself is a stable product. We are quite satisfied with its performance."

"The most valuable feature of AWS Fargate is its ease of use."

"Fargate is beneficial for hosting our solutions and providing workflow management, creating legal documents, and delivering business intelligence."

"AWS Fargate has many valuable services. It does the job with minimal trouble. It's very observable. You can see what's going on and you have logs. You have everything. You can troubleshoot it. It's affordable and it's flexible."

More AWS Fargate pros

Cons

"The migration of data between different versions could be improved."

"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."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"One limitation is that not all machine learning libraries and models support it."

"The solution must improve its performance."

"The main concern is the overhead of Java when distributed processing is not necessary."

"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."

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

More Apache Spark cons

"Challenges include higher costs for smaller clients, limited control over underlying infrastructure customization, and potential latencies during task startup."

"There are features that could be added or enhanced."

"As I have not worked with Fargate extensively, I do not have any specific improvements."

"Sometimes, Fargate can be really hard to configure."

"The main area for improvement is the cost, which could be lowered to be more competitive with other major cloud providers."

"I would like to see enhanced faster application scaling and better integration with the elastic file system to unify storage volumes and improve the launch time of instances."

"AWS Fargate could improve the privileged mode containers. We had some problems and they were not able to run."

"There is a tool named Compose that helps in converting the Docker Compose file into a Kubernetes file. For ECS and Fargate, AWS reads a Dockerfile and helps with conversion. However, I am uncertain if it's ready for deployment after conversion."

More AWS Fargate cons

Pricing and Cost Advice

"It is an open-source solution, it is free of charge."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

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

"The solution is affordable and there are no additional licensing costs."

"The product is expensive, considering the setup."

"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."

"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 an open-source tool."

More Apache Spark pricing and cost advice

"I rate the price of AWS Fargate a four out of five."

"We would advise that this solution has a slightly-higher price point than others on the market. There is a free plan available for start-ups, but the free and lower range licensing models do not provide the full functionality."

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Top Industries

By visitors reading reviews

Financial Services Firm

28%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

24%

Computer Software Company

13%

Government

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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...

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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...

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What do you like most about AWS Fargate?

The most valuable feature of Fargate is that it's self-managed. You don't have to configure your own clusters or deploy any Kubernetes clusters. This simplifies the initial deployment and scaling p...

See all answers

What needs improvement with AWS Fargate?

As I have not worked with Fargate extensively, I do not have any specific improvements.

See all answers

What advice do you have for others considering AWS Fargate?

AWS Fargate is easily manageable. We just need to create a cluster and focus on our infrastructure. Customers do not need to take downtime since everything is managed by AWS with no downtime. Overa...

See all answers

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Compared 8% of the time

Jakarta EE vs Apache Spark

Compared 3% of the time

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Compared 41% of the time

Heroku vs AWS Fargate

Compared 15% of the time

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Compared 13% of the time

Oracle Compute Cloud Service vs AWS Fargate

Compared 8% of the time

Apache NiFi vs AWS Fargate

Compared 7% of the time

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Product Reports

Buyer's Guide

Apache Spark

March 2025

Download Apache Spark product report

Buyer's Guide

AWS Fargate

March 2025

Download AWS Fargate product report

Overview

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

A new compute engine that enables you to use containers as a fundamental compute primitive without having to manage the underlying instances. With Fargate, you don’t need to provision, configure, or scale virtual machines in your clusters to run containers. Fargate can be used with Amazon ECS today, with plans to support Amazon Elastic Container Service for Kubernetes (Amazon EKS) in the future.

Fargate has flexible configuration options so you can closely match your application needs and granular, per-second billing.

Amazon Web Services (AWS)

Sample Customers

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers

Buyer's Guide

AWS Fargate vs. Apache Spark

March 2025

Free Report: AWS Fargate vs. Apache Spark

Find out what your peers are saying about AWS Fargate vs. Apache Spark and other solutions. Updated: March 2025.

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