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 14.4% 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 66 Apache Spark reviews

1,526 Views
1,149 Comparison Views

90% willing to recommend

AWS Fargate

Read 17 AWS Fargate reviews

3,381 Views
1,929 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: April 2025).

Buyer's Guide

AWS Fargate vs. Apache Spark

April 2025

Download the complete report

Helped 849,686 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 May 2025, in the Compute Service category, the mindshare of Apache Spark is 11.3%, up from 10.2% compared to the previous year. The mindshare of AWS Fargate is 14.4%, down from 18.5% 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

"We use Spark to process data from different data sources."

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

"The product is useful for analytics."

"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."

"The product's deployment phase is easy."

"The most valuable feature of Apache Spark is its ease of use."

"There's a lot of functionality."

"I found the solution stable. We haven't had any problems with it."

More Apache Spark pros

"All AWS services allow me to manage a wide range of applications and services."

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

"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 process."

"If you create your deployment with a good set of rules for how to scale in, you can just set it and forget it."

"I like their containerization service. You can use Docker or something similar and deploy quickly without the know-how related to, for example, Kubernetes. If you use AKS or Kubernetes, then you have to have the know-how. But for Fargate, you don't need to have the know-how there. You just deploy the container or the image, and then you have the container, and you can use it as AWS takes care of the rest. This makes it easier for those getting started or if you don't have a strong DevOps team inside your organization."

"It allows for focusing on applications instead of managing infrastructure."

"AWS Fargate is an easy-to-use tool to simplify setup. You only pay for the resources you use. If you need to quickly create, delete, or scale applications without managing resources like EC2 instances, Fargate is the best service to use."

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

More AWS Fargate pros

Cons

"Dynamic DataFrame options are not yet available."

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

"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."

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

"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

More Apache Spark cons

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

"Service computing is not as straightforward compared to other computing services. It requires more effort to use effectively."

"We faced challenges in vertically scaling our workload."

"I would like to see the older dashboard instead of the newer version. I don't like the new dashboard."

"AWS Fargate needs improvement in terms of setup complexity."

"If there are any options to manage containers, that would be good. That relates more to the cost point. For example, over the next three months, I'll be making a comparison between solutions like CAST AI and other software-as-a-service platforms that offer Kubernetes management with an emphasis on cost reduction."

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

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

More AWS Fargate cons

Pricing and Cost Advice

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

"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."

"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."

"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."

"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."

"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."

"Apache Spark is an expensive solution."

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

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

25%

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...

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

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?

The monitoring capabilities of AWS Fargate could be improved and made more robust. The error handling aspect sometimes causes issues and can get stuck during deployment, making the process not very...

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What advice do you have for others considering AWS Fargate?

I would recommend AWS Fargate as an alternative to AWS Lambda for running loads or hosting a service. It is a good service for keeping instances running, which minimizes initial latency. Overall, I...

See all answers

Comparisons

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Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Amazon Corretto vs Apache Spark

Compared 2% of the time

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Compared 41% of the time

Heroku vs AWS Fargate

Compared 16% of the time

Oracle Compute Cloud Service vs AWS Fargate

Compared 9% of the time

Amazon EC2 vs AWS Fargate

Compared 9% of the time

AWS Lambda vs AWS Fargate

Compared 3% of the time

More AWS Fargate Competitors

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

AWS Fargate

April 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

April 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: April 2025.

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