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 11.2% mindshare in CS, compared to Amazon Web Services (AWS)’s 14.7% 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

1,526 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: March 2025).

Buyer's Guide

AWS Fargate vs. Apache Spark

March 2025

Download the complete report

Helped 847,862 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 April 2025, in the Compute Service category, the mindshare of Apache Spark is 11.2%, up from 9.7% compared to the previous year. The mindshare of AWS Fargate is 14.7%, down from 17.7% 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

"The product's deployment phase is easy."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"It provides a scalable machine learning library."

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

"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."

"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."

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

"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

More Apache Spark pros

"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."

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

"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."

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

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

"AWS Fargate automatically scales to meet demand, making it ideal for applications with variable workloads."

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

More AWS Fargate pros

Cons

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

"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."

"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 is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."

"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."

"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."

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

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

More Apache Spark cons

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

"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."

"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."

"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."

"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."

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

"We would like to see some improvement in the process documents that are provided with this product, particularly for auto-scaling and other configuration tools that are a bit complicated."

More AWS Fargate cons

Pricing and Cost Advice

"Apache Spark is an open-source tool."

"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 not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

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

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

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

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"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."

More Apache Spark pricing and cost advice

"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."

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

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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...

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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?

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

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Compared 11% of the time

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Compared 7% of the time

Amazon EMR vs Apache Spark

Compared 3% of the time

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Compared 40% of the time

Heroku vs AWS Fargate

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Compared 11% of the time

Oracle Compute Cloud Service vs AWS Fargate

Compared 8% of the time

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Compared 7% of the time

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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

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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