AWS Compute Optimizer 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 #15. Apache holds a 12.0% mindshare in CS, compared to Amazon Web Services (AWS)’s 0.3% 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

4,468 Views
1,117 Comparison Views

90% willing to recommend

AWS Compute Optimizer

Read 1 AWS Compute Optimizer review

114 Views
106 Comparison Views

100% willing to recommend

Apache Spark

AWS Compute Optimizer

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Executive Summary

We performed a comparison between Apache Spark and AWS Compute Optimizer based on real PeerSpot user reviews.

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Oracle and others in Compute Service.

To learn more, read our detailed Compute Service Report (Updated: July 2025).

Buyer's Guide

Compute Service

July 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

Apache Spark

Ranking in Compute Service

4th

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

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

AWS Compute Optimizer

Ranking in Compute Service

15th

Average Rating

8.0

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the Compute Service category, the mindshare of Apache Spark is 12.0%, up from 11.4% compared to the previous year. The mindshare of AWS Compute Optimizer is 0.3%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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Shady Mogawer

IT Manager at Arabian Cement Company

Easy to manage, flexible, and has good scaling options

A partner company called Cloud40 helped us with the deployment. It took a couple of weeks for the deployment to happen. The steps we took for the deployment process included research and information planning about what we wanted to do while moving from physical to the cloud. We have used all the hardware and other resources available to make sure of the connection.

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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

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

"Provides a lot of good documentation compared to other solutions."

"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."

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

"Spark can handle small to huge data and is suitable for any size of company."

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

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."

"The product is useful for analytics."

More Apache Spark pros

"I find the solution's scaling capability to be an important benefit. You can scale it vertically or horizontally, i.e., you can upgrade the hardware or clone the machine. The solution is also easy to manage and flexible. Additionally, you get some layers of security without paying for it."

Cons

"It should support more programming languages."

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

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

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

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

"The logging for the observability platform could be better."

"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."

"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."

More Apache Spark cons

"I have two areas of improvement to comment on. Most of the product names in AWS are not indicative of what they are doing. Moreover, AWS is not organized and you do not have the full platform with you. It is hard to know some AWS services."

Pricing and Cost Advice

"Apache Spark is an expensive solution."

"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."

"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."

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

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

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

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

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

More Apache Spark pricing and cost advice

"I find the solution's pricing reasonable. You need to pay extra for IP and other miscellaneous costs."

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

10%

Comms Service Provider

Manufacturing Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

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What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

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What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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More Apache Spark Competitors

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Product Reports

Buyer's Guide

Apache Spark

August 2025

Download Apache Spark product report

Buyer's Guide

Compute Service

July 2025

Download AWS Compute Optimizer 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

AWS Compute Optimizer recommends optimal AWS Compute resources for your workloads to reduce costs and improve performance by using machine learning to analyze historical utilization metrics. Over-provisioning compute can lead to unnecessary infrastructure cost and under-provisioning compute can lead to poor application performance. Compute Optimizer helps you choose the optimal Amazon EC2 instance types, including those that are part of an Amazon EC2 Auto Scaling group, based on your utilization data.

By applying the knowledge drawn from Amazon’s own experience running diverse workloads in the cloud, Compute Optimizer identifies workload patterns and recommends optimal compute resources. Compute Optimizer analyzes the configuration and resource utilization of your workload to identify dozens of defining characteristics, for example, if a workload is CPU-intensive, or if it exhibits a daily pattern or if a workload accesses local storage frequently. The service processes these characteristics and identifies the hardware resource headroom required by the workload. Compute Optimizer infers how the workload would have performed on various hardware platforms (e.g. Amazon EC2 instances types) and offers recommendations.

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

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Oracle and others in Compute Service. Updated: July 2025.

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