AWS Compute Optimizer vs Apache Spark comparison

Apache and Amazon Web Services (AWS) are both solutions in the Compute Service category. Apache is ranked #5 with an average rating of 8.6, while Amazon Web Services (AWS) is ranked #15. Apache holds a 11.3% mindshare in CS, compared to Amazon Web Services (AWS)’s 0.1% 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

1,415 Views
1,077 Comparison Views

90% willing to recommend

AWS Compute Optimizer

Read 1 AWS Compute Optimizer review

102 Views
14 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, Zadara and others in Compute Service.

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

Buyer's Guide

Compute Service

May 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

5th

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

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

AWS Compute Optimizer

Ranking in Compute Service

15th

Average Rating

8.0

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 Compute Optimizer is 0.1%, down from 0.2% 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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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

"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."

"The solution has been very stable."

"The solution is scalable."

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

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

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

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

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

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

"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."

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

"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."

"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."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"There were some problems related to the product's compatibility with a few Python libraries."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

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

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

"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 solution, it is free of charge."

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

"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."

"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 open-source. You have to pay only when you use any bundled product, such as Cloudera."

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

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

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.

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

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Comparisons

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AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 11% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

No data available

Product Reports

Buyer's Guide

Apache Spark

May 2025

Download Apache Spark product report

Buyer's Guide

Compute Service

May 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, Zadara and others in Compute Service. Updated: May 2025.

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