AWS Batch vs Apache Spark comparison

Apache and Amazon Web Services (AWS) are both solutions in the Compute Service category. Apache is ranked #3 with an average rating of 8.6, while Amazon Web Services (AWS) is ranked #6 with an average rating of 9.0. Apache holds a 11.3% mindshare in CS, compared to Amazon Web Services (AWS)’s 20.3% 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 65 Apache Spark reviews

1,769 Views
1,353 Comparison Views

90% willing to recommend

AWS Batch

Read 4 AWS Batch reviews

2,942 Views
2,708 Comparison Views

100% willing to recommend

Apache Spark

AWS Batch

Comparison Buyer's Guide

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Executive Summary

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

Buyer's Guide

AWS Batch vs. Apache Spark

January 2025

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Helped 832,138 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

3rd

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

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

AWS Batch

Ranking in Compute Service

6th

Average Rating

9.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2025, in the Compute Service category, the mindshare of Apache Spark is 11.3%, up from 8.5% compared to the previous year. The mindshare of AWS Batch is 20.3%, up from 19.6% 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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Larry Singh

Head of Bioinformatics at Paratus Sciences

User-friendly, good customization and offers exceptional scalability, allowing users to run jobs ranging from 32 cores to over 2,000 cores

The main drawback to using AWS Batch would be the cost. It will be more expensive in some cases than using an HPC. It's more amenable to cases where you have spot requirements. So, for instance, you don't exactly know how much compute resources you'll need and when you'll need them. So it's much better for that flexibility. But if you're going to be running jobs consistently and using the compute cluster consistently for a lot of time, and it's not going to have a lot of downtime, then the HPC system might be a better alternative. So, really, it boils down to cost versus usage trade-offs. It's going to be more expensive for a lot of people. In future releases, I would like to see anything that could help make it easier to set up your initial system. And besides improving the GUI a little bit, the interface to it, making it a little bit more descriptive and having more information at your fingertips, so if you could point to the help of what the different features are, you can get quick access to that. That might help. With most of the AWS services, the difficulty really is getting information and knowledge about the system and seeing examples. So, seeing examples of how it's being used under multiple use cases would be the best way to become familiar with it. And some of that would just come with experience. You have to just use it and play with it. But in terms of the system itself, it's not that difficult to set up or use.

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

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

"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."

"Features include machine learning, real time streaming, and data processing."

"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."

"ETL and streaming capabilities."

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

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

More Apache Spark pros

"We can easily integrate AWS container images into the product."

"AWS Batch's deployment was easy."

"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."

"There is one other feature in confirmation or call confirmation where you can have templates of what you want to do and just modify those to customize it to your needs. And these templates basically make it a lot easier for you to get started."

Cons

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"It should support more programming languages."

"The solution needs to optimize shuffling between workers."

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

"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."

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

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

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

More Apache Spark cons

"The solution should include better and seamless integration with other AWS services, like Amazon S3 data storage and EC2 compute resources."

"When we run a lot of batch jobs, the UI must show the history."

"The main drawback to using AWS Batch would be the cost. It will be more expensive in some cases than using an HPC. It's more amenable to cases where you have spot requirements."

"AWS Batch needs to improve its documentation."

Pricing and Cost Advice

"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 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."

"Apache Spark is an open-source tool."

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

"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."

"The product is expensive, considering the setup."

"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

"The pricing is very fair."

"AWS Batch's pricing is good."

"AWS Batch is a cheap solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Retailer

Financial Services Firm

28%

Computer Software Company

11%

Manufacturing Company

Government

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 main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...

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Which is better, AWS Lambda or Batch?

AWS Lambda is a serverless solution. It doesn’t require any infrastructure, which allows for cost savings. There is no setup process to deal with, as the entire solution is in the cloud. If you use...

See all answers

What do you like most about AWS Batch?

AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling.

See all answers

What is your experience regarding pricing and costs for AWS Batch?

AWS Batch is a cheap solution.

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 9% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

AWS Lambda vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

AWS Lambda vs AWS Batch

Compared 59% of the time

Amazon EC2 vs AWS Batch

Compared 6% of the time

Oracle Compute Cloud Service vs AWS Batch

Compared 5% of the time

AWS Fargate vs AWS Batch

Compared 5% of the time

Amazon EC2 Auto Scaling vs AWS Batch

Compared 3% of the time

More AWS Batch Competitors

Product Reports

Buyer's Guide

Apache Spark

February 2025

Download Apache Spark product report

Buyer's Guide

Compute Service

January 2025

Download AWS Batch product report

Also Known As

No data available

Amazon Batch

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 Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2 and Spot Instances.

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

Hess, Expedia, Kelloggs, Philips, HyperTrack

Buyer's Guide

AWS Batch vs. Apache Spark

January 2025

Free Report: AWS Batch vs. Apache Spark

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

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