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AWS Batch vs Apache Spark comparison

 

Comparison Buyer's Guide

Executive Summary

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
65
Ranking in other categories
Hadoop (1st), Java Frameworks (2nd)
AWS Batch
Ranking in Compute Service
5th
Average Rating
8.4
Number of Reviews
9
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 Batch is 21.0%, up from 17.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Compute Service
 

Featured Reviews

Ilya Afanasyev - PeerSpot reviewer
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.
Larry Singh - PeerSpot reviewer
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"The most valuable feature of Apache Spark is its flexibility."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The fault tolerant feature is provided."
"I feel the streaming is its best feature."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"We can easily integrate AWS container images into the product."
"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."
"AWS Batch's deployment was easy."
 

Cons

"It should support more programming languages."
"The solution needs to optimize shuffling between workers."
"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."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"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."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The initial setup was not easy."
"When we run a lot of batch jobs, the UI must show the history."
"AWS Batch needs to improve its documentation."
"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."
"The solution should include better and seamless integration with other AWS services, like Amazon S3 data storage and EC2 compute resources."
 

Pricing and Cost Advice

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"They provide an open-source license for the on-premise version."
"The solution is affordable and there are no additional licensing costs."
"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."
"Apache Spark is an expensive solution."
"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."
"We are using the free version of the solution."
"AWS Batch's pricing is good."
"AWS Batch is a cheap solution."
"The pricing is very fair."
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
8%
Comms Service Provider
6%
Financial Services Firm
28%
Computer Software Company
11%
Manufacturing Company
7%
University
6%
 

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.
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...
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...
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...
What do you like most about AWS Batch?
AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling.
What is your experience regarding pricing and costs for AWS Batch?
AWS Batch itself is a service for which I don't usually pay directly. I pay for the compute and memory used underneath, such as AWS EC2 ( /products/amazon-ec2-reviews ), AWS Fargate ( /products/aws...
 

Comparisons

 

Also Known As

No data available
Amazon Batch
 

Overview

 

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
Find out what your peers are saying about AWS Batch vs. Apache Spark and other solutions. Updated: April 2025.
848,716 professionals have used our research since 2012.