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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
8.8
Number of Reviews
5
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

"The product's initial setup phase was easy."
"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."
"The solution is scalable."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"The product is useful for analytics."
"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."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"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 manages the execution of computing workload, including job scheduling, provisioning, and scaling."
"We can easily integrate AWS container images into the product."
"AWS Batch's deployment was easy."
 

Cons

"The Spark solution could improve in scheduling tasks and managing dependencies."
"They could improve the issues related to programming language for the platform."
"Apache Spark lacks geospatial data."
"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."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"When we run a lot of batch jobs, the UI must show the history."
"The solution should include better and seamless integration with other AWS services, like Amazon S3 data storage and EC2 compute resources."
"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

"It is an open-source platform. We do not pay for its subscription."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"We are using the free version of the solution."
"The product is expensive, considering the setup."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"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."
"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."
"It is an open-source solution, it is free of charge."
"AWS Batch's pricing is good."
"The pricing is very fair."
"AWS Batch is a cheap solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
28%
Computer Software Company
13%
Manufacturing Company
8%
Comms Service Provider
5%
Financial Services Firm
28%
Computer Software Company
10%
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: March 2025.
845,040 professionals have used our research since 2012.