AWS Batch 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 8.7, while Amazon Web Services (AWS) is ranked #6 with an average rating of 8.3. Apache holds a 10.4% mindshare in CS, compared to Amazon Web Services (AWS)’s 11.6% 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 68 Apache Spark reviews

3,663 Views
989 Comparison Views

90% willing to recommend

AWS Batch

Read 10 AWS Batch reviews

1,345 Views
1,345 Comparison Views

100% willing to recommend

Apache Spark

AWS Batch

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Executive SummaryUpdated on May 21, 2025

Apache Spark and AWS Batch are leading solutions in data processing, each catering to distinct needs. Apache Spark stands out due to its high performance in large-scale data processing through in-memory techniques, while AWS Batch benefits from its seamless integration into the AWS ecosystem, offering simple job management and scalability.

Features: Apache Spark implements Spark Streaming for real-time processing, Spark SQL for efficient querying, and MLlib for advanced machine learning capabilities. Spark's in-memory processing ensures quick and scalable analytics, and its compatibility with multiple languages like Python and Scala enhances versatility. AWS Batch is proficient in scheduling and resource provisioning and supports executing parallel jobs in Docker containers. Its seamless integration with AWS services optimizes significant data workload processes.

Room for Improvement: Apache Spark could improve with enhanced memory management, broader machine learning algorithm support, and more stable API documentation, especially for newcomers. Meanwhile, AWS Batch could benefit from improved documentation, error handling, and faster logging methods. It is also suggested to have robust integrations with other AWS services and better user education tools.

Ease of Deployment and Customer Service: Apache Spark offers flexible on-premises and hybrid cloud deployments that may involve setup complexity, often relying on community support due to its open-source nature. AWS Batch shines with simpler deployments through its strong integration with AWS services, benefiting from AWS's comprehensive customer support, despite needing some improvements in documentation and user experience.

Pricing and ROI: Apache Spark, being open-source, poses cost-saving opportunities but may require investment in infrastructure. Enterprises experience improved ROI through operational efficiency despite rising costs with complex cloud setups. AWS Batch is economical, especially with spot instances, though intensive use can drive up costs. It is praised for efficient resource optimization, maintaining strong ROI potential for substantial operations.

To learn more, read our detailed AWS Batch vs. Apache Spark Report (Updated: December 2025).

Buyer's Guide

AWS Batch vs. Apache Spark

December 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

6.9

Number of Reviews

Ranking in other categories

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

AWS Batch

Ranking in Compute Service

6th

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the Compute Service category, the mindshare of Apache Spark is 10.4%, down from 11.4% compared to the previous year. The mindshare of AWS Batch is 11.6%, down from 20.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service Market Share Distribution
Product	Market Share (%)
Apache Spark	10.4%
AWS Batch	11.6%
Other	78.0%

Compute Service

Featured Reviews

Devindra Weerasooriya

Data Architect at Devtech

Provides a consistent framework for building data integration and access solutions with reliable performance

The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

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Ajeet Kumar Singh

Software Engineering Manager – Digital Production Optimization at Yara International ASA

Flexibility in planning and scheduling with containerized workload management has significantly improved computational efficiency

AWS Batch is highly flexible. It allows users to plan, schedule, and compute on containerized workloads. In previous roles, I utilized it for diverse simulations, including on-demand and scheduled computations. It facilitates creating clusters tailored to specific needs, such as memory-centric or CPU-centric workloads, and supports scaling operations massively, like running one hundred thousand Docker containers simultaneously.

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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

"Apache Spark, specifically PySpark and the tools available there, have been quite helpful in my event analysis work."

"The product's deployment phase is easy."

"Apache Spark provides a very high-quality implementation of distributed data processing."

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

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

"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."

"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

More Apache Spark pros

"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 is highly flexible; it allows users to plan, schedule, and compute on containerized workloads, create clusters tailored to specific needs like memory-centric or CPU-centric workloads, and supports scaling operations massively, like running one hundred thousand Docker containers simultaneously."

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

"The main feature I like about AWS Batch is its scalability; whether ten extraction jobs or ten thousand jobs are running, it works seamlessly and scales seamlessly."

"The stability of AWS Batch is impeccable; we have run thousands of jobs without encountering any problems, and AWS Batch consistently performs as expected."

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

"AWS Batch's deployment was easy."

"I appreciate that AWS Batch works with EC2, allowing me to launch jobs and automatically spin up the EC2 instance to run them; when the jobs are completed, the EC2 instance shuts down, making it cost-effective."

More AWS Batch pros

Cons

"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."

"The solution must improve its performance."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

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

"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"Dynamic DataFrame options are not yet available."

"The solution needs to optimize shuffling between workers."

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."

"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."

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

Pricing and Cost Advice

"We are using the free version of the solution."

"The product is expensive, considering the setup."

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

"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."

"Apache Spark is an open-source tool."

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

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

"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."

More Apache Spark pricing and cost advice

"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

25%

Computer Software Company

Manufacturing Company

University

Financial Services Firm

30%

Manufacturing Company

Computer Software Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	15
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	5
Large Enterprise	6

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?

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

See all answers

What needs improvement with Apache Spark?

Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...

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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?

Pricing is good, as AWS Batch allows specifying spot instances, providing cost-effective solutions when launching jobs and spinning up EC2 instances.

See all answers

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Compared 12% of the time

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Compared 9% of the time

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Product Reports

Buyer's Guide

Apache Spark

February 2026

Download Apache Spark product report

Buyer's Guide

AWS Batch

February 2026

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

December 2025

Free Report: AWS Batch vs. Apache Spark

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