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 8.6, while Amazon Web Services (AWS) is ranked #6 with an average rating of 8.3. Apache holds a 10.1% mindshare in CS, compared to Amazon Web Services (AWS)’s 10.7% 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 69 Apache Spark reviews

3,829 Views
1,034 Comparison Views

90% willing to recommend

AWS Batch

Read 10 AWS Batch reviews

1,395 Views
1,395 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: March 2026).

Buyer's Guide

AWS Batch vs. Apache Spark

March 2026

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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 March 2026, in the Compute Service category, the mindshare of Apache Spark is 10.1%, down from 11.3% compared to the previous year. The mindshare of AWS Batch is 10.7%, down from 20.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service Mindshare Distribution
Product	Mindshare (%)
Apache Spark	10.1%
AWS Batch	10.7%
Other	79.2%

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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Kashish Patel

Senior Battery Data Engineer at a agriculture with 51-200 employees

Enables efficient scaling and robust integration despite debugging challenges

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 Fargate option is cost-effective and efficient, removing dependency on EC2 instances. AWS Batch also integrates with the entire AWS ecosystem, including S3, Lambda, and AWS Lambda Step Functions, making it robust. I can use different services with AWS Batch, trigger it through other services, and orchestrate AWS Batch jobs. AWS Batch allows time-extensive workloads to run for days without interruption, unlike AWS Lambda's fifteen-minute hard deadline. It's reliable and cost-effective, and it has been a good solution since 2021.

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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

"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 data processing framework is good."

"Apache Spark can do large volume interactive data analysis."

"The processing time is very much improved over the data warehouse solution that we were using."

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

"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."

"The product's deployment phase is easy."

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

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

"AWS Batch is invaluable for parallelizing processes and samples, which is essential for our large data sets, such as terabytes of genome data."

"AWS Batch's deployment was easy."

"AWS Batch is a cost-effective way to perform batch processing, primarily using spot instances and containers."

"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."

"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."

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

More AWS Batch pros

Cons

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

"At the initial stage, the product provides no container logs to check the activity."

"The solution’s integration with other platforms should be improved."

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

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"We are building our own queries on Spark, and it can be improved in terms of query handling."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

"The solution needs to optimize shuffling between workers."

More Apache Spark cons

"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."

"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."

Pricing and Cost Advice

"Apache Spark is an expensive solution."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"The product is expensive, considering the setup."

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

"Apache Spark is an open-source tool."

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

"Spark is an open-source solution, so there are no 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."

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

23%

Manufacturing Company

Computer Software Company

Comms Service Provider

Financial Services Firm

30%

Manufacturing Company

Computer Software Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	16
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?

I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...

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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...

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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

What needs improvement with AWS Batch?

I haven't identified any significant improvements for AWS Batch. In other AWS services, I've encountered issues with APIs and documentation, but AWS Batch is straightforward and user-friendly. The ...

See all answers

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Compared 19% of the time

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Compared 12% of the time

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Compared 8% of the time

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Product Reports

Buyer's Guide

Apache Spark

March 2026

Download Apache Spark product report

Buyer's Guide

AWS Batch

March 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

March 2026

Free Report: AWS Batch vs. Apache Spark

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