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 11.2% mindshare in CS, compared to Amazon Web Services (AWS)’s 12.9% 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

3,530 Views
953 Comparison Views

90% willing to recommend

AWS Batch

Read 10 AWS Batch reviews

1,287 Views
1,287 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 January 2026, in the Compute Service category, the mindshare of Apache Spark is 11.2%, down from 11.4% compared to the previous year. The mindshare of AWS Batch is 12.9%, down from 19.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service Market Share Distribution
Product	Market Share (%)
Apache Spark	11.2%
AWS Batch	12.9%
Other	75.9%

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.

Read full review

Quotes from Members

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

Pros

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."

"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

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

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"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 product is useful for analytics."

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

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."

"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."

"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."

"AWS Batch's deployment was easy."

"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 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."

More AWS Batch pros

Cons

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

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

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

"I would like to see integration with data science platforms to optimize the processing capability for these tasks."

"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."

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

"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

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."

"AWS Batch needs to improve its documentation."

"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."

Pricing and Cost Advice

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

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

"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."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"The solution is affordable and there are no additional licensing costs."

"Apache Spark is an open-source tool."

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

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

25%

Computer Software Company

Manufacturing Company

Comms Service Provider

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...

See all answers

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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Product Reports

Buyer's Guide

Apache Spark

January 2026

Download Apache Spark product report

Buyer's Guide

AWS Batch

January 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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