AWS Batch vs Apache Spark comparison

Apache and Amazon Web Services (AWS) are both solutions in the Compute Service category. Apache is ranked #3 with an average rating of 8.8, while Amazon Web Services (AWS) is ranked #6 with an average rating of 8.3. Apache holds a 11.4% mindshare in CS, compared to Amazon Web Services (AWS)’s 14.1% 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

3,558 Views
961 Comparison Views

90% willing to recommend

AWS Batch

Read 10 AWS Batch reviews

1,282 Views
1,282 Comparison Views

100% willing to recommend

Apache Spark

AWS Batch

Comparison Buyer's Guide

Download the report

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

Download the complete report

Helped 879,259 peers since 2012

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

3rd

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 December 2025, in the Compute Service category, the mindshare of Apache Spark is 11.4%, up from 11.1% compared to the previous year. The mindshare of AWS Batch is 14.1%, down from 19.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service Market Share Distribution
Product	Market Share (%)
Apache Spark	11.4%
AWS Batch	14.1%
Other	74.5%

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.

Read full review

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

"Provides a lot of good documentation compared to other solutions."

"The solution has been very stable."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

"The most valuable feature of this solution is its capacity for processing large amounts of data."

More Apache Spark pros

"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."

"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 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."

"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."

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

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

"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

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

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

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

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

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

"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."

"The initial setup was not easy."

"The migration of data between different versions could be improved."

More Apache Spark cons

"AWS Batch needs to improve its documentation."

"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."

Pricing and Cost Advice

"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."

"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."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

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

"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 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."

More Apache Spark pricing and cost advice

"AWS Batch's pricing is good."

"The pricing is very fair."

"AWS Batch is a cheap solution."

See which vendors are best for you

Use our free recommendation engine to learn which Compute Service solutions are best for your needs.

See recommendations

879,259 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

11%

Manufacturing Company

Comms Service Provider

Financial Services Firm

31%

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

Comparisons

Spring Boot vs Apache Spark

Compared 18% of the time

AWS Lambda vs Apache Spark

Compared 8% of the time

SAP HANA vs Apache Spark

Compared 6% of the time

Apache NiFi vs Apache Spark

Compared 6% of the time

Amazon EC2 vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

AWS Lambda vs AWS Batch

Compared 25% of the time

Amazon EC2 vs AWS Batch

Compared 15% of the time

AWS Fargate vs AWS Batch

Compared 11% of the time

Oracle Compute Cloud Service vs AWS Batch

Compared 10% of the time

Amazon EC2 Auto Scaling vs AWS Batch

Compared 6% of the time

More AWS Batch Competitors

Product Reports

Buyer's Guide

Apache Spark

December 2025

Download Apache Spark product report

Buyer's Guide

AWS Batch

December 2025

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

Find out what your peers are saying about AWS Batch vs. Apache Spark and other solutions. Updated: December 2025.

DOWNLOAD NOW

879,259 professionals have used our research since 2012.

See our AWS Batch vs. Apache Spark report.

See our list of best Compute Service vendors.

We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.