AWS Batch vs Apache Spark comparison

Apache and Amazon Web Services (AWS) are both solutions in the Compute Service category. Apache is ranked #4 with an average rating of 8.8, while Amazon Web Services (AWS) is ranked #3 with an average rating of 8.4. Apache holds a 11.6% mindshare in CS, compared to Amazon Web Services (AWS)’s 17.2% 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 67 Apache Spark reviews

3,754 Views
939 Comparison Views

90% willing to recommend

AWS Batch

Read 10 AWS Batch reviews

1,328 Views
1,328 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: September 2025).

Buyer's Guide

AWS Batch vs. Apache Spark

September 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

4th

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

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

AWS Batch

Ranking in Compute Service

3rd

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Compute Service category, the mindshare of Apache Spark is 11.6%, up from 11.5% compared to the previous year. The mindshare of AWS Batch is 17.2%, up from 16.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service Market Share Distribution
Product	Market Share (%)
AWS Batch	17.2%
Apache Spark	11.6%
Other	71.2%

Compute Service

Featured Reviews

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Empowering data consolidation and fast decision-making with efficient big data processing

I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.

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Larry Singh

Head of Bioinformatics at Paratus Sciences

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.

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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

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

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

"The good performance. The nice graphical management console. The long list of ML algorithms."

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"Spark can handle small to huge data and is suitable for any size of company."

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

"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 scalability has been the most valuable aspect of the solution."

More Apache Spark pros

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

"AWS Batch's deployment was easy."

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

"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."

Cons

"There were some problems related to the product's compatibility with a few Python libraries."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"The product could improve the user interface and make it easier for new users."

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

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

"Apache Spark provides very good performance The tuning phase is still tricky."

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

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."

"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."

"AWS Batch needs to improve its documentation."

Pricing and Cost Advice

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

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

"Apache Spark is an expensive solution."

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

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

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"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."

"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

27%

Computer Software Company

11%

Comms Service Provider

Manufacturing Company

Financial Services Firm

29%

Manufacturing Company

Computer Software Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	27
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?

Regarding Apache Spark, I have only used Apache Spark Structured Streaming, not the machine learning components. I am uncertain about specific improvements needed today. However, after five years, ...

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

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Compared 8% of the time

Apache NiFi vs Apache Spark

Compared 6% of the time

Spark SQL vs Apache Spark

Compared 6% of the time

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AWS Lambda vs AWS Batch

Compared 37% of the time

Amazon EC2 vs AWS Batch

Compared 14% of the time

AWS Fargate vs AWS Batch

Compared 10% 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

October 2025

Download Apache Spark product report

Buyer's Guide

AWS Batch

October 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

September 2025

Free Report: AWS Batch vs. Apache Spark

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