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 8.6, while Amazon Web Services (AWS) is ranked #5 with an average rating of 8.4. Apache holds a 11.5% mindshare in CS, compared to Amazon Web Services (AWS)’s 20.1% 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 66 Apache Spark reviews

4,837 Views
1,209 Comparison Views

90% willing to recommend

AWS Batch

Read 10 AWS Batch reviews

1,511 Views
1,511 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: June 2025).

Buyer's Guide

AWS Batch vs. Apache Spark

June 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

7.4

Number of Reviews

Ranking in other categories

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

AWS Batch

Ranking in Compute Service

5th

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Compute Service category, the mindshare of Apache Spark is 11.5%, up from 11.1% compared to the previous year. The mindshare of AWS Batch is 20.1%, up from 15.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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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 fault tolerant feature is provided."

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

"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."

"The solution is very stable."

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

"I found the solution stable. We haven't had any problems with it."

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

More Apache Spark pros

"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."

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

"AWS Batch's deployment was easy."

Cons

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

"The solution needs to optimize shuffling between workers."

"It should support more programming languages."

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

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

"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."

"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."

"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."

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."

"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."

Pricing and Cost Advice

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

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

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

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

"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."

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

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

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

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

12%

Manufacturing Company

Comms Service Provider

Financial Services Firm

29%

Computer Software Company

Manufacturing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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.

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What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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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 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 25% of the time

SAP HANA vs Apache Spark

Compared 10% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

AWS Lambda vs Apache Spark

Compared 6% of the time

More Apache Spark Competitors

AWS Lambda vs AWS Batch

Compared 49% of the time

Amazon EC2 vs AWS Batch

Compared 8% of the time

AWS Fargate vs AWS Batch

Compared 8% of the time

Oracle Compute Cloud Service vs AWS Batch

Compared 7% of the time

Amazon EC2 Auto Scaling vs AWS Batch

Compared 4% of the time

More AWS Batch Competitors

Product Reports

Buyer's Guide

Apache Spark

July 2025

Download Apache Spark product report

Buyer's Guide

AWS Batch

July 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

June 2025

Free Report: AWS Batch vs. Apache Spark

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