Is AWS Batch right for data professionals?#

2025-08-19

8 min read

AWS Batch is a powerful service for batch computing workloads in the cloud. It offers sophisticated job scheduling, automatic resource provisioning, and seamless scaling capabilities that make it an excellent choice for organizations with dedicated cloud infrastructure teams.

However, if you’re a data scientist, quantitative analyst, or research scientist who wants to focus on analysis rather than infrastructure management, you might find AWS Batch requires more cloud operations expertise than you’d prefer to develop and maintain.

AWS Batch excels when you have experienced DevOps professionals managing your cloud infrastructure. But for data professionals who want to spend their time on models, analysis, and insights rather than VPC configurations and IAM policies, the operational overhead can be substantial. Let’s explore what this means in practice.

Navigating infrastructure setup and configuration#

AWS Batch provides comprehensive control over your compute infrastructure, which is excellent for organizations with dedicated cloud teams. However, even with Fargate’s managed infrastructure, setting up your first job involves several AWS services that may be unfamiliar to data professionals:

Creating and configuring VPCs and subnets
Setting up security groups and network access controls
Defining IAM roles and policies
Configuring compute environments, job queues, and job definitions
Specifying detailed resource requirements and networking parameters

For data scientists accustomed to running analyses locally, this infrastructure-first approach represents a significant shift in workflow. While this level of control enables powerful use cases, it can feel like a steep learning curve when you just want to parallelize a data processing task.

Understanding networking requirements#

AWS Batch’s networking model is designed for strict security requirements, but this can create unexpected challenges for data professionals. A common example: jobs may fail with timeout errors like failed to resolve ref docker.io/library/amazonlinux:latest: dial tcp 44.208.254.194:443: i/o timeout.

The root cause is often a networking configuration detail: Fargate tasks require "assignPublicIp":"ENABLED" to access the internet for pulling Docker images. While this security-first approach makes sense in enterprise environments, it can be surprising for data scientists who expect their containers to “just work” like they do in local Docker environments. Additionally, fixing such configuration issues often requires recreating multiple AWS resources, making iteration slower than many data workflows expect.

Managing complex IAM permissions#

AWS Batch implements security through detailed IAM permissions, which provides excellent control for organizations with security teams. However, this creates a complex permission model where different components (compute environments, job definitions, and jobs) each require specific IAM configurations. For example:

Reading data from S3 requires configuring task roles, trust policies, and specific permissions like AmazonS3ReadOnlyAccess
Using GPU instances requires switching from Fargate to EC2, which introduces additional IAM requirements like instance profiles and ECS instance roles

For data professionals accustomed to file-based permissions or simpler cloud services, like AWS Lambda, this granular security model can feel like learning a specialized domain that’s separate from their core data expertise.

Debugging across multiple AWS services#

AWS Batch’s distributed architecture means debugging information is spread across multiple AWS services, each with their own logging interfaces:

CloudWatch Logs for application output
EC2 system logs for infrastructure issues
Container logs for runtime problems
AWS Batch service logs for job orchestration

There’s no centralized, easy-access log aggregation. This distributed logging model aligns with AWS’s microservices architecture and provides comprehensive observability for DevOps teams familiar with AWS tooling. However, for data professionals accustomed to seeing all their program output in one terminal or notebook, this can feel fragmented. Job failures at different stages (provisioning, container startup, or execution) require checking different log sources, which can slow down the iterative debugging process that’s common in data work.

Scaling and GPU considerations#

AWS Batch’s scaling behavior and GPU support are optimized for large-scale, production batch workloads, but may not align with typical data science workflows:

Cold-start provisioning for EC2-based environments can take 5-10 minutes or more, which is might be reasonable for long-running batch jobs but can feel slow during iterative development
GPU workloads require EC2 compute environments rather than Fargate, necessitating additional infrastructure configuration
AWS service quotas (like GPU instance limits) can cause jobs to remain in RUNNABLE status without clear error messages, requiring familiarity with AWS quota management

These characteristics work well for planned, production batch workloads but can create friction during the experimental, iterative phases common in data science projects.

Container-first approach vs. script-based workflows#

AWS Batch follows a container-first approach. Rather than directly submitting scripts, you embed commands in job definitions or package them into Docker images. For more complex workflows, the typical pattern involves uploading scripts to S3 and configuring job definitions to retrieve and execute them.

This containerized approach provides excellent reproducibility and environment isolation, which is valuable for production workloads. However, data professionals often prefer the immediacy of script-based workflows and being able to quickly modify a Python file and re-run it. The container-first model introduces additional steps (building images or managing S3 uploads) that can slow down the rapid iteration cycles common in exploratory data analysis.

For data professionals operating without a dedicated cloud infrastructure or devops team, this can be a prohibitive bottleneck.

DevOps integration requirements#

AWS Batch integrates well with AWS-native tools like CloudFormation and AWS CLI, making it a natural fit for teams already invested in the AWS ecosystem. However, integration with popular third-party tools can require additional effort:

Terraform support exists but may require custom scripting for complex configurations
Automated deployment of job definitions works best with AWS-native CI/CD tools, though integration with other systems is possible with additional setup

For organizations with established AWS DevOps teams, these integrations are manageable. However, for data teams using tools like Dagster, Prefect, GitHub Actions, or other non-AWS tooling may find the integration requires more DevOps expertise than they are able to maintain.

When AWS Batch works well#

AWS Batch excels in the right organizational context. Teams find success when they have:

Dedicated DevOps or cloud infrastructure teams to handle setup and ongoing maintenance
Established AWS expertise and infrastructure-as-code practices
Already fully embedded in the AWS ecosystem
Long-running, production batch workloads that benefit from AWS’s configurability
Complex security and compliance requirements that benefit from fine-grained IAM controls

Organizations with these characteristics often appreciate AWS Batch’s comprehensive feature set and deep AWS integration.

Alternatives for data-focused teams#

For data professionals who want to focus on analysis rather than infrastructure, several alternatives offer simpler approaches:

Kubernetes jobs provide more transparent control and ecosystem integration, though they still require container expertise
AWS Lambda works well for lightweight, short-duration batch tasks, though with execution time and memory limitations
Data-focused platforms like Coiled are designed specifically for data science workflows, offering the scale of cloud computing with the simplicity of local development

These alternatives prioritize different trade-offs. While AWS Batch optimizes for enterprise control and AWS integration, data-focused platforms like Coiled optimize for researcher productivity and rapid iteration. See our blog post on Choosing an AWS Batch alternative for a detailed comparison.

Batch jobs with Coiled#

If you’re a data professional looking for cloud-scale computing without infrastructure complexity, Coiled offers a different approach. Designed specifically for data science workflows, Coiled automates the provisioning of EC2 instances in your AWS account, synchronizes your Python packages, and handles cleanup when you’re done. It bridges the gap between local development and cloud scale, letting you go from interactive notebook testing to distributed computing without building Docker containers or managing Kubernetes.

Importantly, you don’t have to sacrifice security or production-ready scale with this approach. Coiled works equally well for iterative development and large-scale production jobs, meaning your entire team can use one tool throughout the development lifecycle. While researchers can “package sync away” during exploration and prototyping, the same platform seamlessly handles production workloads with Docker containers and enterprise security controls. This unified approach eliminates the friction of switching between development and production tools, allowing teams to move much faster from experimentation to deployment.

With Coiled, you can:

Launch quickly: Spin up VMs in minutes without configuring VPCs or IAM policies
Scale transparently: Adjust resources automatically with clear feedback on provisioning status
Save with Spot instances: Use cost-effective Spot instances with automatic replacement handling
Access GPUs simply: Enable GPU acceleration without switching compute environments or managing quotas
Debug intuitively: View centralized logs and performance metrics in familiar interfaces

To run a batch job, add #COILED comments to your script to specify the cloud resources you want:

Bash Script

Spin up ten cloud VMs with 32 GB of memory to run their own echo command.

my_script.sh#

#!/bin/bash

#COILED ntasks 10
#COILED memory 32GB
#COILED container ubuntu:latest

echo Hello from $COILED_BATCH_TASK_ID

Then launch your script with coiled batch run:

$ coiled batch run my_script.sh

COILED_BATCH_TASK_ID is an identifier unique to each task which runs from “0”, “1”, “2”, …, “9” in this case.

Python Script

Use # COILED comments directly in Python scripts. Drop the container directive to use package sync to automatically replicate your Python environment on remote VMs.

my_script.py#

# COILED n-tasks     10
# COILED memory      8 GiB
# COILED region      us-east-2

import os

print(f"Hello from {os.environ['COILED_BATCH_TASK_ID']}")

and then launch your script with coiled batch run:

$ coiled batch run my_script.py

It’s easy to get started with Coiled:

$ pip install coiled
$ coiled quickstart

FAQs#

What is AWS Batch used for?#

AWS Batch is a managed service for running batch computing workloads in the cloud. It automatically provisions compute resources, queues jobs, and scales up or down based on demand. It’s often used for data processing, scientific computing, ML training, and other large-scale, parallelizable workloads.

Why might AWS Batch feel complex for data professionals?#

AWS Batch is designed as an enterprise-grade service that requires configuring multiple AWS components (VPCs, subnets, security groups, IAM roles, and job definitions) before running jobs. This comprehensive approach provides control and security, but can feel like a significant learning investment for data professionals who want to focus on their analysis rather than infrastructure management.

Does AWS Batch support GPUs?#

Yes, AWS Batch supports GPUs through EC2 compute environments. GPU support requires using EC2 rather than Fargate, which involves additional configuration of IAM roles, instance profiles, and ensuring adequate GPU service quotas are in place.

How do I submit scripts to AWS Batch?#

AWS Batch does not natively accept standalone scripts. Commands must be embedded in job definitions as JSON or included in container images. For multi-line scripts, the typical workflow is to upload the script to S3, create a job role with S3 read access, and configure your job to pull and run the script.

What are common AWS Batch considerations for data teams?#

Data professionals often encounter these learning areas when adopting AWS Batch:

Networking configurations that affect container internet access
IAM permission models that differ from file-based systems
AWS service quotas that can affect job scheduling
Cold-start provisioning times that may impact iterative workflows
Distributed logging across multiple AWS services

What are alternatives to AWS Batch?#

Different alternatives optimize for different use cases:

Kubernetes jobs offer more transparent control and ecosystem integration
AWS Lambda or ECS provide simpler container-based execution models
Data-focused platforms like Coiled specialize in Python/data science workflows, emphasizing ease of use and rapid iteration over enterprise infrastructure controls