Deploying container-based ETL jobs using AWS Batch

As data pipelines grow in complexity and scale, executing ETL (Extract, Transform, Load) jobs efficiently becomes essential. Traditional ETL workflows often face challenges around scalability, scheduling, cost optimization, and infrastructure management. That’s where container-based ETL jobs with AWS Batch come into play—offering a powerful solution for running jobs in a fully managed, elastic compute environment.

In this blog, we’ll explore how to deploy containerized ETL workloads using AWS Batch, and how it helps modernize your data engineering processes with reliability, scalability, and minimal operational overhead.

Why Use AWS Batch for ETL?

AWS Batch is a fully managed service that enables you to run batch computing workloads of any scale in the cloud. It dynamically provisions the right quantity and type of compute resources (e.g., EC2 instances or Fargate) based on the job requirements and job queues

Benefits of AWS Batch for ETL: 

1. No server management: Fully managed job scheduling and compute provisioning.
2. Cost-effective: Automatically uses Spot Instances when available.
3. Scalability: Run thousands of parallel jobs efficiently.
4. Container-native: Easily run Docker-based ETL workloads.
5. Secure: Integrates with IAM roles and VPC for secure job execution.

Step 1: Containerize Your ETL Code

First, package your ETL code (Python, Spark, SQL scripts, etc.) into a Docker container. This ensures consistency across development, testing, and production.

Example Dockerfile for a simple Python ETL:

Dockerfile

FROM python:3.10-slim

WORKDIR /app

COPY requirements.txt .

RUN pip install -r requirements.txt

COPY . .

ENTRYPOINT ["python", "etl_script.py"]

Build and push your image to Amazon Elastic Container Registry (ECR):

bash

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docker build -t my-etl-job .

aws ecr create-repository --repository-name etl-job

docker tag my-etl-job:latest <account-id>.dkr.ecr.<region>.amazonaws.com/etl-job:latest

docker push <account-id>.dkr.ecr.<region>.amazonaws.com/etl-job:latest

Step 2: Create a Job Definition in AWS Batch

A job definition tells AWS Batch how to run your job, including:

Docker image URI

vCPU and memory requirements

IAM role

Environment variables

Example:

json

{

  "jobDefinitionName": "etl-job-def",

  "type": "container",

  "containerProperties": {

    "image": "<ECR-IMAGE-URI>",

    "vcpus": 2,

    "memory": 4096,

    "command": ["python", "etl_script.py"],

    "jobRoleArn": "arn:aws:iam::<account-id>:role/AWSBatchJobRole"

  }

}

Create it using AWS CLI:

bash

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aws batch register-job-definition --cli-input-json file://job-def.json

Step 3: Configure Compute Environment and Job Queue

Set up a Compute Environment that defines the EC2 instance types or Fargate resources AWS Batch can use. Link it to a Job Queue that prioritizes and manages job scheduling.

bash

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aws batch create-compute-environment ...

aws batch create-job-queue ...

You can choose between:

Managed EC2: More control over instance types.

Fargate: Fully serverless, ideal for smaller ETL jobs.

Step 4: Submit and Monitor Jobs

Now you’re ready to run your ETL job:

bash

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aws batch submit-job \

  --job-name my-etl-run \

  --job-queue my-job-queue \

  --job-definition etl-job-def

You can monitor job status and logs in the AWS Console under AWS Batch and CloudWatch Logs.

Step 5: Automate with EventBridge or Step Functions

For production-ready workflows, integrate job submissions with:

Amazon EventBridge (for time-based or event-based scheduling)

AWS Step Functions (for chaining multiple ETL steps)

This allows you to build reliable, event-driven data pipelines without managing cron jobs or custom schedulers.

Conclusion

Using AWS Batch to deploy container-based ETL jobs streamlines the entire data processing lifecycle. It allows teams to scale effortlessly, cut costs using Spot Instances, and focus on writing ETL logic rather than managing infrastructure.

By combining Docker, ECR, and AWS Batch, you can build robust, portable, and production-grade ETL workflows that are ready to handle modern data demands.

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