data-pipeline-samples
Sample AWS Data Pipeline templates for automating data movement and transformation workflows.
What is data-pipeline-samples?
Data Pipeline Samples is a collection of example templates and configurations for AWS Data Pipeline, a web service that automates data movement and transformation workflows. It provides ready-to-use pipeline definitions that demonstrate how to create data-driven workflows with task dependencies, helping users quickly get started with orchestrating data transformation tasks on AWS infrastructure.
Data engineers and developers who need to automate ETL (Extract, Transform, Load) processes or data movement workflows using AWS services. It's particularly useful for teams adopting AWS Data Pipeline who want practical examples of pipeline configuration and execution.
Developers choose this project because it provides production-tested templates that accelerate AWS Data Pipeline adoption, with parameterized configurations that avoid hardcoding and detailed documentation that explains each component. The samples demonstrate best practices for integrating with AWS services like EC2, S3, and IAM while showing how to manage workflow dependencies.
Overview
This repository hosts sample pipelines
Use Cases
Best For
- Learning AWS Data Pipeline fundamentals through working examples like the Hello World pipeline
- Creating reference templates for executing shell commands on EC2 instances within data workflows
- Setting up parameterized pipeline configurations to avoid hardcoded variables like S3 paths
- Understanding how to implement task dependencies and scheduling in data transformation workflows
- Getting started with AWS service integrations (EC2, S3, IAM) for data pipeline execution
- Developing custom data workflows by modifying and extending pre-built sample templates
Not Ideal For
- Projects requiring real-time data streaming or event-driven processing
- Teams using multi-cloud or non-AWS environments needing cloud-agnostic solutions
- Organizations seeking low-code or GUI-driven workflow tools without JSON configuration
- Small-scale data tasks where EC2 instance overhead is cost-prohibitive
Pros & Cons
Pros
The repository includes ready-to-use JSON definitions for common workflows like the Hello World example, accelerating development by providing reference implementations without starting from scratch.
Samples use pipeline parameters to avoid hardcoding variables, such as S3 log paths, making them adaptable to different environments with minimal code changes.
Templates demonstrate best practices for integrating AWS services like EC2, S3, and IAM, with detailed examples on setting up resources and roles for data pipeline execution.
Each sample comes with clear setup and run instructions, including CLI commands and JSON explanations, which help users understand and execute pipelines effectively.
Cons
Running samples requires setting up a Python virtual environment, installing dependencies like awscli and boto3, and creating IAM roles, which adds overhead for quick experimentation.
The samples are tightly coupled with AWS services, making them unsuitable for projects that need portability across cloud providers or use alternative orchestration tools.
The README explicitly states 'THIS IS A WORK IN PROGRESS,' indicating that samples may be incomplete, lack updates, or have untested edge cases for production use.
Frequently Asked Questions
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