1. AWS Data Pipeline
- Purpose: Orchestrates data movement and batch ETL workflows between AWS services or on-premises.
- Focus: Scheduling and automating data flows, not performing transformations itself (though it can trigger EMR jobs, SQL scripts, or Lambda).
- Best for: Multi-step data pipelines across services on a schedule.
Example: Move data from on-prem Oracle → S3 → Redshift every night.
2. AWS Glue
- Purpose: Fully-managed ETL service for data cataloging, transformation, and loading.
- Focus: Data processing and transformation. It can also orchestrate ETL jobs, but it’s more about preparing data for analytics than just moving it.
- Best for: Automated, serverless ETL, especially when using Spark to process large datasets.
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Components:
- Glue Data Catalog – keeps metadata of datasets.
- Glue ETL Jobs – process data using Spark/PySpark.
- Glue Crawlers – automatically detect schema and update the catalog.
Example: Transform raw S3 logs → normalize fields → write to Redshift for analytics.
3. AWS Step Functions
- Purpose: Orchestrates application workflows and serverless services.
- Focus: Task coordination, conditional logic, branching, retries, parallel execution, human approval.
- Best for: Business/application workflows, not specifically data ETL (though it can orchestrate ETL jobs).
Example: Order workflow: receive order → charge payment → update inventory → notify user.
Comparison Table
| Feature | AWS Data Pipeline | AWS Glue | AWS Step Functions |
|---|---|---|---|
| Primary Use | Orchestrate batch data movement | ETL: catalog, transform, load | Orchestrate application workflows |
| Trigger | Scheduled or on-demand | Scheduled, on-demand, or event-based | Event-driven, API, or scheduled |
| Logic | Sequential, basic retry | ETL transformations, partitioning | Sequential, parallel, branching, error handling |
| Services | S3, EMR, RDS, DynamoDB | S3, Redshift, RDS, JDBC sources | Lambda, ECS, Batch, Glue, DynamoDB, SNS, SQS |
| Transformations | Limited (via jobs) | Rich transformations with Spark | Optional, via Lambda or services |
| Metadata | No | Glue Data Catalog | No |
| Best For | Moving/transforming data pipelines | Preparing analytics-ready data | Coordinating services / microservices |
Analogy
- Data Pipeline → Conveyor belt moving data between factories.
- Glue → The machine on the belt that cleans, transforms, and prepares the data.
- Step Functions → Project manager coordinating tasks, teams, and decisions across the company.
✅ Exam Tip:
- If the question is about orchestrating ETL for analytics, Glue is usually the answer.
- If it’s about scheduled data movement (no heavy transformations), think Data Pipeline.
- If it’s about application workflow orchestration, branching, retries, human approvals → Step Functions.
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