Hello! Welcome back to your preparation for the AWS Certified Data Engineer - Associate exam.
In our last lesson, we focused on how to trigger our data pipelines, comparing direct S3 event notifications with the more powerful and flexible Amazon EventBridge. We concluded that for most modern data architectures, EventBridge is the superior choice for routing events.
Now that we know how to start a workflow, the next logical question is: what AWS service should we use to orchestrate the sequence of tasks within that workflow? In this lesson, we will address the learning outcome: Compare AWS Step Functions with the legacy AWS Data Pipeline service for workflow orchestration.
While AWS Data Pipeline is a legacy service, understanding its purpose and limitations is important for the exam. You may encounter questions that test your knowledge of migration scenarios or require you to differentiate it from modern services like AWS Step Functions, which you are already familiar with.
1. The Legacy Orchestrator: AWS Data Pipeline
Let's start by understanding what AWS Data Pipeline was designed for. It was one of AWS's early services for automating data workflows.
Compare Glue, Data Pipeline & Step functions
The article 'Compare Glue, Data Pipeline & Step functions' from Dev.to provides a concise summary of AWS Data Pipeline's original purpose.
Read the first section, '1. AWS Data Pipeline'. Focus on its stated purpose, focus, and the example use case.
As the article notes, Data Pipeline is an orchestration service focused on scheduled data movement and batch ETL workflows. Its primary job was to reliably move data between different locations (e.g., from an on-premises database to S3) and trigger processing jobs on a recurring schedule.
However, the most crucial thing to know about AWS Data Pipeline today is its status as a legacy service.
AWS Data Pipeline vs AWS Glue: Which Tool is Right for ...
The article from CBT Nuggets, 'AWS Data Pipeline vs AWS Glue', makes the legacy status of Data Pipeline very clear. This is essential context for the exam.
Read the section 'The Legacy of AWS Data Pipeline'. Note the date it was closed to new customers and AWS's reasoning for this decision.
To summarize, AWS Data Pipeline is:
- Legacy: As of July 2024, it is closed to new customers. AWS is no longer adding new features.
- Schedule-focused: Its core strength is running jobs on a predefined schedule (e.g., every night at 2 AM).
- Template-based: You define a pipeline using a JSON template that specifies data sources, activities (the work to be done), and schedules.
- Manages Compute: It doesn't perform transformations itself. Instead, it provisions and manages other resources, like an EMR cluster or an EC2 instance, to run a script or job. This can lead to slower start-up times.
For the exam, think of Data Pipeline as the old-school way to handle recurring "copy-and-process" jobs.
2. The Modern Orchestrator: AWS Step Functions
In contrast to Data Pipeline, AWS Step Functions is the modern, flexible, and serverless solution for workflow orchestration. You've already worked with Step Functions in this course, so this will be a good reinforcement of its capabilities.
Step Functions allows you to coordinate multiple AWS services into a serverless workflow. Its visual nature makes it easy to see the logic, and its architecture provides capabilities far beyond what Data Pipeline could offer.
AWS Data Pipeline vs AWS Glue: Which Tool is Right for ...
Let's revisit the core capabilities of Step Functions using the same two articles, which describe it from slightly different angles.
Read the section 'AWS Step Functions: Features and Capabilities'. Pay attention to its integrations and the common data processing use cases.
As a reminder, Step Functions excels at:
- Complex Logic: It supports branching (
Choicestate), parallel execution (Parallelstate), dynamic iteration (Mapstate), and sophisticated error handling and retry mechanisms. - Serverless Orchestration: The service itself is serverless, so you don't manage any infrastructure for the workflow logic. You only pay for state transitions.
- Deep Integration: It can directly integrate with and control over 220 AWS services, making it the central nervous system for modern applications and data pipelines.
- Event-Driven: It is a natural target for Amazon EventBridge, allowing you to build highly responsive, event-driven pipelines.
The image below shows a perfect example of a modern data processing workflow orchestrated by Step Functions. An S3 event triggers a state machine that coordinates Lambda functions and a Glue crawler to process data and make it available for querying in Athena.
This visual represents everything that makes Step Functions the preferred choice for new data engineering workflows.
3. Head-to-Head Comparison
Now, let's put the two services side-by-side. Understanding these distinctions is key to correctly answering exam questions that might involve either service.
| Feature | AWS Data Pipeline (Legacy) | AWS Step Functions (Modern) |
|---|---|---|
| Primary Use Case | Scheduled, batch data movement and transformation. | General-purpose workflow and application orchestration. |
| Architecture | Template-based (JSON). Manages underlying compute resources (e.g., EC2, EMR). | Visual state machine. Serverless orchestration layer. |
| Workflow Logic | Primarily sequential with basic preconditions and retries. | Advanced logic: branching, parallelism, dynamic loops, complex error handling. |
| Triggers | Primarily schedule-based. Can be started on-demand. | Event-driven (EventBridge), API call (SDK/API Gateway), scheduled, or started from another service. |
| Integrations | Integrates with a smaller set of older services like EMR, RDS, DynamoDB, and on-premise sources. | Native integration with over 220 AWS services, especially serverless components like Lambda, Glue, and SNS. |
| State Management | Limited. Tracks the state of pipeline activities (e.g., WAITING_FOR_RUNNER, RUNNING). |
Detailed, built-in state management. Each execution has a complete visual history of inputs, outputs, and state transitions. |
| Current Status | Legacy service. Closed to new customers. No new features. | Actively developed. The standard for new workflow orchestration on AWS. |
Test your understanding!
An organization has a requirement to build a new data processing pipeline. The pipeline must be triggered whenever a new file is uploaded to S3. The workflow needs to first run a validation Lambda function. If the validation passes, it should simultaneously start a Glue ETL job and send a success notification to an SNS topic. If it fails, it should move the file to an 'error' folder and alert an operations team.
Which service should you use to orchestrate this workflow, and why?
Show answer
You should use AWS Step Functions.
The reasons are:
- Complex Logic: The workflow requires branching ("if validation passes..."), which is a core feature of the
Choicestate in Step Functions. - Parallel Execution: The requirement to run a Glue job and send an SNS notification simultaneously is a perfect use case for the
Parallelstate. - Error Handling: Step Functions provides robust, built-in error handling (
Catchblocks) to implement the "if it fails..." logic cleanly. - Modern Integrations: It needs to orchestrate modern, serverless components like Lambda and Glue, which is a primary strength of Step Functions.
AWS Data Pipeline would be a poor choice because it lacks the advanced branching, parallelism, and fine-grained error handling needed for this workflow.
Conclusion: Making the Right Choice for the Exam
We have compared the legacy AWS Data Pipeline with the modern AWS Step Functions. For your work and for the certification exam, the distinction is clear.
Key Takeaways:
- AWS Data Pipeline is a legacy service for scheduled, batch data movement. You should only consider it if an exam question specifically mentions an existing legacy architecture, a migration from Data Pipeline, or a very simple, recurring data copy task between older AWS services.
- AWS Step Functions is the modern, default choice for orchestrating virtually all new data pipelines. Its serverless nature, support for complex logic (branching, parallelism), extensive integrations, and event-driven capabilities make it far more powerful and flexible.
- The key deciding factor is complexity and architecture. If the workflow involves anything beyond a simple, linear, scheduled job—especially if it uses serverless components like Lambda or needs conditional logic—Step Functions is the correct answer.
Next Up:
In this lesson, we saw how Step Functions is the master orchestrator. In our final lesson of this module, we will zoom in on one of its most critical workers: AWS Lambda. We'll explore how to integrate Lambda functions into data pipelines for lightweight, event-driven transformations, a fundamental pattern you will use constantly in data engineering on AWS.