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AWS Data Pipeline vs AWS Step Functions: What are the differences?
Developers describe AWS Data Pipeline as "Process and move data between different AWS compute and storage services". AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email. On the other hand, AWS Step Functions is detailed as "Build Distributed Applications Using Visual Workflows". AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
AWS Data Pipeline can be classified as a tool in the "Data Transfer" category, while AWS Step Functions is grouped under "Cloud Task Management".
Pros of AWS Data Pipeline
- Easy to create DAG and execute it1
Pros of AWS Step Functions
- Integration with other services6
- Easily Accessible via AWS Console4
- Complex workflows4
- High Availability2
- Workflow Processing2