Category: Analytics
Service: AWS Data Pipeline
Answer:
An AWS Data Pipeline workflow consists of the following components:
Data nodes: These are the data sources and destinations that are used by the pipeline. They can be Amazon S3, Amazon RDS, Amazon DynamoDB, or other data storage services.
Activities: These are the data processing steps that are performed on the data. Activities can be data transformations, such as data conversion or filtering, or they can be AWS service tasks, such as running an Amazon EMR job.
Preconditions: These are conditions that must be met before an activity can be run. Preconditions can be based on data availability, time of day, or other factors.
Schedule: This determines when the pipeline runs and how often.
Failure handling: This specifies how the pipeline should handle failures, such as retrying failed activities or sending notifications.
All of these components work together to create a pipeline that can process and transform data. The pipeline takes data from a source, performs a series of transformations on the data, and then writes the transformed data to a destination. The pipeline can be run on a schedule or triggered manually, and it can handle failures and errors in a variety of ways.
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