Choosing the Right Orchestration Service for Your Data Pipeline
Comparative Analysis between Workflows in AWS Glue, AWS Step Functions, and Amazon Managed Workflows for Apache Airflow (MWAA)
- Purpose-built orchestrators: These AWS native data orchestration services are built with a specific purpose for a specific use case.
- Workflows in AWS Glue - AWS Glue is a serverless data integration service that makes it easy for analytics users to discover, prepare, move, and integrate data from multiple sources. You can use it for analytics, machine learning, and application development. It also includes additional productivity and data ops tooling for authoring, running jobs, and implementing business workflows. Workflows in AWS are part of AWS Glue and as such, it's part of a fully managed serverless service that's native to AWS. Glue Workflows provide orchestration of AWS Glue jobs, crawlers and triggers. And if the data pipeline is built using jobs and crawlers, this allows to have a single platform, AWS Glue, that can handle all aspects of the data pipeline. It is particularly well suited for complex, multi job ETL operations, involving Python or Apache spark. It provides a visual designer using workflow graphs. It supports ingestion of streaming data and batch data and it also provides template based workflow creation allowing you to reuse some of your data pipeline assets.
- General purpose orchestrators: These AWS native data orchestration are built to orchestrate workflows of various natures.
- AWS Step Functions - It is a visual workflow service that helps developers use AWS services to build distributed applications to automate processes, to orchestrate micro services and also create data and machine learning pipelines.
- Amazon Managed Workflows for Apache Airflow (MWAA) - It is a managed orchestration service for Apache Airflow that you can use to setup and operate data pipelines in the cloud at scale. Apache Airflow is an open-source tool used to programmatically author, schedule, and monitor sequences of processes and tasks referred to as workflows.
- End-to-end pipeline: Describes the terminology used to define the end-to-end pipeline for a data pipeline.
- Steps: Describes the components involved in each step, highlighting the unique classification.
- Runtime: Describes the distinctive term for the runtime execution of the data pipeline orchestration.
- Error handling: Describes how each service handles errors with their specific constructs.
- Step dependency: Describes how dependencies in between steps are defined within each service.
- Deployment Model: Describes the type of deployment model the service uses.
- Authoring: Describes how users can create and define their workflows.
- Scheduling: Describes how workflows are scheduled and executed at specific times or intervals.
- Multiple Scheduling: Describes whether the service allows for the configurations of multiple schedules for the same workflow.
- High Availability: Describes high availability and resiliency features offered to maintain robust workflows.
- Invoke: Describes the different ways that workflows can be invoked.
- Backfill / Catch-up: Describes if the service is able to process historical data in a data store that may have been missed initially when the workflows were run.
- Integration: Describes how well integrated it is with both AWS and non-AWS services.
- Data Transferred / Payload: Describes the volume and method of data exchange between different steps or components within a workflow.
- Error Handling: Describes workflow resilience and fault tolerance.
- Failure Notification: Describes how users get alerted in the event of a failure.
- Observability: Describes the visibility and monitoring capabilities of the workflow execution, providing insight on performance and internal operations.
- Loop Iterations: Describes if the service supports the repetition of certain steps or tasks in a workflow to be repeated as many times as needed, without needing to define each repetition.
- Conditional Branching: Describes the ability to direct the flow of execution of a workflow based on conditional logic or decision points.
- Concurrent Executions: Describes the ability to run multiple executions of a workflow simultaneously.
- Maximum Number of Steps: Describes the maximum number of steps a user can have in a single workflow.
- Batch Events: Describes the ability to trigger batch jobs or workflows involving large sets of data as opposed to single records.
- Streaming Data: Describes the ability to orchestrate data that is being ingested in real time.
- Cost: Describes the price of orchestrating workflows within the service.
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