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In order to determine the best practice for running Airflow at scale, datagovteam would like to create an Airflow POC using Kubernetes in Docker (KinD).
Acceptance Criteria
[ACs should be clearly demoable/verifiable whenever possible. Try specifying them using BDD.]
GIVEN [a contextual precondition]
[AND optionally another precondition]
WHEN [a triggering event] happens
THEN [a verifiable outcome]
[AND optionally another verifiable outcome]
Background
[Any helpful contextual notes or links to artifacts/evidence, if needed]
This is the airflow-equivalent work for the datagov-brokerpak-solr section under the "Practical Learning (the Solrcloud example here "should" be very similar to the environment in which airflow would exist.)" chapter in
This ticket is more-so doing everything by-hand. Follow on work would being converting the "by-hand" work into "terraform" work and then finally into "broker" work.
"By-Hand": invoking the kubectl and helm commands
"Terraform": representing everything in terraform code and defining the resources based on the kubernetes and helm providers based on the same logic used in the "by-hand" method.
"Broker": packaging the terraform code into the CSB and creating resources by using the CSB CLI.
User Story
In order to determine the best practice for running Airflow at scale, datagovteam would like to create an Airflow POC using Kubernetes in Docker (KinD).
Acceptance Criteria
[ACs should be clearly demoable/verifiable whenever possible. Try specifying them using BDD.]
[AND optionally another precondition]
WHEN [a triggering event] happens
THEN [a verifiable outcome]
[AND optionally another verifiable outcome]
Background
[Any helpful contextual notes or links to artifacts/evidence, if needed]
Security Considerations (required)
[Any security concerns that might be implicated in the change. "None" is OK, just be explicit here!]
Sketch
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