📄️ Best Practices

Recommended Airflow best practices for Datacoves: set static start dates, use catchup=False, avoid dynamic scheduled dates, and follow official Airflow docs.

📄️ Config Defaults

Reference for Datacoves Airflow default configuration: executor, parallelism, scheduler, Kubernetes worker timeout, and database retry settings.

📄️ Variables

Reference for Datacoves-injected Airflow variables: DAG paths, dbt settings, environment slugs, version info, and notification configuration.

📄️ DAG Generators

Reference for dbt-coves DAG generators in Datacoves: AirbyteGenerator, AirbyteDbtGenerator, FivetranGenerator, and FivetranDbtGenerator with all params.

📄️ Decorators

Reference for Datacoves Airflow decorators: @task.datacoves_bash and @task.datacoves_dbt — parameters, usage examples, and service connection integration.

📄️ CLI Commands

Reference for Datacoves CLI commands: datacoves my import, my pytest, and my api-key for managing My Airflow instances from the VS Code terminal.

📄️ Service Connection Vars

Environment variables injected by Datacoves service connections for Snowflake, Redshift, BigQuery, and Databricks warehouse authentication in Airflow.

📄️ Operators

Reference for Datacoves Airflow operators: DatacovesBashOperator and DatacovesDbtOperator, their parameters, behavior, and usage examples in DAG files.

📄️ Billing

Understand how Datacoves bills Airflow usage based on Kubernetes worker pod running time, what is and isn't billed, and how to query task instance tables.