Apache Airflow and Snowflake integration
Orchestrating Snowflake from self-hosted Airflow with the Snowflake provider: SQL API and stage-copy operators, Snowpark and Snowpark container jobs, connection setup and where credentials should live.

Integration pages for the provider packages that connect Airflow to data warehouses, compute platforms, secrets stores, lineage tools and identity providers, based on the Airflow providers documentation.
Orchestrating Snowflake from self-hosted Airflow with the Snowflake provider: SQL API and stage-copy operators, Snowpark and Snowpark container jobs, connection setup and where credentials should live.
Running Databricks jobs, notebooks and SQL from Airflow with the Databricks provider: submit-run versus run-now, deferrable operators that free worker slots, and the DatabricksWorkflowTaskGroup for Databricks Workflows.
Triggering and monitoring dbt Cloud jobs from Airflow with DbtCloudRunJobOperator and DbtCloudJobRunSensor, deferrable waiting, artifact download, and what the provider does not cover, since it targets the hosted dbt Cloud service rather than dbt Core.
Submitting Spark work from Airflow with SparkSubmitOperator, SparkSqlOperator, SparkJDBCOperator and PySparkOperator over Spark Connect, including the Spark connection setup each one needs and the JDBC transfer modes.
Running isolated task containers with the KubernetesPodOperator on any executor: pod specs, deferrable mode, cleanup actions, and handling secrets through Kubernetes Secrets or Airflow connections rather than environment variables. Separate from executor selection.
Moving Airflow connections and variables out of the metadata database into HashiCorp Vault with the Vault secrets backend: KV mount layout, KV version 1 or 2, connection and variable paths, and multiple path lookups.
Emitting data lineage from Airflow tasks with the OpenLineage provider so pipelines, datasets and runs can be traced in a lineage backend, and what a team needs to configure and operate around it.
Using the Keycloak auth manager in Airflow 3 to hold users, roles, groups and permissions in Keycloak, generating API tokens, and the caveats of an alpha component and multi-team clients. Narrower than the general SSO page.
Running BigQuery work from Airflow with the Google provider: BigQueryInsertJobOperator for queries and loads, durable job reconnects on retry, deferrable mode on the triggerer, table and partition sensors, and built-in check operators for data quality.
Orchestrating AWS data services from self-hosted Airflow with the Amazon provider: which services have operators, IAM connection design, S3 and Redshift transfers, EMR and Glue jobs, and running Airflow outside AWS while still driving AWS workloads.
Triggering and monitoring Azure Data Factory pipelines from Airflow: AzureDataFactoryRunPipelineOperator, passing parameters, waiting on the triggerer instead of a worker, the run status sensor, and when to keep ADF for copy activities while Airflow owns the schedule.
Running Trino SQL from Airflow pipelines: the Trino provider and its common SQL dependency, SQLExecuteQueryOperator against Trino, GCS to Trino transfers, lineage through OpenLineage, and connection security for a shared Trino cluster.
Producing to and consuming from Kafka in Airflow with the Kafka provider, and starting DAGs from Kafka messages with asset watchers: offset handling, idempotent consumers, triggerer sizing, and when Kafka belongs in a streaming tool instead of Airflow.
Triggering Airbyte connections from Airflow so ingestion and transformation run in one dependency graph: AirbyteTriggerSyncOperator in synchronous or asynchronous mode, AirbyteJobSensor, client credentials for self-hosted or cloud Airbyte, and retry safety.
Routing Airflow failures and missed deadlines to the on-call channel: the Slack, PagerDuty and Opsgenie providers, notifiers on tasks and Deadline Alerts, avoiding alert floods from retries, and what each alert should link back to.
Running SQL against operational databases and warehouses from Airflow with SQLExecuteQueryOperator and the PostgreSQL, MySQL and Microsoft SQL Server providers: connections, transactions, splitting statements, returning results to XCom and keeping credentials out of DAG code.
Moving files between partner SFTP servers, legacy hosts and object storage with the Airflow SFTP and SSH providers: key-based connections, sensors that wait for drop files, host key checking, and running remote commands without opening broad network access.
Loading and transforming data in ClickHouse from Airflow: the community ClickHouse provider, running SQL steps and checks, batching inserts from upstream systems, and keeping heavy transformation inside ClickHouse rather than on Airflow workers.

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