Amazon SageMaker
Follow a step-by-step tutorial for using Airflow to orchestrate the training and testing of a SageMaker model.
Apache Kafka/Confluent
How to produce to and consume from Kafka topics using the Kafka Airflow provider
Azure Blob Storage
Learn how to create an Azure Blob Storage connection in Airflow.
Azure Container Instances
Learn how to orchestrate containers with Azure Container Instances from your Airflow DAGs.
Azure Data Factory
2 items
Microsoft Entra Workload ID
Learn how to create an Azure Workload Identity connection in Airflow.
BigQuery
Learn how to create a BigQuery connection in Airflow.
Cohere
Learn how to integrate Cohere and Airflow.
Databricks
2 items
dbt Cloud
2 items
dbt Core
dbt Core is an open-source library for analytics engineering that helps users build interdependent SQL models for in-warehouse data transformation, using ephemeral compute of data warehouses.
DuckDB
Learn how to use DuckDB with Airflow.
Fivetran
Learn how to orchestrate Fivetran syncs using Airflow
Great Expectations
Orchestrate Great Expectations data quality checks with your Airflow DAGs.
Jupyter notebook
Run a parameterized Jupyter notebook using Airflow and the Astro CLI.
Marquez
Use OpenLineage and Marquez to get lineage metadata locally from your Airflow DAGs.
MLflow
How to use MLflow with Airflow in three different ways.
MongoDB
Learn how to load data into MongoDB with your Apache Airflow DAGs.
Microsoft SQL Server
Learn how to create a Microsoft SQL Server connection in Airflow.
OpenAI
Learn how to integrate OpenAI and Airflow.
OpenSearch
Learn how to integrate OpenSearch and Airflow.
pgvector
Learn how to integrate pgvector and Airflow.
Pinecone
Learn how to integrate Pinecone and Airflow.
Postgres
Learn how to create a Postgres connection in Airflow.
Redshift
2 items
Snowflake
3 items
Soda Core
Learn how to orchestrate Soda Core data quality checks with your Airflow DAGs.
Weaviate
Learn how to integrate Weaviate and Airflow.
Weights and Biases
Learn how to use Airflow and Weights and Biases to manage and visualize your ML model lifecycle.