Balyasny Asset Management (BAM) is a diversified global investment firm founded in 2001 with over $20 billion in assets under management. As dbt took hold at BAM, we had multiple teams building dbt projects against Snowflake, Redshift, and SQL Server. The common question was: How can we quickly and easily productionise our projects? Airflow is the orchestrator of choice at BAM, but our dbt users ranged from Airflow power users to people who’d never heard of Airflow before. We built a single solution on top of Cosmos that allowed us to: Decouple the dbt project from the Airflow repository Have each dbt node run as a separate Airflow task Allow users to run dbt with little to no Airflow knowledge Enable users to have fine-grained control over how dbt is run and to combine it with other Airflow tasks Provide observability, monitoring, and alerting.

Using various operators to perform daily routines. Integration with Technologies: Redis: Acts as a caching mechanism to optimize data retrieval and processing speed, enhancing overall pipeline performance. MySQL: Utilized for storing metadata and managing task state information within Airflow’s backend database. Tableau: Integrates with Airflow to generate interactive visualizations and dashboards, providing valuable insights into the processed data. Amazon Redshift: Panasonic leverages Redshift for scalable data warehousing, seamlessly integrating it with Airflow for data loading and analytics. Foundry: Integrated with Airflow to access and process data stored within Foundry’s data platform, ensuring data consistency and reliability. Plotly Dashboards: Employed for creating custom, interactive web-based dashboards to visualize and analyze data processed through Airflow pipelines. GitLab CI/CD Pipelines: Utilized for version control and continuous integration/continuous deployment (CI/CD) of Airflow DAGs (Directed Acyclic Graphs), ensuring efficient development and deployment of workflows.