Airflow TriggerDagRunOperator | Configure DAG dependencies at ease | ETL Pipelines | Master DAG

Airflow is a platform to programmatically author, schedule, and monitor workflows.
The Airflow TriggerDagRunOperator is used to configure the DAG Dependencies.
What does it mean?
Assume you have DAGs for Branch Master, Customer Master, Account Master, and Transaction data. Each DAG has a dependency on the previous dag, meaning that the Customer Master DAG can't run until the data has been extracted for Branch. An account cannot exist without a customer as such completing the ETL of Customer Data is a must before you start the ETL of Account Master. In such a scenario, we can use TriggerDagRunOperator to configure the dependencies.
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Data Engineering with Airflow Content:
1) Getting started with Airflow
2) Creating a Simple ETL DAG using DummyOperator
3) Creating a Simple ETL DAG using PythonOperator
4) Using XCOMs for Cross-Communication between Tasks
5) Trigger DAG with Config Parameters
6)
Use Airflow to author workflows as Directed Acyclic Graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine.
Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Challenges handled by Airflow:
Failures: retry if failure happens(how many times? how often?)
Monitoring: success or failure status, how long does the process runs?
Dependencies: Data dependencies: upstream data is missing
Execution dependencies: job 2 runs after job 1 is finished.
Scalability: There is no centralized scheduler between different cron machines
Deployment: deploy new changes constantly
Process historic data: backfill/rerun historic data
Connect with us on Whatsapp : + 91 8939694874
Website Blog: k2analytics.co...
Write to me at: [e-mail]

[@aasthagupta9381]

Amazing series, thank you for putting this on YT

[@Victor-hc2gq]

When manually executing the master DAG, it calls the secondary DAG but does not execute the secondary DAG immediately as in the video. any idea how to solve it?

[@onlysin530]

Encountered same problem. Apparently for mine, issue looks like its because it's using default sqlite, which doesn't allow parallelism, so DAG can only run sequentially. It will not execute secondary DAG since master DAG is still running.

[@Matt-ft4wb]

🍀 Promo-SM