Airflow SubDAGs & TaskGroups Concept | Parallel Processing | Nested TaskGroups | k2analytics.co.in

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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) Passing DataFrame Object from Extract to Transform to Load Function
6) Connections and Hooks, airflow.hooks.postgres_hook, PostgresHook (pip install apache-airflow-providers-postgres)
7) SubDAGs, TaskGroups, Parallel Processing
Airflow is a platform to programmatically author, schedule, and monitor workflows.
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.in/blog
Write to me at : [e-mail]

[@thiruankireddy4563]

Your content really highly rated especially the way of explanation

[@nguyenminhoan7882]

sir how can i Parallel Processing but with dag_ids, not task_id

[@s.m.shreyas918]

nice explaination but , kindly provide code also.

[@RajeshJakhotiaAIML]

Pause the video and type. It will be useful practice

[@sedatopalhan9382]

How can Access this video source code and Airflow Topic source codes? Could you prode github repo🙏

[@RajeshJakhotiaAIML]

Suggest you should pause the video and type the codes. I personally feel coding is best learnt by typing syntax, hands-on coding.

[@ashwinkumar5223]

Sir, how to call a operators declared in DAG file from one new python file as a one Task

[@RajeshJakhotiaAIML]

Kindly clarify with more details. I couldn't understand the question

[@ashwinkumar5223]

@@RajeshJakhotiaAIML Some ssh operators are declared in DAG file. I want to call those ssh operators from one python file to invoke store proc.

[@RajeshJakhotiaAIML]

@@ashwinkumar5223 Calling the stored procedure from python callable function should not be difficult. You need to important the Python File, preferably if you can make it a Class Object then all the functions would be readily available. In that you have to write a code connecting to database and call the Stored Procedure