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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
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...
Write to me at : ar.jakhotia@k2analytics.co.in
30 сен 2024
