Data-Pipelines-with-Apache-Airflow | data pipeline to automate data warehouse ETL | Data Migration library

by shravan-kuchkula Python Version: Current License: No License

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kandi X-RAY | Data-Pipelines-with-Apache-Airflow Summary

Data-Pipelines-with-Apache-Airflow is a Python library typically used in Migration, Data Migration applications. Data-Pipelines-with-Apache-Airflow has no bugs, it has no vulnerabilities and it has low support. However Data-Pipelines-with-Apache-Airflow build file is not available. You can download it from GitHub.

Developed a data pipeline to automate data warehouse ETL by building custom airflow operators that handle the extraction, transformation, validation and loading of data from S3 -> Redshift -> S3

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Data-Pipelines-with-Apache-Airflow has a low active ecosystem.

It has 61 star(s) with 42 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

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The latest version of Data-Pipelines-with-Apache-Airflow is current.

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Data-Pipelines-with-Apache-Airflow has 0 bugs and 0 code smells.

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Community Discussions

Trending Discussions on Data-Pipelines-with-Apache-Airflow

Meaing of `schedule_interval=None` and `start_date=airflow.utils.dates.days_ago(n)` in an Airflow DAG?

QUESTION

Meaing of `schedule_interval=None` and `start_date=airflow.utils.dates.days_ago(n)` in an Airflow DAG?

Asked 2021-Nov-02 at 18:28

I don't understand how to interpret the combination of schedule_interval=None and start_date=airflow.utils.dates.days_ago(3) in an Airflow DAG. If the schedule_interval was '@daily', then (I think) the following DAG would wait for the start of the next day, and then run three times once a day, backfilling the days_ago(3). I do know that because schedule_interval=None, it will have to be manually started, but I don't understand the behavior beyond that. What is the point of the days_ago(3)?

...

ANSWER

Answered 2021-Nov-02 at 18:28

Your confusion is understandable. This is also confusing for the Airflow scheduler which is why using dynamic values for start_date considered a bad practice. To quote from the Airflow FAQ:

We recommend against using dynamic values as start_date

The reason for this is because Airflow calculates DAG scheduling using start_date as base and schedule_interval as period. When reaching the end of the period the DAG is triggered. However when the start_date is dynamic there is a risk that the period will never end because the base always "moving".

To ease your confusion just change the start_date to some static value and then it will make sense to you.

Noting also that the guide that you referred to was written before AIP-39 Richer scheduler_interval was implemented. Starting Airflow 2.2.0 it's much easier to schedule DAGs. You can read about Timetables in the documentation.

Source https://stackoverflow.com/questions/69813933

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Data-Pipelines-with-Apache-Airflow

You can download it from GitHub.
You can use Data-Pipelines-with-Apache-Airflow like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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