data-pipelines-with-apache-airflow | Code for Data Pipelines with Apache Airflow | BPM library

by BasPH Python Version: 1.0 License: Non-SPDX

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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 Automation, BPM applications. data-pipelines-with-apache-airflow has no bugs, it has no vulnerabilities, it has build file available and it has low support. However data-pipelines-with-apache-airflow has a Non-SPDX License. You can download it from GitHub.

Code accompanying the Manning book Data Pipelines with Apache Airflow.

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data-pipelines-with-apache-airflow has a low active ecosystem.

It has 499 star(s) with 262 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 18 open issues and 4 have been closed. On average issues are closed in 1 days. There are 16 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of data-pipelines-with-apache-airflow is 1.0

Quality

data-pipelines-with-apache-airflow has 0 bugs and 0 code smells.

Security

data-pipelines-with-apache-airflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

data-pipelines-with-apache-airflow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

data-pipelines-with-apache-airflow has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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data-pipelines-with-apache-airflow releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed data-pipelines-with-apache-airflow and discovered the below as its top functions. This is intended to give you an instant insight into data-pipelines-with-apache-airflow implemented functionality, and help decide if they suit your requirements.

List of available ratings
Convert a date string to a timestamp
Generate the tasks for the given dataset
Returns True if there is no ratings for the given time period
Gets the list of ratings for a given date range
Get data from the API endpoint
Close the session
Fetch the ratings for a given time range
Fetch ratings from the API
Get all ratings for a given month
Return a session object
Get a paginated list of ratings
Generate a workflow for a given dataset
Download the ml - 25 ratings dataset
Show all events
Convert a string to a datetime object
Calculate template stats
Generate a pandas DataFrame containing events for the given end date
Generate the events for a day
Rank a list of movies by rating
Fetch weather
Read ratings from a csv file
Write the ratings to the given directory
Clean up old sales
Deploy the model
Fetch sales objects

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data-pipelines-with-apache-airflow Key Features

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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

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Vulnerabilities

No vulnerabilities reported

Install data-pipelines-with-apache-airflow

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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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