Airflow etl directory10/18/2023 ![]() The logs are going to be dumped to a file or database as well. Workers : These are the machines that actually do the tasks they can can be separate machines from scheduler or the same.Īirflow will dump all information about your DAGs into logs. Scheduler : This scrolls the file system and puts things into the queue. Webserver (Flask based UI) : The webserver talks to metadata db to get information to present. Metadata DB (database) : Keeps track of tasks, how long each run took, etc. Tasks that are dependent upon the completion of other tasks are run sequentially and tasks that are not dependent upon one another can be run in parallel. Each node in the DAG is a task that needs to be compeleted. Data pipelines in Airflow are made up of DAGs (Directed Ayclic Graphs) that are scheduled to be completed at specific times. Now we can dive into Airflow!Īs mentioned in the introduction Airflow is a platform to schedule and monitor workflows as well as a method to set up data pipelines. Lastly, note that I don't have any password necessary to access the database, this was just for convience. Since this is just one single table, I'm not going to worry about such things as primary and foreign keys (checkout this post to learn what these are). This script is stored in a file name makeTable.py and can be run using the command, python makeTable.pyįrom the appropriate directory and before we set up our Airflow job. Specifically I transform and load the following into the database, I only take a subset of the data that is returned from OpenWeatherMap. ![]() The code below creates a table called weather_table in a local PostgreSQL database named WeatherDB. I went over the basics of how to use PostgresSQL in a previous blog post, so I'll just present the code I used to make one here. Now, let's go over how to set up a PostgreSQL database. A great introduction into using API's with Python can be found here. Note that this is the exact Bash command that I'll use to have Airflow collect daily weather data. The above code is stored in a file title getWeather.py and be run from the command line by typing from the appropriate directory: python getWeather.py If the request is succesfull, then weather data is returned and is then dumped into a JSON file with a name that is the current date using the JSON package. Proper exception handling here is definitely something I will add in the future. After the request has been made, I check to see if it was successful by checking the status_code, result.status_code = 200 In order to use this code yourself you would have to obtain your own API key and either substitute it into the code directly or have a variable API_KEY = your-api-key in a config.py file. ![]() Notice that I keep my API key in a seperate file called config.py. To get a better feel for how the request below works, check out the OpenWeatherMap API documentation page here. Below I wrote a module, getWeather.py, that uses a GET request to obtain the weather for Brooklyn, NY. In Python, this is done using the requests module. To use a Web API to get data, you make a request to a remote web server, and retrieve the data you need. Let's first get started with how to query an API. Airflow is a platform to schedule and monitor workflows and in this post I will show you how to use it to extract the daily weather in New York from the OpenWeatherMap API, convert the temperature to Celsius and load the data in a simple PostgreSQL database. Since the data is a dictionary of strings this means we must transform it before storing or loading into a database. JSON can pretty much be thought of a semi-structured data or as a dictionary where the dictionary keys and values are strings. Often times the data we get back is in the form of JSON. If the query is sucessful, then we will receive data back from the API's server. You can see the source code for this project here.Įxtracting data can be done in a multitude of ways, but one of the most common ways is to query a WEB API. I am just a novice and really frustrated and can't seem to find any answers online.In this blog post I want to go over the operations of data engineering called Extract, Transform, Load (ETL) and show how they can be automated and scheduled using Apache Airflow. I have it downloaded in my /home/project/airflow/dags/finalassignment folder. I'm not quite sure why it is not recognizing the file. When it fails I get this log: ERROR - Task failed with exception # You can override them on a per-task basis during operator initializationĭescription='Apache Airflow Final Assignment', # Operators we need this to write tasks!įrom _operator import BashOperator # The DAG object we'll need this to instantiate a DAG This is the code up to the point of failure: # import the libraries ![]() I am trying to run a DAG but it is failing. So forgive me if I'm missing something obvious. ![]()
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