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Apche Handlers Di Hosting – This is a tutorial on writing a data pipeline that imports time-series data from a public API and puts it into a local database that is scheduled to run daily.

This course is part of my data research project in Jakarta Smart City, where I try to help a team of data analysts extract information from data to help solve problems for the city of Jakarta. My job is to create a data pipeline to download, transform and load public API data into a local database.

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Apche Handlers Di Hosting

For this project, I use Python to write the script, PostgreSQL as a database, SQLAlchemy as a Python SQL toolkit, and Apache Airflow as a platform to manage my workflows and daily plans. Additional documentation about Apache Airflow can be found on its official documentation page.

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The data uses TomTom API data. TomTom is a location technology company that offers public APIs related to location and marketing. The data I bring you is an hourly traffic index report for Jakarta, Indonesia. The data comes from this URL endpoint: https://api.midway.tomtom.com/ranking/liveHourly/IDN_jakarta.

The data itself is a 7-day time series of hourly data. Normally I get data on September 7th at 1:30 PM, and I get data from September 1st at 2:00 PM to September 7th at 1:00 PM. When I download the data the next day, September 8th at 8:30am, I get data from September 2nd at 9:00pm to September 8th at 8:00am. So I can’t just get the data and put it in a csv file because it will overwrite the previous data. I also couldn’t just add the data because it would overlap.

To solve this problem with data overlap, I slice the imported data by date and store it in different csv files. Each csv file contains only 24 hours of data. The data from the csv file is exported to the database. Since I run the script daily, I save yesterday’s data. Air flow makes my script work as planned.

There may be problems with this approach and the script may not run for a day. This will cause the data to be lost for one day. This problem can be minimized by using an Airflow trap

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Function where Airflow will execute the script for the sites that have not been processed since the last run date. For example, if Airflow’s last day was 2 days ago and yesterday’s execution is skipped, Airflow will run the script twice today, once on yesterday’s execution date and once on today’s execution. Therefore, if we don’t skip jobs for more than seven days in a row, Airflow will use this function to avoid losing the data import.

With that in mind, we’re ready to start writing our first data pipeline with Apache Airflow. You can find all the code in my Github account: https://github.com/gsusanto/airflow

First we need to install Airflow and PostgreSQL. For this project I run Airflow and PostgreSQL in a Docker container. I followed a tutorial written by Ivan Rezic here to set up an Airflow container with PostgreSQL and LocalExecutor using Docker and Docker Compose. He has a detailed description of what he put in his docker-compose.yml file and what it does.

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After following the tutorial, you should have a working Airflow container. Make sure your container is working properly by creating and running the container:

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Wait for your air flow chart and website placement to start. Next, test your Airflow web server by opening a local browser and going to HTTP://localhost:8080. You should find the login page. If you follow the tutorial, you can login with user: admin and password: admin1234

We use two directories: the dags folder and the log folder. The dags folder is where we put our python scripts and our DAGs. The log folder is where we can check the logging of our DAG operations.

First, we write a configuration file to store the API URL and csv log in the config.py file. We can access these variables in another Python file by importing config and calling config.VARIABLE_NAME

Next we write a data entry script to import the data and put it into csv files. First, we get the data from the TomTom API in the import_data() function. Then we transform the data with transform_data(). Here we rename it, place the header, change the timestamps to the time zone of Jakarta, and add a new column indicating the name of the day. Then get_new_data() slices the data to get yesterday’s data. Finally, save the data to a csv file called “Tomtom_.csv” in the save_new_data_to_csv() function.

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So we can start writing the script to export the data to the database. The first thing to do is to create a table with a name

Next, we need to create the Link class in Tomtom. The Connection class is used to connect our Python to our PostgreSQL database using SQLAlchemy. The Tomtom class will describe us

Finally, we write our DAG script that tells the Scheduler and Executioner to run our script at the specified times. The first DAG we’ll write is a DAG that will run our single data migration script, and it will appear

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Table in our database. We use BashOperator to ask Airflow to run a bash script. Our bash script is a single line command to run our Python script. We enter your affiliate name, which is stored in the Flight index

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Flag Before running this script, we need to initialize a Flight variable called “data_dev_connection”.

Finally, we will write our main DAG script and run our Tomtom capture script and export it to the db script every day. The location can be specified below

Set the start date to 6 days ago so that Airflow can import all 7 days of TomTom data and reset

Point to the Truth. So we set up 2 BashOperators, one for each script. These bash commands are bypassed on the day of the job when we use Flight commands

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Macro and } formula to fill in the workday. Airplane will display this file and convert the pattern } to a working date. So the actual bash command is “python /opt/airflow/dags/tomtom_ingestion.py — — date 2021-09-20”.

All the documents you need. Now we are ready for our Airflow website at localhost:8080. The first thing we need to set up is the air flow index to save us

To a Postgres database. For this go to Admin > Index. Then add a new variable with key: “data_dev_connection” and value: “postgresql+psycopg2://airflow:airflow@postgres/airflow”.

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We can go back to our main DAGs page and start managing our DAGs. The first DAG we will run is the data_migration DAG. We can start by changing the DAG and then resetting it. Have one good organization. Running this DAG will initialize it

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Next, our main DAG tomtom changes. Likewise, we turn it on and run it. The Flight will run 6 times with different working days because we have fixed the start date 6 days ago. This will create our csv file and fill our database for 6 days.

One possible use of this data is to create a traffic dashboard in Jakarta. This dashboard can be used to decide, for example, what is the best time to go and avoid rush hour traffic. Together with my colleague Mas Hadhari, we use data imported from TomTom to create:

When we collect long-term traffic index data in Jakarta, we can use this data to add to the dashboard for further analysis. One potential application that benefits from these data is to measure the effectiveness of government-sanctioned public employment policies. We can do this by comparing the traffic index over time during the period of public works restrictions and also comparing it to the traffic index before and after the period of the social travel rules. The data pipeline can also be improved by creating a data cleanup schedule that will delete old csv files as we have stored this data in the database.

(Data Analyst) from Jakarta Smart City, Data and Analytics Group. All opinions expressed in this article are personal and do not reflect the opinions of Smart City Jakarta or the Provincial Government of DKI Jakarta. This is what you need for hosting. Use phpMyAdmin with Terminal (SSH). To import a database into phpMyAdmin, use the import phpMyAdmin database link.

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Import a database into phpMyAdmin without being able to import a file up to 50MB. To import a data file up to 50 MB in size, you can use a port or SSH host.

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