Tutorials

Automate your Data Scraping with Apache Airflow and Beautiful Soup

Learn how to easily automate your LinkedIn Scraping with Airflow and Beautiful Soup.

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Summarize with AI:

Extracting data from LinkedIn can provide organizations with powerful business intelligence. Whether you're a recruiter looking to streamline candidate sourcing, a marketer seeking to understand your target audience, or a researcher analyzing professional trends, scraping LinkedIn profiles using Python can be invaluable. This tutorial will guide you through building a robust LinkedIn profile scraper using Apache Airflow and Python's BeautifulSoup library, complete with automated data extraction and storage workflows.

We'll combine the orchestration capabilities of Apache Airflow with the parsing power of BeautifulSoup to create a reliable, scalable scraping solution that can handle multiple profiles while respecting LinkedIn's platform guidelines. By the end of this guide, you'll have a fully functional data pipeline that can extract profile information and store it in a PostgreSQL database for further analysis.

What is Data Scraping?

Data scraping is an automated technique for extracting data from websites, applications, or documents. This process enables the efficient collection of large volumes of data, making it invaluable for tasks like market research, lead generation, or data analysis. While Python libraries like Beautiful Soup and Selenium make web scraping more accessible, it's important to note that scraping should always be done ethically, respecting website terms of service and rate limits.

Prerequisites

Below are the prerequisite tools you’ll need to build your own LinkedIn profile data scraper:

You’ll need to install Python 3+ since our scraper code will be using Python.
You’ll need to get Airbyte to do the migration for you. To deploy Airbyte, follow the simple instructions in our documentation here.
You’ll need Apache Airflow 2.0 so that you can programmatically author, schedule and monitor workflows.

Here is a quick command-line snippet that can help you install Apache Airflow on your local machine:

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virtualenv airflow -p python3source airflow/bin/activatepip3 install apache-airflow

You’ll need to install the Apache Airflow provider for Airbyte as well as Beautiful Soup to scrape LinkedIn.

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pip3 install apache-airflow-providers-airbytepip3 install beautifulsoup4

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You’ll need a Chrome driver to log into your LinkedIn account programmatically.
You’ll also need PostgreSQL running in a container so that you can store your scraped data.

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docker run --rm -P -p 127.0.0.1:5432:5432 -e POSTGRES_PASSWORD=password --name postgres postgres:alpine

Step 1: Configuring Apache Airflow

Initiate the Airflow DB by running the following command:

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airflow db init

Create a user for Airflow using the following command while making sure to update the required values with your own.

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airflow users create --username {YOUR_USERNAME} --firstname {YOUR_NAME} --lastname {YOUR_LAST_NAME} --role Admin --email {YOUR_EMAIL}

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Once the packages are installed start the airflow web server and scheduler by running the following commands in two separate terminal windows. Ensure that the virtual environment is active in both windows.

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airflow webserverairflow scheduler

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Once the setup is complete, visit http://localhost:8080 to access the Airflow Web UI.

Enter the credentials used to create the user in the previous step. Once logged in you will have access to the Airflow UI as shown below.

Step 2: Setting up the JSON source in Airbyte

In this recipe, the python scraper will parse and process the LinkedIn page and store data in JSON to be loaded into PostgreSQL. It will then trigger an Airbyte connection that will migrate the scraped data from that file to a PostgreSQL database.

Setup the source for Airbyte by going to Connections > new connection.

Provide a name for the source, and select Source Type as File and then Local Filesystem under provider.

This input file needed by Airbyte needs to be located at the following physical local machine mount path (/tmp/airbyte_local/) which Airbyte mounts to ‘/local’. In this example, we have created a subdirectory under that physical mount path called linkedin and a JSON file called linkedin.json.

The effective logical URL that you can use in Airbyte is /local/linkedin/linkedin.json.

The source configuration should look something like this:

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Once the file source is properly configured, the connection can be tested.

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Step 3: Setting up the PostgreSQL database

The next step is to configure the destination for the scraped data, in this case a PostgreSQL database. If you do not have PostgreSQL set up, you can quickly set one with docker using the following command:

docker run --rm -P -p 127.0.0.1:5432:5432 -e POSTGRES_PASSWORD=password --name postgres postgres:alpine

Once you have PostgreSQL running, you will need a database created that will be used by Airbyte to write the data. Run the following commands to connect to PostgreSQL and create a new database called linkedin.

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docker exec -ti postgres psql -U postgresCREATE DATABASE linkedin;

In the Airbyte UI select PostgreSQL as your destination and enter the following values for your configuration.

Before proceeding, ensure that the connection is successful.

Once the destination is set up, you can create the connection. Set the sync frequency to manual and the sync mode to append.

Once the connection is successfully set up, make a note of the connection ID which will be required for later for configuring Apache Airflow to trigger that connection. The ID can be found in the URL for that connection page.

Step 4: Create an Airflow DAG

The next step is to set up Apache Airflow so that it can trigger the Airbyte API endpoints.

First go to Admin > Connection > AddConnection.

Give the connection ID a name (like airbyte_linkedin_connection in our case) and select Airbyte as the connection type. Enter localhost for host and 8001 for the port. Hit Save.

Now that the Airflow connection to trigger Airbyte is set up, we need to create a DAG inside Airflow that upon execution will run our python scraper, store the data in the JSON file and then trigger the Airbyte sync.

Create a dags folder and the dag file by running the following commands in your terminal. Airflow will automatically pick up the dag created in the folder upon restart.

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cd airflowmkdir dags && cd dagstouch airbyte_airflow_dag.py

To figure out which Linkedin profiles to scrape, the script needs a simple CSV file that contains the URLs to the required profiles as shown. Create a csv file with the following data on your local machine. More profiles can easily be added to the bottom of this file. Keep track of the location of this file since it is needed by the python script.

person_id,profile_url

1,https://www.linkedin.com/in/micheltricot/

2,https://www.linkedin.com/in/jean-lafleur-294083185/

3,https://www.linkedin.com/in/jrhizor/

Download Chrome driver and keep track of its location on your file system. This is an open source tool for automated testing of web apps across many browsers, and in this case we will use it for accessing LinkedIn programmatically in our Python web scraper.

Next copy the following into a file called airbyte_airflow_dag.py. Make sure to update the highlighted values.

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import requestsfrom bs4 import BeautifulSoupimport pandas as pdimport reimport osfrom datetime import datetime import jsonfrom selenium import webdriverfrom airflow import DAGfrom airflow.utils.dates import days_agofrom airflow.providers.airbyte.operators.airbyte import AirbyteTriggerSyncOperator with DAG(dag_id='trigger_linkedin_airbyte_job', default_args={'owner': 'admin'}, schedule_interval='@daily', start_date=days_ago(1) ) as dag: df=pd.read_csv('{PATH_TO}/profiles.csv')driver = webdriver.Chrome('{PATH_TO}/chromedriver') #login using webdriverdriver.get('https://www.linkedin.com/login?trk=guest_homepage-basic_nav-header-signin')username = driver.find_element_by_id('username')username.send_keys('{YOUR_LINKEDIN_USERNAME}')password = driver.find_element_by_id('password')password.send_keys('{YOUR_LINKEDIN_PASSWORD}')sign_in_button = driver.find_element_by_xpath('//*[@type="submit"]')sign_in_button.click()Json_data = []for i in range(df.shape[0]): URL = df['profile_url'][i] #change profile_url here driver.get(URL) soup = BeautifulSoup(driver.page_source, 'html.parser') person_id = df['person_id'][i] name = soup.find('div', {'class': 'display-flex justify-space-between pt2'}).find("h1").text.strip().encode('ascii', 'ignore').decode() position = soup.find('div', {'class': 'display-flex justify-space-between pt2'}).find('div', {'class': 'text-body-medium break-words'}).text.strip().encode('ascii', 'ignore').decode() location =soup.find('div', {'class': 'display-flex justify-space-between pt2'}).find('span', {'class': 'text-body-small inline t-black--light break-words'}).text.strip().encode('ascii', 'ignore').decode() company = soup.find('div', {'class': 'display-flex justify-space-between pt2'}).find('div', {'aria-label': 'Current company'}).text.strip().encode('ascii', 'ignore').decode() try: connec = soup.find('div', attrs={'class' : 'ph5'}).find('span', {'class': 'link-without-visited-state'}).text.strip().encode('ascii', 'ignore').decode() except: connec = soup.find('div', {'class': 'ph5 pb5'}).find('span', {'class': 't-bold'}).text.strip().encode('ascii', 'ignore').decode() no_conn=connec.split()[0] no_conn=no_conn[:3] no_conn=int(no_conn) user_data = dict() user_data['Person ID'] = str(person_id) user_data['Name'] = str(name) user_data['Role'] = str(position) user_data['Location'] = str(location) user_data['Current Company'] = str(company) user_data['Number of Connections'] = int(no_conn) json_data.append(user_data) with open('/tmp/local_airbyte/linkedin/linkedin.json', 'w') as fp: json.dump(json_data, fp, sort_keys=True, indent=4) json_to_postgres = AirbyteTriggerSyncOperator( task_id='airbyte_airflow_linkedin', airbyte_conn_id='airbyte_linkedin_connection', connection_id='{YOUR_AIRBYTE_CONNECTION_ID}', asynchronous=False, timeout=3600, wait_seconds=3

Once the file is saved, restart the airflow webserver and scheduler and login to the Airflow UI again.

Step 5: Running the Airflow DAG and Airbyte sync

Once logged in, the newly added DAG should be visible. The DAG is scheduled to be run once a day but can also be triggered manually.

Click on the DAG to open up the DAG page. Here turn on the DAG and click on Play Button > Trigger DAG. This will begin the LinkedIn scraping.

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During the scraping the script will write data into the /tmp/airbyte_local/linkedin/linkedin.json and should look something like this.

Once the scraping is complete, it will trigger the Airbyte sync.

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Once the sync is complete, you can verify that the Airflow job ran successfully in the UI.

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You can view the tables created by Airbyte by running the following commands:

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docker exec -ti postgres psql -U postgresc linkedindt

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You can view the data imported to the table by running the following SQL table query on the linked table in postgreSQL
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SELECT * FROM linkedin;

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By querying this data, marketing campaigns can be executed to raise awareness for a new product feature based on job titles.

Wrapping up.

Here’s what we’ve accomplished during this recipe:

Configure Apache Airflow
Configure JSON data source in Airbyte
Configure a PostgreSQL Airbyte destination
Create and configure the DAG in Airflow
Running the Airflow DAG and Airbyte Sync
Analyzing the results

This was just one example of how Apache Airflow along with Airbyte can be used to create robust and complex workflows using open source tools.

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