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Begin by accessing LinkedIn Ads data through the LinkedIn Marketing Developer Platform. You need to use LinkedIn's API to programmatically request data from your ad campaigns. First, create a LinkedIn Developer account and set up an application to get your API keys. Use the API to extract data such as campaign performance, clicks, impressions, and more.
Implement OAuth 2.0 for authenticating with LinkedIn's API. Obtain an access token by using your application's client ID and client secret. Make sure to handle token refreshes properly to maintain seamless data extraction. Use this token to authorize your data extraction requests to LinkedIn Ads.
Once you have extracted the data from LinkedIn Ads, structure it into a JSON format for easier processing. JSON is a lightweight data-interchange format that is easy to read and write for humans and machines. Ensure that the data fields are correctly labeled and organized to be compatible with Google Cloud Pub/Sub.
Log in to your Google Cloud Platform account and create a new project if you haven't already. Enable the Pub/Sub API for your project. This will allow you to create topics and subscriptions to manage and distribute your data efficiently.
Navigate to the Pub/Sub section of your Google Cloud Console and create a new topic. A topic is a named resource to which messages are sent by publishers. This will serve as the endpoint for your LinkedIn Ads data to be published.
Write a Python script to publish your structured JSON data to the Google Pub/Sub topic. Use the Google Cloud Client Library for Python to interact with Pub/Sub. Install the library using pip (`pip install google-cloud-pubsub`) and use it to authenticate with your Google Cloud project. Write a script that reads your JSON data and publishes it to the Pub/Sub topic you created.
Set up a cron job or use Cloud Scheduler to automate your Python script execution at regular intervals. This ensures that your data transfer from LinkedIn Ads to Google Pub/Sub occurs automatically and continuously without manual intervention. Adjust the frequency of the script execution based on your data update needs (e.g., hourly, daily).
By following these steps, you can efficiently move data from LinkedIn Ads to Google Pub/Sub without the need for third-party connectors or integrations, allowing for seamless data processing and analysis.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
LinkedIn ads helps businesses of any size achieve their goals and reach their target market. Over 850M active professionals are on LinkedIn. Target your audience them by job title, function, industry, and more.
LinkedIn Ads API provides access to a wide range of data related to LinkedIn advertising campaigns. The following are the categories of data that can be accessed through the API:
1. Ad Campaign Data: This includes data related to the performance of ad campaigns such as impressions, clicks, conversions, and spend.
2. Audience Data: This includes data related to the audience targeted in the ad campaigns such as demographics, job titles, industries, and locations.
3. Account Data: This includes data related to the LinkedIn advertising account such as account balance, billing information, and account settings.
4. Ad Creative Data: This includes data related to the ad creatives used in the campaigns such as ad formats, images, and headlines.
5. Conversion Tracking Data: This includes data related to the conversion tracking set up for the campaigns such as conversion events, conversion values, and conversion tracking tags.
6. Engagement Data: This includes data related to the engagement of the audience with the ad campaigns such as likes, comments, and shares.
7. Performance Data: This includes data related to the overall performance of the ad campaigns such as click-through rates, conversion rates, and cost per click.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
