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Begin by accessing your LinkedIn Ads account. Navigate to the Campaign Manager and identify the campaigns or datasets you need to export. LinkedIn allows you to download reports in CSV format directly from the Campaign Manager. Ensure you have the necessary permissions to export this data.
Once you have identified the data you need, use LinkedIn's built-in export functionality to download the data as a CSV file. You can typically find this option under the "Export" button in the Campaign Manager. Choose the appropriate metrics and date range for your analysis before exporting.
After exporting the data, review the CSV file to ensure that it includes all necessary fields and is formatted correctly. Clean the data as needed by removing duplicates, handling missing values, and converting data types for compatibility with Databricks Lakehouse. Save the cleaned data locally on your machine.
Log into your Databricks account and set up a new workspace if one is not already available. Create a new cluster or use an existing one, ensuring that it has adequate resources to handle the data you plan to upload. Configure the cluster settings as per your requirements.
Once your environment is set up, navigate to the "Data" tab in Databricks and select "Add Data." Here, you'll be able to upload your CSV file. Follow the prompts to choose your file from your local system and specify any necessary options, such as delimiter type and whether to infer schema automatically.
After uploading, use Spark SQL or Databricks notebooks to transform the data as needed. You might need to perform operations such as normalizing data formats, joining datasets, or aggregating metrics. Once the data is transformed, save it to a suitable format (e.g., Delta Lake format) in the Databricks Lakehouse for optimized querying and storage.
Validate the data in the Databricks Lakehouse by running sample queries to ensure accuracy. Once verified, consider setting up a script or notebook in Databricks to automate this data import process in the future. You can use Databricks Jobs or a scheduling tool to run this script at regular intervals, ensuring your data remains up-to-date.
By following these steps, you can effectively move data from LinkedIn Ads to a Databricks Lakehouse without relying on third-party connectors or integrations.
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:
