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Begin by familiarizing yourself with the LinkedIn Ads API documentation. This step is crucial as it provides an overview of the available endpoints, authentication processes, and the type of data you can extract. You will need to generate an access token to authenticate API requests.
Create a LinkedIn Developer account if you haven’t already. Once logged in, create a new app which will provide you with the necessary credentials (Client ID and Client Secret) needed to authenticate API requests. Ensure that your app has the necessary permissions to access LinkedIn Ads data.
Use the LinkedIn OAuth 2.0 authentication flow to generate an access token. This involves constructing an authorization URL, obtaining an authorization code, and then exchanging this code for an access token. The token will allow you to make API requests to access LinkedIn Ads data.
Write a script (in Python, for example) to send HTTP requests to the LinkedIn Ads API endpoints for the data you need. Use the access token for authentication in your requests. Parse the JSON responses to extract the required data fields, such as campaign performance metrics, ad analytics, etc.
Once the data is extracted, transform it to match the schema of your Snowflake tables. This might involve cleaning and reformatting the data, such as converting JSON fields to CSV or transforming date formats. Ensure that the data types (e.g., strings, integers, dates) align with those in your Snowflake schema.
Use Snowflake's native capabilities to stage your data. You can use the Snowflake web interface or SnowSQL command-line client to upload your transformed data files (e.g., CSV) to an external stage or a Snowflake internal stage. Ensure your Snowflake account and warehouse are set up and configured correctly.
Execute a `COPY INTO` command in Snowflake to load the data from the stage into your target tables. This command allows you to specify file format options and handle potential data quality issues (e.g., skipping rows with errors). Verify the data load by querying the target tables to ensure the data has been imported successfully.
By following these steps, you will be able to move data from LinkedIn Ads to your Snowflake destination without the use of 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?
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