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Begin by familiarizing yourself with the LinkedIn Ads API documentation. This will help you understand the endpoints available for data extraction, authentication, and limitations such as rate limits and data access permissions. Ensure you have the necessary permissions to access the API and extract the data you need.
Create a LinkedIn Developer Application to obtain your API credentials. Go to the LinkedIn Developer Portal, create a new app, and note down the Client ID and Client Secret. These credentials are essential for authenticating API requests.
Use the OAuth 2.0 protocol to authenticate and obtain an access token. This involves directing users to LinkedIn's authorization URL, where they will authorize your application to access their data. Upon successful authorization, you will receive an authorization code, which you can exchange for an access token using your Client ID and Client Secret.
Utilize the access token to send API requests to LinkedIn Ads endpoints. Use the appropriate API endpoint to retrieve the specific data you need, such as campaign performance or audience insights. Make sure to handle pagination and rate limits, as LinkedIn may restrict the number of records you can fetch in a single request.
Once you have extracted the data, transform it into a format suitable for insertion into your Oracle database. This might involve cleaning the data, converting it into a CSV or JSON format, and ensuring that data types are compatible with your Oracle database schema.
Establish a connection to your Oracle database using a programming language like Python or Java, which has built-in libraries for database connectivity (e.g., cx_Oracle for Python). Ensure you have the necessary credentials and network access to connect to the Oracle database server.
Write a script to load the transformed data into your Oracle database. Use SQL INSERT statements or bulk loading techniques to insert the data efficiently. Make sure to handle any potential errors, such as data type mismatches or constraint violations, and verify that the data is correctly inserted into the target tables.
By following these steps, you can directly move data from LinkedIn Ads to an Oracle DB 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: