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To begin, log into your LinkedIn Ads account. Navigate to the Campaign Manager and select the specific campaign from which you want to extract data. Use LinkedIn's reporting tools to generate a report that includes the data fields you need, such as impressions, clicks, and conversions.
Once your report is ready, export the data in a CSV format. LinkedIn provides an option to download reports as CSV files which are easy to handle and can be processed further. Make sure to select all the necessary date ranges and data fields before exporting.
Set up a local development environment with Python or any other preferred scripting language that allows you to manipulate CSV files. Ensure you have access to any libraries needed for processing CSV data, such as Python’s `pandas` library.
Read the CSV file using your chosen language or tool. For example, if using Python, use `pandas` to load the CSV into a DataFrame. Perform any necessary data cleaning or transformation operations, such as handling missing values or formatting dates, to prepare the data for insertion into TiDB.
Ensure that you have a TiDB instance running and accessible. Use a MySQL client or command-line tool to connect to your TiDB instance, as TiDB supports the MySQL protocol. Confirm that you have the necessary database and tables set up to store your data.
Convert the processed data into SQL INSERT statements. You can loop through each row of your DataFrame and construct an INSERT statement for each record. Execute these statements using your MySQL client or by utilizing a script with a database connection library like `mysql-connector-python` for Python.
Once the data has been inserted, perform a verification step to ensure data integrity. Query the TiDB tables to check that all records have been accurately transferred and that there are no discrepancies. This step ensures that your data migration was successful and that the data in TiDB reflects what was exported from LinkedIn Ads.
By following these steps, you can effectively move data from LinkedIn Ads to TiDB without the need for third-party connectors or integrations, ensuring full control over your data processing and migration pipeline.
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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