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Begin by logging into your LinkedIn Ads account. Navigate to the Campaign Manager and select the campaign or data set you wish to export. LinkedIn allows you to export campaign data manually. Choose the "Export" option to download the data in a CSV format, which is typically the most convenient format for data manipulation and importation into databases.
Open the exported CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure it is complete and accurate. Clean the data by removing any unnecessary columns, correcting formatting issues, and handling any missing values. This step is crucial to ensure the data is ready for import into PostgreSQL.
Ensure that you have a PostgreSQL database server running. You can set this up on your local machine or a remote server. If needed, install PostgreSQL following the official installation instructions for your operating system. After installation, use the `psql` command-line tool or a GUI like pgAdmin to create a new database and define the necessary tables that correspond to the structure of your LinkedIn Ads data.
Convert your cleaned CSV data into a format suitable for PostgreSQL import. This typically involves ensuring that the data types in your CSV match the data types of your PostgreSQL table columns. Save the adjusted file in a CSV format again, ensuring that the delimiter used in the file matches what PostgreSQL expects (commonly a comma).
Use SQL commands to create a table in your PostgreSQL database that matches the structure of your CSV file. Define each column with the appropriate data type. Here is an example SQL command to create a table:
```sql
CREATE TABLE linkedin_ads_data (
campaign_name TEXT,
impressions INTEGER,
clicks INTEGER,
spend NUMERIC,
date DATE
);
```
Use the `COPY` command in PostgreSQL to import the CSV data into your table. This command reads from the CSV file and inserts the data into the specified table. Here is an example command:
```sql
COPY linkedin_ads_data(campaign_name, impressions, clicks, spend, date)
FROM '/path/to/your/file.csv'
DELIMITER ','
CSV HEADER;
```
Ensure the file path is correct and accessible by the PostgreSQL server.
Once the data has been imported, verify the import by querying the PostgreSQL table. Use a simple `SELECT` statement to ensure that the data appears as expected and there are no discrepancies. Here is an example query:
```sql
SELECT * FROM linkedin_ads_data;
```
Check for errors or issues, and ensure the data integrity is maintained. If there are any issues, you may need to revisit the data cleaning or table setup steps.
By following these steps, you can manually transfer data from LinkedIn Ads to a PostgreSQL database 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: