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Begin by logging into your Facebook Ads Manager. Navigate to the "Ads Reporting" section to create a custom report containing the data you need. Set your desired metrics, dimensions, and date range, then export the report as a CSV file. Ensure the file is saved securely on your local machine.
Open the exported CSV file to ensure that all necessary data is present and correctly formatted. Check for any inconsistencies or errors in the data that may cause issues during the import process. Make any necessary adjustments, such as formatting date fields or cleaning up text data.
Log in to your Starburst Galaxy account. If you do not have an account, you will need to create one and set up your environment. Ensure you have the necessary permissions to import data into your desired schema.
In Starburst Galaxy, navigate to the SQL Editor. Create a new table schema that matches the structure of your CSV file. Define the table with appropriate column names and data types corresponding to the data in your CSV file. Execute the SQL command to create the table.
Manually convert the data from your CSV file into SQL `INSERT` statements. This can be done by writing a script or using a spreadsheet formula to format each row of data into a SQL command. Ensure each statement aligns with the table schema you created in Starburst Galaxy.
In the Starburst Galaxy SQL Editor, execute the SQL `INSERT` statements you generated. Depending on the size of your data, you may need to execute several batches of commands. Monitor for any errors during this process and resolve them as needed.
Once the data has been imported, perform a series of checks to ensure that all records have been correctly transferred and match the original dataset from Facebook Marketing. Run simple queries to count records, check for null entries, and verify data accuracy against the original CSV file.
By following these steps, you can manually transfer data from Facebook Marketing to Starburst Galaxy 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.
Facebook Marketing is an extension of Facebook’s online social networking service. Making strategic use of its gigantic user base, Facebook has partnered with AXA Group to leverage the power of people connections (over 1.32 billion active users monthly) for extraordinarily efficient digital marketing and commercial collaboration. Through Facebook’s huge user base, Facebook Marketing is able to reach unprecedented numbers of people with personalized sales and marketing advertisements, making it a huge addition to the world of marketing.
Facebook Marketing's API provides access to a wide range of data that can be used for advertising and marketing purposes. The types of data that can be accessed through the API include:
1. Ad performance data: This includes metrics such as impressions, clicks, conversions, and cost per action.
2. Audience data: This includes information about the demographics, interests, and behaviors of the people who engage with your ads.
3. Campaign data: This includes information about the campaigns you have run, such as budget, targeting, and ad creative.
4. Page data: This includes information about your Facebook Page, such as the number of likes, followers, and engagement metrics.
5. Insights data: This includes data about how people are interacting with your content on Facebook, such as reach, engagement, and video views.
6. Custom audience data: This includes information about the custom audiences you have created, such as their size and composition.
7. Ad account data: This includes information about your ad account, such as billing and payment information.
Overall, the Facebook Marketing API provides a wealth of data that can be used to optimize your advertising campaigns and improve your marketing efforts on the platform.
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:
