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Begin by accessing your LinkedIn Ads account and navigating to the Campaign Manager. Use the reporting feature to generate a report with the desired data from your ad campaigns. Ensure you select the appropriate metrics and dimensions needed for analysis. Export this report in a CSV format to your local machine for further processing.
Open the downloaded CSV file using a spreadsheet application or a programming language like Python. Review the data to ensure it’s complete and clean any inconsistencies or errors. Ensure all necessary columns are present and properly formatted, as this will help in the transformation process.
If using Python, set up a local environment with necessary libraries like pandas for data manipulation. Install these libraries using a package manager like pip. This environment will facilitate transforming your CSV data into a format suitable for Firebolt.
Use your chosen programming language to transform the data into the schema required by Firebolt. This involves renaming columns, changing data types, and ensuring data consistency. Use scripts to automate this process, ensuring the transformed data aligns with Firebolt's data model.
Firebolt requires a secure connection for data uploads. Use Firebolt’s SQL interface to connect directly from your local machine. Authenticate using your Firebolt credentials, ensuring you have the necessary permissions to create tables and upload data.
Before uploading, define the table structure in Firebolt that will hold your LinkedIn Ads data. Use Firebolt’s SQL editor to create tables with columns matching your transformed dataset. Specify data types and any indexing options that optimize query performance.
Finally, upload your transformed CSV data into Firebolt using the COPY INTO command available in Firebolt. This command allows you to load data directly from your local machine into the specified table in Firebolt. Ensure the data load is successful by running a few queries to verify the accuracy and completeness of the data.
By following these steps, you can efficiently transfer your LinkedIn Ads data to Firebolt 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: