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Begin by logging into your LinkedIn Ads account. Navigate to the campaign manager where you can access your ad data. Use the reporting feature to export the necessary ad data. Typically, LinkedIn allows you to download this data in CSV format. Ensure you select the relevant fields such as campaign performance, click-through rates, impressions, etc.
Once you have downloaded the CSV file, open it using a spreadsheet application like Microsoft Excel or Google Sheets. Check the data for any inconsistencies or errors, and clean it to ensure that it's ready for import. This includes removing any unnecessary columns, correcting data formats, and ensuring all necessary fields are present.
Typesense requires data in JSON format. Use a scripting language like Python to transform your CSV data into JSON. You can write a Python script that reads the CSV file and converts each row into a JSON object. Ensure that each JSON object aligns with the schema you plan to use in Typesense.
If you haven’t already, install and set up a Typesense server. Typesense can be run locally or on a cloud server. Follow the Typesense documentation to configure your server, and ensure it's running and accessible. You will need to set up an API key for secure access.
Before importing data, you need to define a schema in Typesense that matches the structure of your JSON data. This involves specifying fields, field types, and any indexing options you need. Use the Typesense management API to create a collection with this schema.
Develop a script in Python or another language of your choice that will read the JSON data and use the Typesense API to import it into your collection. Your script should handle authentication using the Typesense API key, and it should include error handling to deal with any issues that arise during the import process.
After importing the data, perform checks to ensure that all data has been imported correctly. Use the Typesense API to query your data and verify that it matches what you expect. Additionally, test the search functionality to ensure that your data can be queried and that the search results are accurate and performant.
By following these steps, you can effectively transfer data from LinkedIn Ads to Typesense 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?
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