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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.
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.
For huge analytical tables, Apache Iceberg is a high-performance format. Using Apache Iceberg, engines such as Spark, Trino, Flink, Presto, Hive and Impala can safely work with the same tables, at the same time, providing the reliability and simplicity of SQL tables to big data. With Apache Iceberg, you can merge new data, update existing rows, and delete specific rows. Data files can be eagerly rewritten or deleted deltas can be used to make updates faster.
1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Add Source" button and select "LinkedIn Ads" from the list of available connectors.
3. Enter a name for the connector and click "Next".
4. Enter your LinkedIn Ads credentials, including your LinkedIn Ads account ID, access token, and secret key.
5. Click "Test Connection" to ensure that the credentials are correct and the connection is successful.
6. Once the connection is successful, click "Create" to save the connector.
7. You can now use the LinkedIn Ads connector to create a new pipeline or add it to an existing one.
8. To create a new pipeline, navigate to the "Pipelines" tab and click "Create Pipeline".
9. Select the LinkedIn Ads connector as the source and choose your destination connector.
10. Follow the prompts to configure the pipeline and map the data fields.
11. Once the pipeline is configured, click "Create" to start syncing data from LinkedIn Ads to your destination.
1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Click on the "Apache Iceberg" destination connector and select "Create new connection."
3. Enter a name for your connection and provide the necessary credentials for your Apache Iceberg database, including the host, port, database name, username, and password.
4. Test the connection to ensure that it is successful. 5. Select the tables or data sources that you want to replicate to your Apache Iceberg database.
6. Configure any additional settings or options for your connection, such as the frequency of data replication or any transformations that you want to apply to your data.
7. Save your connection and start the replication process.
8. Monitor the progress of your data replication and troubleshoot any issues that may arise.
9. Once the replication process is complete, verify that your data has been successfully replicated to your Apache Iceberg database.
10. Use your Apache Iceberg database to analyze and query your data as needed.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
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 should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: