Databases
Marketing Analytics

How to load data from LinkedIn Ads to Kafka

Learn how to use Airbyte to synchronize your LinkedIn Ads data into Kafka within minutes.

TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up LinkedIn Ads as a source connector (using Auth, or usually an API key)
  2. set up Kafka as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is LinkedIn Ads

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.

What is Kafka

A communication solutions agency, Kafka is a cloud-based / on-prem distributed system offering social media services, public relations, and events. For event streaming, three main functionalities are available: the ability to (1) subscribe to (read) and publish (write) streams of events, (2) store streams of events indefinitely, durably, and reliably, and (3) process streams of events in either real-time or retrospectively. Kafka offers these capabilities in a secure, highly scalable, and elastic manner.

Integrate LinkedIn Ads with Kafka in minutes

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Prerequisites

  1. A LinkedIn Ads account to transfer your customer data automatically from.
  2. A Kafka account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including LinkedIn Ads and Kafka, for seamless data migration.

When using Airbyte to move data from LinkedIn Ads to Kafka, it extracts data from LinkedIn Ads using the source connector, converts it into a format Kafka can ingest using the provided schema, and then loads it into Kafka via the destination connector. This allows businesses to leverage their LinkedIn Ads data for advanced analytics and insights within Kafka, simplifying the ETL process and saving significant time and resources.

Step 1: Set up LinkedIn Ads as a source connector

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.

Step 2: Set up Kafka as a destination connector

1. First, you need to have an Apache Kafka destination connector installed on your system. If you don't have it, you can download it from the Apache Kafka website.  
2. Once you have the Apache Kafka destination connector installed, you need to create a new connection in Airbyte. To do this, go to the Connections tab and click on the "New Connection" button.  3. In the "New Connection" window, select "Apache Kafka" as the destination connector and enter the required connection details, such as the Kafka broker URL, topic name, and authentication credentials.  
4. After entering the connection details, click on the "Test Connection" button to ensure that the connection is working properly.  
5. If the connection test is successful, click on the "Save" button to save the connection.  
6. Once the connection is saved, you can create a new pipeline in Airbyte and select the Apache Kafka destination connector as the destination for your data.  
7. In the pipeline configuration, select the connection you created in step 3 as the destination connection.  
8. Configure the pipeline to map the source data to the appropriate Kafka topic and fields.  
9. Once the pipeline is configured, you can run it to start sending data to your Apache Kafka destination.

Step 3: Set up a connection to sync your LinkedIn Ads data to Kafka

Once you've successfully connected LinkedIn Ads as a data source and Kafka as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select LinkedIn Ads from the dropdown list of your configured sources.
  3. Select your destination: Choose Kafka from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific LinkedIn Ads objects you want to import data from towards Kafka. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from LinkedIn Ads to Kafka according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Kafka data warehouse is always up-to-date with your LinkedIn Ads data.

Use Cases to transfer your LinkedIn Ads data to Kafka

Integrating data from LinkedIn Ads to Kafka provides several benefits. Here are a few use cases:

  1. Advanced Analytics: Kafka’s powerful data processing capabilities enable you to perform complex queries and data analysis on your LinkedIn Ads data, extracting insights that wouldn't be possible within LinkedIn Ads alone.
  2. Data Consolidation: If you're using multiple other sources along with LinkedIn Ads, syncing to Kafka allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: LinkedIn Ads has limits on historical data. Syncing data to Kafka allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: Kafka provides robust data security features. Syncing LinkedIn Ads data to Kafka ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: Kafka can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding LinkedIn Ads data.
  6. Data Science and Machine Learning: By having LinkedIn Ads data in Kafka, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While LinkedIn Ads provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Kafka, providing more advanced business intelligence options. If you have a LinkedIn Ads table that needs to be converted to a Kafka table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a LinkedIn Ads account as an Airbyte data source connector.
  2. Configure Kafka as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from LinkedIn Ads to Kafka after you set a schedule

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:

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Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
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Connectors Used

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Connectors Used

Frequently Asked Questions

What data can you extract from LinkedIn Ads?

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 data can you transfer to Kafka?

You can transfer a wide variety of data to Kafka. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from LinkedIn Ads to Kafka?

The most prominent ETL tools to transfer data from LinkedIn Ads to Kafka include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from LinkedIn Ads and various sources (APIs, databases, and more), transforming it efficiently, and loading it into Kafka and other databases, data warehouses and data lakes, enhancing data management capabilities.