How to load data from LinkedIn Ads to BigQuery
Learn how to use Airbyte to synchronize your LinkedIn Ads data into BigQuery within minutes.
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
How to Sync to Manually
Step 1: Access LinkedIn Ads API
To begin, you need to access LinkedIn's Ads API to retrieve your advertising data. First, ensure you have a LinkedIn Developer account. Create an application within the LinkedIn Developer Portal to obtain your Client ID and Client Secret. These credentials will be used to authenticate your requests to LinkedIn's APIs.
Step 2: Authenticate API Access
Use OAuth 2.0 to authenticate your API requests. You'll need to implement a mechanism to obtain an access token. Start by generating an authorization code through a user consent process. Exchange this code for an access token using LinkedIn's token endpoint. This token will allow you to interact with LinkedIn's APIs securely.
Step 3: Extract Data from LinkedIn Ads
Once authenticated, make API calls to LinkedIn Ads endpoints to extract the necessary data. Use the access token to request data such as campaign performance, ad statistics, and other relevant records. Depending on your needs, you might need to call multiple endpoints or paginate through results to gather all required data.
Step 4: Transform and Structure Data
After extracting the data, it's important to transform and structure it to fit your schema in BigQuery. LinkedIn's API response is typically in JSON format, so you may need to parse this data and convert it into a tabular format. Use scripting languages like Python or JavaScript to handle this transformation process.
Step 5: Prepare BigQuery Environment
Set up your BigQuery environment by creating a dataset and defining tables that align with the structure of your transformed data. Ensure that your Google Cloud Platform (GCP) project is active and you have the necessary permissions to create and manage datasets and tables within BigQuery.
Step 6: Load Data into BigQuery
Use Google Cloud's client libraries or command-line tools to load your structured data into BigQuery. If you're using Python, the `google-cloud-bigquery` library can be particularly useful. Convert your transformed data into a CSV or JSON file, and use a script to upload this data into BigQuery tables using the `load_table_from_file()` method.
Step 7: Schedule Regular Data Transfers
To maintain up-to-date data in BigQuery, automate the data extraction and loading process. Schedule regular jobs using cron jobs on a server or Google Cloud Functions to periodically extract new data from LinkedIn Ads API, transform it, and load it into BigQuery. This ensures your data warehouse remains current and reliable for analysis.
---
By following these steps, you can effectively move data from LinkedIn Ads to BigQuery without relying on third-party connectors or integrations.