Trusted by data-driven companies
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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- 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
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
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."
Begin by accessing the LinkedIn Ads API. You need to create a LinkedIn Developer account and register your application to obtain API credentials, including the Client ID and Client Secret. These credentials will be used to authenticate your API requests. Ensure you have the necessary permissions to access the LinkedIn Ads data.
Use OAuth 2.0 for authentication. Send a request to LinkedIn's authorization endpoint to obtain an authorization code. Exchange this authorization code for an access token by making a request to LinkedIn�s token endpoint. This access token will be used to authenticate your API calls to retrieve LinkedIn Ads data.
With the access token, make API calls to LinkedIn's Ads API endpoints to fetch the required data. Specify the data you need, such as campaign performance metrics, ad creatives, or targeting criteria. Use the appropriate API endpoints and parameters to tailor the data retrieval to your needs. Parse the JSON responses to extract the relevant data.
Once you have fetched the data, format it appropriately for uploading to Amazon S3. Convert the JSON data into a format suitable for storage, such as CSV or Parquet. Ensure the data is clean and structured, with proper headers and consistent data types, to facilitate analysis or further processing.
Log into your AWS Management Console and create a new S3 bucket if you haven�t already. Define the bucket's name and region, and configure its settings, such as access permissions and versioning, based on your requirements. Ensure that the bucket is publicly accessible if needed, or configure proper IAM policies for secure access.
Write a script using a programming language like Python with the Boto3 library (AWS SDK for Python) to automate the upload process. The script should authenticate with AWS using your AWS credentials, specify the target S3 bucket and object key, and upload the formatted data file. Handle any exceptions to ensure a robust upload process.
To maintain an up-to-date dataset, schedule the script to run at regular intervals. Use a tool like cron on Unix-based systems or Task Scheduler on Windows to automate the execution of your script. Ensure the schedule aligns with your reporting needs and consider incorporating logging or notifications to monitor the success of each data transfer.
By following these steps, you can move data from LinkedIn Ads to Amazon S3 efficiently, 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:
