Summarize this article with:
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 exporting your data from Google Directory. Use Google Takeout to select the specific data you wish to export. Choose the relevant Google services like Google Contacts, Google Drive, or any other data source available in Google Directory. Ensure the data is exported in a compatible format, such as CSV, JSON, or XML, which AWS services can process.
Once the data export is complete, download the data files to your local machine. Organize the files and ensure they are in a structured format ready for upload. If necessary, clean the data to remove any inconsistencies or errors that might complicate later processing.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket or choose an existing one to store your data. Configure the bucket with appropriate permissions to ensure secure access. If you want the data to be accessible to AWS Glue, ensure your IAM roles are properly set up to grant necessary permissions.
Upload the prepared data files to your configured S3 bucket. You can use the AWS Management Console, AWS CLI, or AWS SDKs to perform the upload. Make sure the files are placed in a specified folder or prefix within the bucket for organized access.
In your AWS Management Console, navigate to AWS Glue. Create a new Glue Data Catalog database to store metadata for your dataset. Define a new Glue crawler that will scan your data in S3 and populate the Glue Catalog with table definitions. Configure the crawler's IAM role to ensure it has permission to access the S3 bucket.
Execute the Glue crawler to automatically detect the schema of your data files in the S3 bucket. The crawler will create tables in the Glue Data Catalog, mapping the structure of the data files to Glue tables. Once the crawler completes, verify that the tables are correctly defined and ready for use in data processing tasks.
With your data cataloged, you can now create AWS Glue ETL (Extract, Transform, Load) jobs. Use the Glue Studio or Glue Console to develop scripts that transform and load your data according to your specific requirements. Once your ETL job is configured, run the job to process and move data within AWS infrastructure as needed.
By following these steps, you'll successfully transfer data from Google Directory to AWS S3 Glue using AWS-native tools and features, 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.
Google (Workspace) Directory is, simply put, a user management system for Google Workspace. It allows IT admins to manage users’ access, facilitates and governs user sign-ons, and, ultimately, is meant to enable users to sign in to multiple Google services through one Google identity. Other features include the ability to monitor devices connected to a business’s domain, manage organizations’ structures, audit applications to which users have approved access, and revoke unauthorized apps.
Google Directory's API provides access to a wide range of data related to the Google Directory service. The API allows developers to retrieve information about various categories of data, including:
- Directory listings: Information about businesses, organizations, and other entities listed in the Google Directory.
- Categories: The different categories and subcategories used to organize listings in the directory.
- Reviews and ratings: User-generated reviews and ratings for businesses and other entities listed in the directory.
- Contact information: Phone numbers, addresses, and other contact information for businesses and organizations listed in the directory.
- Images and videos: Images and videos associated with listings in the directory.
- User profiles: Information about users who have contributed reviews and ratings to the directory.
Overall, the Google Directory API provides developers with a wealth of data that can be used to build applications and services that leverage the information contained in the directory.
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:
