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
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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 the data from your Google Directory. You can achieve this by using Google’s Admin SDK Directory API. First, enable the API in your Google Cloud Platform (GCP) project. Then, write a script using a language like Python to authenticate and call the API to extract user and group data. This data can be exported in JSON or CSV format.
Set up your local or cloud environment to handle the data extraction and transformation. Install necessary dependencies, such as the Google API client for Python and any required libraries for handling JSON or CSV data.
Once you have the data extracted, transform it into a format suitable for Snowflake. For example, if your data is in JSON, convert it to CSV if that's the format you prefer to load into Snowflake. Ensure that the CSV files have the appropriate headers and data types that match the Snowflake table schema.
Before loading data into Snowflake, you need to create a table schema that matches the structure of your transformed data. Use the Snowflake web interface or SnowSQL (Snowflake's command-line client) to create the table. Define the appropriate data types for each column according to the data you exported from Google Directory.
Upload the transformed data to a Snowflake stage. Use SnowSQL to connect to your Snowflake account and employ the `PUT` command to upload the CSV files to an internal stage. This stage acts as a temporary holding area for your data files before they are loaded into the table.
Use the `COPY INTO` command in Snowflake to load the data from the stage into your Snowflake table. This command allows you to specify the source files, the target table, and any necessary file format options, such as field delimiter and NULL handling, to ensure the data is loaded correctly.
Once the data is loaded, perform data integrity checks to ensure that the transfer was successful. Run queries in Snowflake to compare the number of records and key data points with the original data from Google Directory. This verification process ensures that the data has been accurately transferred and is ready for further analysis or use.
By following these steps, you can manually migrate data from Google Directory to a Snowflake destination 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:
