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 the necessary data from Google Directory. You can use the Google Admin Console to create a data export. Go to the Admin Console, navigate to "Tools" -> "Data Export," and initiate an export. This process might take some time, and you will receive an email with a download link once the export is ready. The data will be in a downloadable format, typically as CSV files.
Once you receive the notification email, download the exported data files. These files will usually be in a compressed format such as ZIP. Extract the files to access the CSV data files that you will work with.
Open the CSV files and review the data to ensure it is clean and formatted correctly for ClickHouse. Check for any inconsistencies or formatting issues in the CSV files. Make any necessary modifications to ensure that the data types and structures align with your ClickHouse schema. Save the files after making these changes.
Ensure that your ClickHouse server is up and running. Install ClickHouse on your server if it is not already installed. You can do this by following the ClickHouse installation documentation for your specific operating system. Once installed, start the ClickHouse server and ensure it is accessible.
Using the ClickHouse client, define the table schema that matches the structure of your CSV data. Connect to your ClickHouse server via the command-line client or a graphical interface, and use the `CREATE TABLE` SQL command to define your table structure. Ensure that the column names and data types align with those in your prepared CSV files.
Use the ClickHouse `INSERT INTO ... FORMAT CSV` command to load your data into the defined table. For each CSV file, run a command like:
```sql
clickhouse-client --query="INSERT INTO your_table_name FORMAT CSV" < /path/to/your/csvfile.csv
```
Replace `your_table_name` with the name of your ClickHouse table and `/path/to/your/csvfile.csv` with the path to your CSV file. Repeat this process for each CSV file you need to import.
After loading the data, perform a series of checks to ensure that the data has been imported correctly. Run queries to count the number of records, check for data consistency, and ensure that no records are missing. Use SQL queries such as `SELECT COUNT(*) FROM your_table_name` and other relevant checks based on your data requirements.
By following these steps, you can successfully move data from Google Directory to a ClickHouse warehouse 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:
