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Begin by exporting the data from Google Directory. Navigate to the Google Admin Console and access the "Directory" section. Use the built-in tools to export the required data, typically in CSV or JSON format. Ensure that you have the necessary permissions to perform the export.
Once exported, review the data to ensure it's formatted correctly for import into Starburst Galaxy. You may need to clean up or transform the data to match the schema requirements of the destination database. Use tools like Excel or a Python script to adjust the data format, ensuring consistency and accuracy.
Log into your Starburst Galaxy account and set up the necessary environment for data import. This involves creating a new catalog and schema if not already existing. Define the tables that will receive the data, ensuring the column types and configurations align with the formatted data from Google Directory.
Upload the formatted data files to a storage location that Starburst Galaxy can access, such as an Amazon S3 bucket or a Google Cloud Storage bucket. Ensure that Starburst Galaxy has the necessary permissions to read from this storage location, typically involving setting up appropriate IAM roles or access keys.
In Starburst Galaxy, create an external table that points to the location of your uploaded data files. Use a SQL query to define the external table, specifying the file format and the data's location. This setup allows Starburst Galaxy to read the data without physically moving it into the database.
Execute a SQL query in Starburst Galaxy to load the data from the external table into the internal table(s) you've set up. This step involves selecting data from the external table and inserting it into the internal table, transforming the data as needed during the process.
After loading the data, run queries to verify the integrity and accuracy of the data in Starburst Galaxy. Check for any discrepancies or errors that may have occurred during the transfer. Once verified, clean up any temporary files or configurations used during the process to maintain a tidy environment.
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:
