Tag: BigQuery
Automate GCP BigQuery Table View Creation
In this quick demo, we are going to use a Python Script to automate GCP BigQuery Table View Creation for all tables in a dataset in a different GCP project. Before running the script you need to create Application Default Credentials to let Python script use them to authenticate. The user account or service account…
GCP BigQuery Dynamic Data Masking
GCP BigQuery offers dynamic data masking at column level. By using data masking we obscure columns that holds sensitive data for users, groups and service accounts, while still allowing access to data in those columns. What is Data Masking? Data masking is a method of modifying sensitive data while presenting it to an end user…
Search Indexes in BigQuery
Search Indexes in BigQuery lets you easily find unique data elements without having to know the table schemas in advance.By using this feature we can create indexes on log tables and search for specific error codes or identifying the rows of tables that contain a specific user’s PII for GDPR reporting. How to create search…
Automation of BigQuery Table Snapshot Creation
A BigQuery table snapshot preserves the contents of a table at a particular time. You can create a snapshot of a current table, or create a snapshot of a table as it was at any time in the past seven days. A table snapshot can have an expiration. When the configured amount of time has…
Pseudonymization of BigQuery Table Data
What is Pseudonymization? Pseudonymization is a data de-identification procedure to replace personally identifiable information with artificial information.Pseudonymization makes data record less identifiable while allowing data analysis. Pseudonymization allows re-identification of data with additional information, unlike Anonymization procedure. With Pseudonymization, we can mask BigQuery Table data while sharing it with other users by creating BigQuery table…