Google BigQuery provides a robust and versatile security infrastructure that empowers administrators and users to effectively manage access to datasets, tables, views, and other resources. This guide delves into the essential elements of BigQuery's security model and outlines the procedures for managing permissions.

1. Identity and Access Management (IAM) #

Google BigQuery relies on Google Cloud's IAM for access control. IAM enables you to manage access control by defining who (identity) has what access (role) to which resource.

1.1. Identity #

Identities in IAM can represent a service account, a user, a Google group, or even an entire domain. The key concept is that any identity can be granted access permissions to a BigQuery resource.

1.2. Role #

Roles, on the other hand, represent collections of permissions. There are three types of roles in IAM:

  • Primitive roles: Legacy roles like Owner, Editor, or Viewer.
  • Predefined roles: More granular, service-specific roles.
  • Custom roles: Customized roles created by users to fit specific needs.

In the context of BigQuery, several predefined roles such as BigQuery Data Viewer, BigQuery Data Editor, and BigQuery Data Owner are often used.

2. BigQuery Dataset Roles #

BigQuery dataset roles are more granular and apply directly to BigQuery datasets. These roles include:

  • READER: Grants permissions to read data and metadata from a dataset.
  • WRITER: Grants READER role permissions and the ability to append to or overwrite tables in the dataset.
  • OWNER: Grants WRITER role permissions and the ability to manage the dataset.

3. Access Control for Data Manipulation Language (DML) #

Data Manipulation Language (DML) in BigQuery enables users to manipulate data in tables. Security controls for DML operations fall under IAM policies and permissions.

4. Access Control for Data Definition Language (DDL) #

Data Definition Language (DDL) is used to manage tables and schemas within BigQuery. Similar to DML, DDL operations' security controls also come under IAM policies and permissions.

5. Fine-grained Access Control #

BigQuery supports fine-grained access control at the row and column level with BigQuery Column-level security and BigQuery Policy Tags.

5.1. Column-level Security #

Column-level security allows you to secure sensitive data in a table by controlling access to its individual columns.

5.2. BigQuery Policy Tags #

Policy tags enable you to manage access control at a column level by classifying data into various categories.

6. Data Encryption #

BigQuery automatically encrypts all data at rest and in transit, further enhancing its security stance.

6.1. Data Encryption at Rest #

BigQuery manages and implements encryption for data at rest without requiring any action from the user.

6.2. Data Encryption in Transit #

Data in transit between BigQuery and client applications is encrypted using HTTPS.

7. Audit Logs #

BigQuery integrates with Cloud Audit Logs to keep track of activity and data access in your BigQuery resources.

By understanding and leveraging these security features, you can effectively manage access to your BigQuery resources, ensuring your data remains secure and compliant.

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