With Google BigQuery, enterprises can adopt a more proactive approach to data security and management. Among its notable features, Google BigQuery has a “decoupled” serverless architecture that segregates data storage from its computing.

Here are 3 reasons why Snowflake users must migrate to BigQuery to secure their data and other resources:

1. Identity and Access Management (IAM)

   With effective IAM, Google BigQuery facilitates granular access to specific cloud resources. This security feature follows the “least privilege” principle, which does not grant users more permission than what they need.

   With BigQuery, enterprises can configure their IAM policies at various levels of the resource hierarchy – with each resource inheriting the policies of its parent resource.

   With its predefined roles and responsibilities, BigQuery grants permissions at the user, group, and service account levels. Each of these entities can be granted permissions using predefined or custom roles.

   Here are the various BigQuery resource levels where user permissions can be granted (or denied) access:

   • Organizational level – or for individual projects and folders
   • Datasets
   • Connections
   • Database tables or views
   • BigQuery data policies, row access, or policy tags
2. Security through data classification

   Google BigQuery applies security through data classification – along with row-level and column-level security. Through row-level security, BigQuery can restrict data filtering and generate shared views among its users. By applying the “least privilege” principle, enterprises can apply row-level access to a data subset in the database table.

   Through column-level security, BigQuery ensures grained access to critical database columns using policy tags or data types. With this level of security, enterprises can create policies that check the user’s access during the query process.

3. Data loss prevention (DLP)

   For data discovery and loss prevention, Google BigQuery provides a few exceptional services. For instance, by using data catalogs, enterprises can derive and sustain the data lineage. With these services, enterprises can also scan personally identifiable information (PII) and add security tags to BigQuery tables.

   With DLP, Google Cloud users can detect and protect sensitive data using two methods:

   • Sensitive data profiles containing metadata and metrics of database tables can help in identifying high-risk and sensitive data.
   • On-demand inspection of a specific database table or subset of columns, along with reporting the findings of this inspection.
4. Data encryption

   As a managed service of GCP, Google BigQuery offers encryption for data at rest and in transit. BigQuery offers the following types of data encryption:

   • Google-managed encryption automatically encrypts (and decrypts) all data before writing it to the disk and before it’s read by any authorized user. Google manages all the necessary encryption keys for data protection.
   • Customer-managed encryption allows Google customers to manage their encryption keys for Google BigQuery. They can control and manage their keys using Google Cloud’s key management service.