Unlock the power of your data in the cloud! Get hands-on with Google Cloud's core data services like BigQuery and Looker to validate your practical skills in data ingestion, analysis, and management, and earn your Associate Data Practitioner certification!
Gauge your current knowledge
Gauge your current knowledge
Identity and Access Management (IAM) is the primary way to manage security and access control within Google Cloud. It allows administrators to define who (identity) has what access (role) for which resource. By using IAM policies, organizations can ensure that only authorized users or systems can interact with specific cloud resources, such as virtual machines or databases. This system acts as the main gatekeeper for your cloud environment, preventing unauthorized entry and potential data breaches.
To manage permissions effectively, IAM uses the concept of roles, which are collections of specific permissions. Instead of assigning permissions one by one to every user, administrators assign a role to a user, giving them all the permissions associated with that role. There are three main types of roles:
The most important security concept to apply here is the principle of least privilege. This principle states that a user should only have the minimum amount of access required to perform their job functions. For example, a data analyst who only needs to read data from a database should not be given permission to delete that database. By strictly following this principle, organizations reduce the risk of accidental damage or malicious actions within their cloud infrastructure.
When managing data in Cloud Storage, choosing the right access control method is vital for keeping files secure. The recommended method for most use cases is Uniform Bucket-Level Access. This approach allows you to manage permissions for an entire bucket using standard IAM roles, rather than managing permissions for individual files. Using uniform access simplifies security management because it ensures that every object in the bucket follows the same rules, reducing the chance of human error.
In contrast, Access Control Lists (ACLs) offer a legacy method for defining access to specific objects within a bucket. ACLs allow you to grant read or write access to individual files for specific users, which provides very fine-grained control. However, relying on ACLs can make the system complex and difficult to audit as the amount of data grows. Because of this complexity, Google generally advises users to disable ACLs and rely on IAM policies through Uniform Bucket-Level Access whenever possible.
There are also specific scenarios where you might need to share data publicly or temporarily.
Analytics Hub is a data exchange platform built on top of BigQuery that allows organizations to share data and analytics assets securely. It is designed to solve the problem of sharing data across different departments or with external partners without the need to copy files. By using this service, data practitioners can create a central repository where data is treated as a product that can be published and subscribed to.
The platform organizes data sharing through exchanges and listings. An administrator creates an exchange, which acts as a private or public marketplace for data. Within an exchange, publishers create listings that describe the dataset and the terms of use. When a user subscribes to a listing, a linked dataset is added to their project. This linked dataset is read-only and points directly to the source, ensuring the subscriber always sees the most current data.
There are several key benefits to using Analytics Hub for governance and sharing.
In summary, configuring access control and governance involves a combination of managing user identities, securing storage buckets, and efficiently sharing data. By utilizing Identity and Access Management (IAM), administrators can enforce the principle of least privilege, ensuring users only have the permissions they truly need. For Cloud Storage, choosing between Uniform Bucket-Level Access and ACLs determines how granular the security controls are, with uniform access being the safer standard. finally, Analytics Hub provides a modern solution for sharing BigQuery datasets securely without duplicating data, streamlining collaboration across different teams and organizations.