Associate Data Practitioner

Determine the appropriate Cloud Storage classes based on the frequency of data access and retention requirements

Google Cloud provides four main storage classes to help manage cost and performance. Storage classes are categories that match how often you need to access data and how long you must keep it. By choosing the right class, you can balance access speed and storage cost without wasting resources.

The Standard class is for data you read and write often, such as active web content or frequently updated logs. It offers high throughput and low latency, making it ideal for real-time analytics or serving files directly to users. However, it has a higher price per gigabyte compared to cooler classes.

The Nearline, Coldline, and Archive classes cost less but have higher access fees. They are designed for data you access infrequently:

• Nearline: For data accessed about once a month.
• Coldline: For data accessed about once a quarter.
• Archive: For data accessed once a year or less.

To pick the best class, first analyze how often your team retrieves or updates data. Then match that frequency and your retention period to avoid unnecessary costs. Proper analysis ensures you get the performance you need without paying too much.

Configure rules to delete objects after a specified period to automatically remove unnecessary data and reduce storage expenses (e.g., BigQuery, Cloud Storage)

Lifecycle management in Google Cloud lets you set automatic rules to manage object deletion or movement. These rules run in the background and apply to buckets, tables, or datasets. By using lifecycle rules, you can ensure that old or unused data does not linger and rack up costs.

In Cloud Storage, you define a lifecycle policy with conditions and actions. Conditions might include object age, creation date, or storage class. Actions can be to delete the object or to transition it to a cheaper class. For example:

• Delete objects older than 365 days.
• Move objects to Coldline after 90 days.

In BigQuery, you can set table expiration timestamps to drop tables or partitions automatically. This helps manage query costs and storage fees for datasets you no longer need. Together, these lifecycle rules help teams keep storage tidy and cost-efficient.

Evaluate Google Cloud services for archiving data given business requirements

When you need to archive data, Google Cloud offers services that balance long-term storage needs and cost. Cloud Storage Archive is the lowest-cost option for data accessed very rarely. You can also choose Coldline or Nearline if you expect occasional retrievals.

Another choice is BigQuery long-term storage, which charges lower rates after a table is unchanged for 90 days. This is useful when you want to archive analytics data but still run occasional queries without moving data. Each service has trade-offs in access fees, retrieval time, and integration with other tools.

To pick the right archiving service, consider:

• How often you will access the archived data
• How quickly you need to restore it
• What compliance or retention rules apply

By matching these requirements to service features, you can create an archiving solution that meets both business goals and budget constraints.

Conclusion

Configuring lifecycle management in Google Cloud starts with choosing the right storage class based on how often you access data and how long you must keep it. Implementing automatic deletion or transition rules helps remove unnecessary objects and move them to cheaper classes, reducing wasteful spending.

Evaluating archiving services like Cloud Storage Archive, Coldline, and BigQuery long-term storage ensures your older data stays safe and accessible in the most cost-effective way. Together, these practices give you a strong foundation for managing data over its entire lifespan.