Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

To minimize the cost of storing and retrieving objects in a Cloud Storage bucket while ensuring that cost optimization efforts are transparent to the users and applications, enabling Autoclass is the best approach. Here’s why:

Autoclass Feature:

Autoclassautomatically transitions objects between different storage classes (Standard, Nearline, Coldline, and Archive) based on their access patterns.

It ensures that frequently accessed data is kept in lower-latency, higher-cost storage classes and infrequently accessed data is moved to higher-latency, lower-cost storage classes.

Cost Optimization:

Autoclass optimizes storage costs by automatically moving objects to the most cost-effective storage class based on actual usage patterns, without manual intervention.

This feature ensures that objects are stored in the most economical class appropriate for their access frequency, reducing storage costs over time.

Transparency to Users:

The transition of objects between storage classes is handled automatically by Cloud Storage, making the process transparent to users and applications.

Users and applications interact with the objects in the same way, regardless of the underlying storage class, ensuring seamless access.

Steps to Implement:

Create a Cloud Storage Bucket:

When creating a new Cloud Storage bucket, enable the Autoclass feature.

Configure Autoclass:

Autoclass configuration is typically a straightforward process in the Google Cloud Console, where you enable it during bucket creation.

Monitor and Adjust:

Monitor the storage and access patterns through the Google Cloud Console to ensure that Autoclass is optimizing costs as expected.

Reference Links:

Google Cloud Storage Autoclass

Optimizing Storage Costs with Autoclass

BigQuery Omni enables you to run BigQuery analytics directly on data stored in AWS S3 buckets without having to move or copy the data. This provides several benefits:

Reduced Data Movement Costs:Eliminates the need to egress data from AWS, potentially saving significant costs.

Real-Time Analytics:Allows you to query data in AWS S3 in real-time, providing up-to-date insights.

Simplified Architecture:Reduces the complexity of managing data pipelines and ETL processes.

Here's a breakdown of the steps involved in using BigQuery Omni:

Set up a BigQuery Omni connection:This involves configuring the connection between your Google Cloud project and your AWS S3 bucket. This connection establishes the secure link for BigQuery to access the data in AWS S3.

Create external tables:BigQuery external tables are a way to query data residing in external storage systems, such as AWS S3, without having to import the data into BigQuery. This enables you to directly query the data in its original location.

Query the data using BigQuery:Once the external tables are created, you can use standard SQL queries to analyze the data stored in both Cloud Storage and AWS S3, just as if it were native BigQuery data.

Why other options are not suitable:

Option A:BigLake tables are designed for storing large volumes of structured data within BigQuery itself, not for directly querying data in external storage systems.

Option C and D:While the Storage Transfer Service is useful for moving data between cloud providers, it introduces unnecessary data movement and latency if the goal is to simply query the data in its original location.

Key Points:

BigQuery Omni extends BigQuery's capabilities to analyze data stored in other cloud providers, such as AWS.

External tables provide a way to query data in external storage systems without having to import it into BigQuery.

By leveraging BigQuery Omni and external tables, you can efficiently and cost-effectively query data stored in multiple cloud environments using a single tool, BigQuery.