An enterprise is planning to migrate three distinct data workloads to Google Cloud to support an analytics initiative: Workload 1: 250 TB of historical video archives stored on an on-premises NFS file server. The facility has an outbound internet uplink of only 20 Mbps, and acquiring additional bandwidth is cost-prohibitive. Workload 2: 45 TB of raw image assets residing in an Amazon S3 bucket that must be transferred online to a Google Cloud Storage bucket on a recurring schedule. Workload 3: Ongoing customer relationship data stored in Salesforce that needs to be automatically loaded directly into BigQuery on a daily schedule without custom pipeline code. Which combination of Google Cloud services should you recommend for these workloads?

Workload 1: Transfer Appliance; Workload 2: Storage Transfer Service; Workload 3: BigQuery Data Transfer Service: ### What the Recommended Services Are This architecture pairs Google Cloud's purpose-built transfer solutions to their ideal data sources, locations, network constraints, and destinations: - **Transfer Appliance**: A high-capacity physical storage device shipped to a customer's data center to perform offline data uploads directly to Cloud Storage. - **Storage Transfer Service**: A fully managed cloud service that automates large-scale online object and file movement from cloud providers (like Amazon S3 and Azure Blob) and on-premises storage to Cloud Storage. - **BigQuery Data Transfer Service (DTS)**: A managed service that automates scheduled data movement from Software-as-a-Service (SaaS) applications, supported databases, and external cloud storage directly into BigQuery. ### How This Solution Meets Each Requirement - **Workload 1 (Transfer Appliance)**: Transferring 250 TB over an unupgraded 20 Mbps connection would take several years to complete online. Transfer Appliance enables an offline migration that bypasses the bandwidth bottleneck securely and cost-effectively. - **Workload 2 (Storage Transfer Service)**: Storage Transfer Service provides scalable, managed cloud-to-cloud data ingestion from Amazon S3 directly to Cloud Storage without requiring intermediary compute infrastructure. - **Workload 3 (BigQuery Data Transfer Service)**: DTS has native, code-free SaaS connectors (including Salesforce) to ingest and schedule daily updates straight into BigQuery destination tables. ### Key Advantages - **Optimized Network Utilization**: Prevents network saturation on slow on-premises links while leveraging cloud provider backbones for cloud-to-cloud transfers. - **Operational Efficiency**: Eliminates the need to maintain custom scripts, virtual machines, or ETL pipelines for third-party cloud and SaaS synchronization. - **Built-in Resilience**: Storage Transfer Service and BigQuery Data Transfer Service feature automatic retries, scheduling, and error reporting. ### Why This Is Optimal Choosing the right migration tool based on data volume, bandwidth availability, source system, and target platform delivers the lowest operational cost and quickest time to deployment.

StudyCerts

Data Migration Tools Selection Transfer Scenarios | Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam! | StudyCerts

Professional Cloud Architect

Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!

Intermediate

Prepare and test your skills

Track Readiness

Set Exam Goal

Intermediate

Prepare and test your skills

Track Readiness

Set Exam Goal

Data Migration Tools Selection Transfer Scenarios

Section: Data and system migration and management tooling

Worked example. The correct answer is already marked and every option is explained below, so there is nothing to select here. To answer questions yourself, start the free trial.

An enterprise is planning to migrate three distinct data workloads to Google Cloud to support an analytics initiative:

Workload 1: 250 TB of historical video archives stored on an on-premises NFS file server. The facility has an outbound internet uplink of only 20 Mbps, and acquiring additional bandwidth is cost-prohibitive.
Workload 2: 45 TB of raw image assets residing in an Amazon S3 bucket that must be transferred online to a Google Cloud Storage bucket on a recurring schedule.
Workload 3: Ongoing customer relationship data stored in Salesforce that needs to be automatically loaded directly into BigQuery on a daily schedule without custom pipeline code.

Which combination of Google Cloud services should you recommend for these workloads?

Workload 1: Transfer Appliance; Workload 2: BigQuery Data Transfer Service; Workload 3: Storage Transfer Service

Workload 1: Google Cloud CLI (gcloud storage); Workload 2: Storage Transfer Service; Workload 3: Cloud Data Fusion

Workload 1: Storage Transfer Service; Workload 2: Transfer Appliance; Workload 3: Dataproc

Workload 1: Transfer Appliance; Workload 2: Storage Transfer Service; Workload 3: BigQuery Data Transfer Service

Professional Cloud Architect

Data Migration Tools Selection Transfer Scenarios

Related questions