Exam Professional Cloud Architect topic 1 question 146 discussion

This is pretty simple. Time to transfer using Transfer Appliance: 1-3 weeks (I've used it twice and had a 2-3 week turnaround total) Time to transfer using 1Gbps : 30 hours (https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets) Answer is D, using gsutil

No perfect answer as B and D both have flaws. B is time latency as transfer appliance usually takes weeks; D gsutil applies for less than 1TB. The answer should be storage transfer service for on-premises data, which is not available here. If have to choose one I go for B

B is correct. Search at Gemini it explains in detail why so.

Answer A/C : very to setup and does not improve network limitation Answer D: Interesting for PB, not TB... and minimum 2 weeks after purchase to get the appliance (see https://cloud.google.com/blog/products/storage-data-transfer/transfer-appliances-for-simple-secure-performant-data-movement) Answer D is the best : compress would reduce the size, and even with a 1Gbps network, the transfert would take less 3 hours at full speed. Furthermore -m option use multithread.

B and D sounds feasible but gsutil is not recommended any more so definitely B. https://cloud.google.com/storage/docs/gsutil However, I'm not sure if D is replaced with gcloud storage cp.

D says compress data, ¿in a single file? it will be more than the limit 5 TB of gsutil, so it is B.

I think the answer is B. The main consideration is between B and D. Just thinking if they want the answer to be online transfer, they should have added Online Transfer Service instead of gsutils. Just guessing Google must want us to choose B :)

B fo sho

D will be most cost effective where as B will incur cost (question asking to consider cost effective solution as well) so D is my answer

Option B (Data Transfer appliance) is the best choice for efficient and cost-effective data migration while minimizing database load and transfer time. This solution bypasses network limitations and reduces the impact on the on-premises environment, making it ideal for migrating large data sets to the cloud.

Compressing the data and uploading it with gsutil -m can be a good optimization for your transfer, but it has limitations to consider: Compression Overhead: While compressing the data can reduce upload size and potentially speed up transfer, the compression and decompression processes themselves take time and resources. Depending on your data type, the benefit of reduced size might be offset by the processing overhead. Transfer Appliance: The recommended approach with the Transfer Appliance already utilizes parallel transfers for faster uploads, potentially making gsutil -m less impactful. I will go with B

Compressing the data and uploading it with gsutil -m can be a good optimization for your transfer, but it has limitations to consider: Compression Overhead: While compressing the data can reduce upload size and potentially speed up transfer, the compression and decompression processes themselves take time and resources. Depending on your data type, the benefit of reduced size might be offset by the processing overhead. Transfer Appliance: The recommended approach with the Transfer Appliance already utilizes parallel transfers for faster uploads, potentially making gsutil -m less impactful. I will go with B

with 1 Gbps it will take only 30 hrs so best option is D

Option B and D are most feasible options Option B will be okay if the size of the data is too huge Option D will be good for a few TBs of data. I am assuming 10 TB will fit in this case. https://cloud.google.com/blog/topics/developers-practitioners/how-transfer-your-data-google-cloud

Answer B. Cp limit is 5 TB max

I chose D. According to the link below, 10TB of data can be transferred in 30h. The light blue area is the acceptable line for online transfer. https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets?hl=ja#online_versus_offline_transfer

https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets