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You have an existing weekly Storage Transfer Service transfer job from Amazon S3 to a Nearline Cloud Storage bucket in Google Cloud. Each week, the job moves a large number of relatively small files. As the number of files to be transferred each week has grown over time, you are at risk of no longer completing the transfer in the allocated time frame. You need to decrease the total transfer time by replacing the process. Your solution should minimize costs where possible. What should you do?
Suggested Answer: B
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Why B is correct:Creating parallel transfer jobs by using include and exclude prefixes allows you to split the data into smaller chunks and transfer them in parallel.
This can significantly increase throughput and reduce the overall transfer time.
Why other options are incorrect:A: Changing the storage class to Standard will not improve transfer speed.
C: Dataflow is a complex solution for a simple file transfer task.
D: Agent-based transfer is suitable for large files or network limitations, but not for a large number of small files.
34 comments · newest first
JE
Jerrod
10 months ago
Wait, can you really use multiple agents on Compute Engine? Sounds complicated!
RE
Rebecka
10 months ago
Definitely agree, option B seems like the best choice here.
LE
Lenny
11 months ago
Batch Dataflow jobs are cool, but are they really cost-effective?
MA
Marci
11 months ago
Standard storage class might not be the best for this scenario.
RU
Rusty
11 months ago
I heard parallel transfer jobs can really speed things up!
EL
Elin
11 months ago
I recall that using the Google Cloud CLI might not be the best for large transfers. Option A seems less effective compared to the others, but I’m not completely sure why.
AJ
Aja
11 months ago
I’m leaning towards option D with the agent-based transfer job. It seems like using multiple agents could really speed things up, but I’m not confident about the cost implications.
VI
Viola
12 months ago
I think option C, the Dataflow job, could be a good choice since it can handle large data transfers efficiently. We practiced a similar scenario, but I’m a bit unclear on the scheduling part.
ME
Merri
12 months ago
I remember we discussed the importance of parallel processing in our last study session. Option B sounds like it could help with the transfer time, but I'm not entirely sure how to set up the include and exclude prefixes correctly.
DU
Dusti
1 year ago
This seems straightforward. I'd go with the Dataflow job option - it should be able to handle the large number of files and provide a reliable, scheduled transfer process.
RI
Ricarda
1 year ago
Okay, I think I have a strategy here. Creating parallel transfer jobs with include and exclude prefixes could help speed up the process while minimizing costs. I'll make sure to consider the other options as well.
AZ
Azalee
1 year ago
Hmm, I'm a bit confused by the question. I'll need to review the details on the different storage classes and transfer options to determine the best approach.
AS
Asha
1 year ago
This looks like a tricky one. I'll need to think through the different options carefully to find the most efficient and cost-effective solution.
RU
Ruthann
1 year ago
A for 'Ah, the good old days when we had to do everything manually.' B for 'Bam, let's get this done with parallel power!'
BE
Becky
1 year ago
Just throw more servers at it, that'll fix it! Or, you know, use option D and let the machines do the work.
DE
Deangelo
1 year ago
I agree, let's go with option D and let the machines handle the heavy lifting.
GR
Gregoria
1 year ago
Throwing more servers at it might work temporarily, but option D is a more efficient and cost-effective solution in the long run.
EL
Elliot
1 year ago
Yeah, using multiple transfer agents on Compute Engine instances should definitely help decrease the total transfer time.
ED
Edison
1 year ago
Option D sounds like the best solution to speed up the transfer process.
VI
Viki
1 year ago
I'm leaning towards C. Dataflow seems like the right tool to handle this kind of large-scale data migration in a scalable way.
BE
Benedict
1 year ago
A) Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the ---custom-storage-class flag.
AD
Adela
1 year ago
B) Create parallel transfer jobs using include and exclude prefixes.
JA
Janey
1 year ago
C) Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.
JO
Jolene
1 year ago
I'm leaning towards option C with a batch Dataflow job for a more automated solution.
RO
Rosann
1 year ago
Option D seems intriguing, but I'm not sure if the added cost of Compute Engine instances is worth it. Might be overkill for this use case.
GE
Gerald
1 year ago
C) Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.
TE
Tess
1 year ago
B) Create parallel transfer jobs using include and exclude prefixes.
ME
Merilyn
1 year ago
A) Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the ---custom-storage-class flag.
ME
Melodie
1 year ago
I disagree, I believe option D with multiple transfer agents would be more efficient.
KA
Karima
1 year ago
I think option B is the way to go. Using parallel transfer jobs with prefixes sounds like the most efficient way to get this done.
JU
Judy
1 year ago
D) Create an agent-based transfer job that utilizes multiple transfer agents on Compute Engine instances.
CA
Carin
1 year ago
I agree, that seems like the best way to speed up the transfer process.
SH
Sheridan
1 year ago
B) Create parallel transfer jobs using include and exclude prefixes.
MA
Maybelle
1 year ago
I think option B sounds like a good idea to speed up the transfer.