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You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat
a. What should you do?
Choose 2 answers
Suggested Answer: D, E
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29 comments · newest first
JO
Josefa
6 months ago
Definitely! D is a must for handling more users efficiently.
GR
Gracia
7 months ago
I feel like E is essential. More nodes mean better throughput.
RE
Rebbecca
7 months ago
I’m leaning towards B and C. Flattening might optimize writes.
CH
Chaya
7 months ago
Agreed! D will scale the pipeline, and E boosts Bigtable performance.
AL
Alberto
7 months ago
Definitely need to increase maxNumWorkers for better throughput!
MA
Maynard
7 months ago
Wait, why would local execution help? That sounds counterproductive.
CL
Claribel
8 months ago
I think using the CoGroupByKey transform is a good idea too.
LA
Latrice
8 months ago
Totally agree, more nodes means better handling of concurrent users!
ED
Edward
8 months ago
Increasing the number of nodes in the Bigtable cluster can help with performance.
NG
Ngoc
8 months ago
E) is a must, but I'd also try B) just for fun. Flatten that data!
WI
Willard
9 months ago
A) and D) - local execution and more workers? What is this, amateur hour?
VA
Vallie
9 months ago
I'd go with C) and E). CoGroupByKey and more Bigtable nodes will make this pipeline fly!
DA
Danica
9 months ago
D) and E) are the way to go. Increase the workers and the Bigtable cluster size.
DE
Denny
9 months ago
B) and E) should do the trick. Flatten and more Bigtable nodes should help with the slow updates.
JE
Jeff
9 months ago
I’m a bit confused about the Flatten transform; I thought it was more for restructuring data rather than improving write performance. Not sure if option B is the right call.
MA
Marge
9 months ago
I practiced a similar question about optimizing data pipelines, and I feel like increasing the Bigtable cluster nodes could definitely improve write speeds. So, option E seems plausible.
KE
Kenneth
10 months ago
I'm not entirely sure, but I think using the CoGroupByKey transform could help with aggregating data more efficiently. That might be option C?
RE
Regenia
10 months ago
I'm a bit confused by the Flatten transform option. That doesn't seem directly related to the performance problem. I'm going to go with increasing the Dataflow workers and the Bigtable cluster size - those seem like the most straightforward ways to scale up the system.
AN
Antonio
10 months ago
I remember something about increasing the number of workers in Dataflow to handle more concurrent users, so maybe option D is a good choice.
MO
Mollie
10 months ago
Okay, let's think this through. Configuring local execution for Dataflow probably won't help with the Bigtable performance issues. I'm leaning towards the Bigtable cluster size increase and using CoGroupByKey to optimize the data writes.
RE
Remona
11 months ago
But B could complicate the pipeline. D and E seem safer.
GA
Gabriele
11 months ago
I think D and E are the best options. More workers and nodes can really help.
AN
Angella
11 months ago
Hmm, the question is asking for two answers, so I'll need to pick carefully. I think the CoGroupByKey transform and increasing the Bigtable cluster size are the best options to try.
CA
Camellia
11 months ago
I'm not sure about the Flatten transform, but increasing the number of Dataflow workers and Bigtable nodes sounds like a good way to scale up the pipeline and handle more concurrent users.
MI
Mike
6 months ago
Combining both strategies could really optimize the pipeline!
CH
Chandra
6 months ago
Increasing Bigtable nodes seems crucial for performance.
CO
Corrinne
6 months ago
I think it simplifies the data structure, but not sure if it's necessary.
KA
Kaycee
6 months ago
What about the Flatten transform? Is it useful?
LO
Lorean
10 months ago
I agree, more workers can definitely help.