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Google Professional Data Engineer Exam - Topic 3 Question 83 Discussion

You need to modernize your existing on-premises data strategy. Your organization currently uses.* Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.* Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?
C) Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.
A) Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases Convert your ETL pipelines to Dataflow.
B) Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases Orchestrate your pipelines with Cloud Composer.
D) Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer..

Google Professional Data Engineer Exam - Topic 3 Question 83 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 83
Topic #: 3
[All Professional Data Engineer Questions]

You need to modernize your existing on-premises data strategy. Your organization currently uses.

* Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.

* Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.

You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: C

by Benton at May 12, 2024, 01:23 PM

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Herschel
9 months ago
I didn't know Dataproc could handle all that!
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Jina
9 months ago
Wait, can we really just switch to Cloud Composer without issues?
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Marlon
10 months ago
D seems like the most straightforward option!
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Edda
10 months ago
I think B is better for large workloads, though.
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Amira
10 months ago
A sounds solid for migrating Hadoop to the cloud.
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Ona
10 months ago
I feel like option D is the best fit since it mentions both Dataproc and Cloud Composer, which we covered in a similar practice question.
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Lizette
11 months ago
I think using Cloud Composer for orchestration makes sense since it integrates well with other Google Cloud services. But I'm a bit confused about the differences between Composer and Airflow.
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Demetra
11 months ago
I'm not entirely sure about using Cloud Data Fusion for ETL. I thought we practiced more with Dataflow for those types of pipelines.
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Noemi
11 months ago
I remember we discussed Dataproc in class as a good option for migrating Hadoop clusters. It seems like a solid choice here.
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Makeda
11 months ago
Option C with Cloud Data Fusion looks promising. I like the idea of a visual pipeline designer to help modernize my ETL processes. That could save a lot of time and effort compared to manually converting everything.
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Adelina
11 months ago
I'm a bit unsure about this one. Option B with Bigtable and Cloud Composer sounds interesting, but I'm not sure how well it would integrate with my current Hadoop and Airflow setup. Might need to do some more research on that.
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Veronica
11 months ago
This seems like a straightforward migration to the cloud, so I'd go with option A. Dataproc and Dataflow should let me lift and shift my existing Hadoop and Airflow workloads with minimal changes.
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Clement
11 months ago
I'd probably go with option D. Dataproc and Cloud Composer seem like the safest bet to migrate my Hadoop and Airflow workloads to Google Cloud with minimal disruption. The integration should be pretty straightforward.
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Jerry
11 months ago
Okay, let me think this through. The question is asking about useful questions to ask during the design thinking process. I'll need to recall the main stages of that process and come up with appropriate questions for each.
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Lemuel
11 months ago
Okay, let's see. I think the key is to focus on the interfaces that the TAA needs to control and observe. Option C seems to capture that well.
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Geoffrey
11 months ago
Hmm, this looks like a tricky one. I'll need to think carefully about the different storage options that ACK supports.
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Lenora
1 year ago
I gotta say, these cloud services are really starting to sound like they were named by a team of engineers high on caffeine. 'Dataproc'? 'Cloud Composer'? I feel like I need a PhD in Google Cloud just to understand the question!
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Gerald
1 year ago
Wow, this is a tough one! I'm tempted to go with Option A just to keep things simple, but Option C seems like it offers the most comprehensive modernization. 'When in doubt, go with the most features!' - that's my motto!
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Chantell
1 year ago
Option B is an interesting approach, but I'm not sure Bigtable is the best fit for the large workloads mentioned in the question. I'd probably go with Option C for its more holistic solution.
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Cherelle
1 year ago
Yeah, using Dataproc to migrate Hadoop clusters and Cloud Data Fusion for ETL pipelines sounds like a solid plan.
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James
1 year ago
I agree, Option C seems to cover all the bases for modernizing the data strategy.
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Regenia
1 year ago
Option B is a good choice, but I think Option C offers a more comprehensive solution.
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Wilson
1 year ago
Yeah, Option C sounds like the best choice for migrating Hadoop clusters to Google Cloud and handling HDFS use cases.
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Tricia
1 year ago
I think Option C is the way to go as well, especially with the visual design and deployment of ETL pipelines using Cloud Data Fusion.
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Donte
1 year ago
I agree, Option C seems like a more comprehensive solution for handling the Hadoop workloads.
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Anisha
1 year ago
I'm leaning towards Option D. Using Dataproc and Cloud Composer seems like a straightforward approach that aligns well with the existing orchestration processes.
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Bambi
1 year ago
Yeah, it's important to minimize disruptions when updating your data strategy.
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Linn
1 year ago
Dataproc and Cloud Composer seem like a reliable combination for this migration.
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Evangelina
1 year ago
I agree, sticking with what you know can make the transition smoother.
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Amber
1 year ago
Option D sounds like a good choice. It keeps things simple and aligned with what you already have in place.
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Edward
1 year ago
Option C looks like the most comprehensive solution. Migrating Hadoop to Dataproc and using Cloud Storage for HDFS, while leveraging Cloud Data Fusion for visual ETL design, seems like a great way to modernize the architecture with minimal changes.
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Lenna
1 year ago
I agree, using Dataproc for migration, Cloud Storage for HDFS, and Cloud Data Fusion for ETL pipelines sounds like a solid plan.
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Leanora
1 year ago
Option C looks like the most comprehensive solution. Migrating Hadoop to Dataproc and using Cloud Storage for HDFS, while leveraging Cloud Data Fusion for visual ETL design, seems like a great way to modernize the architecture with minimal changes.
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Cordelia
1 year ago
We should also consider converting our ETL pipelines to Dataflow for better efficiency.
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Cory
1 year ago
I agree, and we can use Cloud Storage to handle any HDFS use cases.
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Cordelia
1 year ago
I think we should use Dataproc to migrate our Hadoop clusters to Google Cloud.
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