Google Professional Data Engineer Exam - Topic 3 Question 95 Discussion

Actual exam question for Google's Professional Data Engineer exam

Question #: 95
Topic #: 3

[All Professional Data Engineer Questions]

You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?

AUse Cloud Composer to load data and run SQL pipelines by using the BigQuery job operators.

BUse Dataflow jobs to read data from Pub/Sub, transform the data, and load the data to BigQuery.

CUse Dataform to build, manage, and schedule SQL pipelines.

DUse Data Fusion to build and execute ETL pipelines

Show Suggested Answer

Suggested Answer: C

To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here's why:

ELT Development Technique:

ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.

BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.

Dataform:

Dataform is a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.

It provides tools for managing SQL as code, including version control and collaborative development.

Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.

Intuitive Coding Environment:

Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.

It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.

Managing SQL as Code:

Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.

This allows for better collaboration, code reviews, and version tracking.

Dataform Documentation

BigQuery Documentation

Managing ELT Pipelines with Dataform

by Carissa at Sep 15, 2024, 05:25 AM

Limited Time Offer

25%

Off

Get Premium Professional Data Engineer Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Janine

4 months ago

I disagree, Data Fusion is more suited for ETL, not ELT.

upvoted 0 times

...

Rodolfo

4 months ago

Wait, can Dataform really manage SQL as code effectively?

upvoted 0 times

...

Lavera

4 months ago

Dataflow is great for streaming, but not sure it's the best fit here.

upvoted 0 times

...

Chana

4 months ago

I think Cloud Composer is overkill for this.

upvoted 0 times

...

Deandrea

5 months ago

Dataform sounds like a solid choice for SQL pipelines!

upvoted 0 times

...

Malinda

5 months ago

I feel like Data Fusion is more for ETL processes, which might not align with the ELT approach we need here.

upvoted 0 times

...

Twana

5 months ago

Dataflow seems more focused on streaming data, so I’m leaning towards Dataform for this scenario, but I could be wrong.

upvoted 0 times

...

Johnna

5 months ago

I think Cloud Composer was mentioned in a practice question, but I’m not clear if it’s the best fit for SQL-heavy tasks.

upvoted 0 times

...

Nana

5 months ago

I remember we discussed Dataform in class as a good option for managing SQL pipelines, but I'm not entirely sure how it compares to the others.

upvoted 0 times

...

Annice

5 months ago

Okay, I've got a strategy here. Since the developers want to use SQL and have an intuitive coding environment, I'm thinking Dataform or Cloud Composer would be the best options to consider. I'll need to dig into the specifics of each to determine the pros and cons.

upvoted 0 times

...

Bettye

5 months ago

Hmm, this is a tricky one. I'm not too familiar with all the different data transformation options on Google Cloud. I'll need to carefully review the details of each choice to figure out which one best fits the requirements.

upvoted 0 times

...

Helaine

5 months ago

This looks like a straightforward question about choosing the right data transformation solution for BigQuery. I think I have a good handle on the key requirements - ELT, SQL-based pipelines, and an intuitive coding environment.

upvoted 0 times

...

Ligia

6 months ago

I'm feeling pretty confident about this one. Based on the requirements, I'd say Dataform is the way to go. It's designed specifically for building, managing, and scheduling SQL-based data pipelines, which seems to be exactly what this scenario calls for.

upvoted 0 times

...

Barbra

6 months ago

Hmm, I'm a bit unsure about this one. I know we covered functional size in class, but I can't quite recall the exact formula. Let me think this through carefully.

upvoted 0 times

...