Discuss Google Associate Data Practitioner Exam Topic 1 Question 27

You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

AUse Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

BUse BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

CCreate a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

DUse Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

Suggested Answer: B

Comprehensive and Detailed In-Depth

For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery's native SQL capabilities to minimize cost and complexity.

Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.

Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.

Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.

Option D: Dataflow is powerful for large-scale or streaming ETL but introduces unnecessary complexity and cost for a small, simple batch job. Extract from Google Documentation: From 'Loading Data into BigQuery from Cloud Storage' (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage): 'You can load data directly from Cloud Storage into BigQuery and use SQL queries to transform it without needing additional processing tools, making it cost-effective for simple transformations.' Reference: Google Cloud Documentation - 'BigQuery Data Loading' (https://cloud.google.com/bigquery/docs/loading-data).

Extract from Google Documentation: From 'Loading Data into BigQuery from Cloud Storage' (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage): 'You can load data directly from Cloud Storage into BigQuery and use SQL queries to transform it without needing additional processing tools, making it cost-effective for simple transformations.'

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12 comments · newest first

Bronwyn 2 months ago

Option D could work too. Dataflow is powerful for transformations.

Carolann 2 months ago

I agree, B is efficient. No need for extra infrastructure.

Carisa 2 months ago

I think option B is the best. BigQuery is designed for this.

Gilma 2 months ago

C is cool for visual learners, but might not be the most efficient.

Chanel 2 months ago

A seems like overkill for simple tasks.

Ciara 2 months ago

Wait, can BigQuery really handle transformations that well?

Reiko 3 months ago

I think D is more flexible for larger datasets.

Barbra 3 months ago

B is definitely the easiest way to go!

Temeka 3 months ago

Cloud Data Fusion sounds interesting, but I feel like it could add unnecessary complexity for this task.

Tegan 3 months ago

I practiced a similar question where Dataflow was the answer, but I wonder if it's necessary for just filtering and aggregating.

Janna 3 months ago

I'm not entirely sure, but I think using Dataproc for such a small dataset might be overkill.

Valene 4 months ago

I remember we discussed how BigQuery's SQL capabilities can be really efficient for simple transformations. It might be the best choice here.

Google Associate Data Practitioner Exam - Topic 1 Question 27 Discussion