Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read –
.named(“ReadLogDataâ€)
.from(“clouddataflow-readonly:samples.log_dataâ€)
You want to improve the performance of this data read. What should you do?
A. Specify the TableReference object in the code.
B. Use .fromQuery operation to read specific fields from the table.
C. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
D. Call a transform that returns TableRow objects, where each element in the PCollection represents a single row in the table.
Could you please answer this question. I a m thinking the answer as B.
1 Answers
It seems this question was taken from a practice (or potentially a previous attempt at the) exam.
Anyway Googling it I found this: https://www.examtopics.com/exams/google/professional-data-engineer/view/6/
See Question 28 – correct answer D
There are some discussions attached to this as well that suggest B
"BigQueryIO.read.from() directly reads the whole table from BigQuery. This function exports the whole table to temporary files in Google Cloud Storage, where it will later be read from. This requires almost no computation, as it only performs an export job, and later Dataflow reads from GCS (not from BigQuery).
BigQueryIO.read.fromQuery() executes a query and then reads the results received after the query execution. Therefore, this function is more time-consuming, given that it requires that a query is first executed (which will incur in the corresponding economic and computational costs)."