You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed u

Question

Options

• AUse Data Fusion's GUI to build the transformation pipelines, and then write the data into
• BConvert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on
• CIngest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform
• DIngest your data into BigQuery using BigQuery Load, convert your PySpark commands into

• A
  5% (2)
• B
  15% (6)
• C
  3% (1)
• D
  77% (30)

Explanation

BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse. It can be used to store and query large amounts of data using SQL syntax. BigQuery Load is a tool that can be used to load data into BigQuery from a variety of sources, including Cloud Storage.

No community discussion yet for this question.