Google Professional Data Engineer
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As a data engineer, you are currently utilizing a SQL-based tool for visualizing data stored in Google BigQuery. The data visualizations necessitate the use of outer joins and analytic functions, and it is crucial that these visualizations are based on data that is no older than 4 hours. However, business users have reported that the visualizations take too long to generate. Your objective is to enhance the performance of these visualization queries while also minimizing the maintenance overhead associated with the data preparation pipeline. What steps should you take to achieve this?
A
Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_staleness parameter to 4 hours and the enable_refresh parameter to true. Reference the materialized views in the data visualization tool._
B
Create views for the visualization queries. Reference the views in the data visualization tool.
C
Create a Cloud Function instance to export the visualization query results as parquet files to a Cloud Storage bucket. Use Cloud Scheduler to trigger the Cloud Function every 4 hours. Reference the parquet files in the data visualization tool.
D
Create materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle changed data automatically. Reference the materialized views in the data visualization tool.