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Question 365 - Professional Data Engineer discussion

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You currently use a SQL-based tool to visualize your data stored in BigQuery The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

A.
Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_3taleness parameter to 4 hours and the enable_refresh parameter to true.Reference: the materialized views in the data visualization tool.
Answers
A.
Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_3taleness 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.
Answers
B.
Create views for the visualization queries.Reference: the views in the data visualization tool.
C.
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.
Answers
C.
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.
D.
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.
Answers
D.
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.
Suggested answer: C

Explanation:

To improve the performance of visualization queries while minimizing maintenance overhead, using materialized views is the most effective solution. Here's why option C is the best choice:

Materialized Views:

Materialized views store the results of a query physically, allowing for faster access compared to regular views which execute the query each time it is accessed.

They can be automatically refreshed to reflect changes in the underlying data.

Incremental Updates:

The incremental updates capability of BigQuery materialized views ensures that only the changed data is processed during refresh operations, significantly improving performance and reducing computation costs.

This feature helps maintain up-to-date data in the materialized view with minimal processing time, which is crucial for data that needs to be no less than 4 hours old.

Performance and Maintenance:

By using materialized views, you can pre-compute and store the results of complex queries involving outer joins and analytic functions, resulting in faster query performance for data visualizations.

This approach also reduces the maintenance overhead, as BigQuery handles the incremental updates and refreshes automatically.

Steps to Implement:

Create Materialized Views:

Define materialized views for the visualization queries with the necessary configurations

CREATE MATERIALIZED VIEW project.dataset.view_name

AS

SELECT ...

FROM ...

WHERE ...

Enable Incremental Updates:

Ensure that the materialized views are set up to handle incremental updates automatically.

Update the data visualization tool to reference the materialized views instead of running the original queries directly.

BigQuery Materialized Views

Optimizing Query Performance

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asked 18/09/2024
pheangphadhu pravitpinyo
44 questions
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