You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple…

Question

Options

• ACreate a transactional database that monitors the pending messages.
• BCreate a new Pub/Sub push subscription to monitor the orders processed in the agent's system.
• CUse Pub/Sub exactly-once delivery in your pull subscription.
• DUse a Deduphcate PTransform in Dataflow before sending the messages to the sales agents.

• A
  17% (8)
• B
  9% (4)
• C
  72% (34)
• D
  2% (1)

Explanation

Explanation/Reference: Materialized views are precomputed views that periodically cache the results of a query for increased performance and efficiency. BigQuery leverages precomputed results from materialized views and whenever possible reads only changes from the base tables to compute up-to-date results. Materialized views can significantly improve the performance of workloads that have the characteristic of common and repeated queries. Materialized views can also optimize queries with high computation cost and small dataset results, such as filtering and aggregating large tables. Materialized views are refreshed automatically when the base the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow. What should Explanation/Reference: Pub/Sub exactly-once delivery is a feature that guarantees that subscriptions do not receive duplicate deliveries of messages based on a Pub/Sub-defined unique message ID. This feature is only supported by the pull subscription type, which is what you are using in this scenario. By enabling exactly-once delivery, you can ensure that each order is processed only once by a sales agent, and that no order is lost or duplicated. This also simplifies your workflow, as you do not need to create a separate database or subscription to monitor the pending or processed messages.

No community discussion yet for this question.