Topic 1 Question 292
You have terabytes of customer behavioral data streaming from Google Analytics into BigQuery daily. Your customers’ information, such as their preferences, is hosted on a Cloud SQL for MySQL database. Your CRM database is hosted on a Cloud SQL for PostgreSQL instance. The marketing team wants to use your customers’ information from the two databases and the customer behavioral data to create marketing campaigns for yearly active customers. You need to ensure that the marketing team can run the campaigns over 100 times a day on typical days and up to 300 during sales. At the same time, you want to keep the load on the Cloud SQL databases to a minimum. What should you do?
Create BigQuery connections to both Cloud SQL databases. Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.
Create a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries.
Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.
Create a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries.
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- 正解だと思う選択肢: C
C. Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.
👍 2scaenruy2024/01/04 - 正解だと思う選択肢: C
- Datastream: It's a fully managed, serverless service for real-time data replication. It allows to stream data from various sources, including Cloud SQL, into BigQuery.
- Reduced Load on Cloud SQL: By replicating the required tables from both Cloud SQL databases into BigQuery, you minimize the load on the Cloud SQL instances. The marketing team's queries will be run against BigQuery, which is designed to handle high-volume analytics workloads.
- Frequency of Queries: BigQuery can easily handle the high frequency of queries (100 times daily, up to 300 during sales events) due to its powerful data processing capabilities.
- Combining Data Sources: Once the data is in BigQuery, you can efficiently combine it with the Google Analytics data for comprehensive analysis and campaign planning.
👍 1raaad2024/01/11
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