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Answer: Set up a BigQuery dataset for the data science team. Configure Datastream to replicate the relevant Cloud SQL tables in BigQuery.
The goal is to provide the data science team with up-to-date data without impacting the production Cloud SQL database. Option C uses Datastream to replicate Cloud SQL data into BigQuery, which is optimized for analytics and reporting. This approach ensures near real-time data availability via CDC (Change Data Capture), offloading reporting workloads from the operational database. Options A (hourly CSV exports) and D (15-minute clones) introduce latency and operational overhead, while B (Bigtable dual writes) is unsuitable for SQL-based reporting. BigQuery's separation from the transactional system and scalability make C the best choice.
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As a developer for an ecommerce website using Cloud SQL for PostgreSQL to store customer order data, how can you allow marketing data scientists to access up-to-date datasets for their reports without impacting the website's performance?
A
Configure Cloud Scheduler to run an hourly Cloud Function that exports the data from the Cloud SQL database into CSV format and sends the data to a Cloud Storage bucket.
B
Set up a Bigtable table for the data science team. Configure the application to perform dual writes to both Cloud SQL and Bigtable simultaneously.
C
Set up a BigQuery dataset for the data science team. Configure Datastream to replicate the relevant Cloud SQL tables in BigQuery.
D
Create a clone of the PostgreSQL database instance for the data science team. Schedule a job to create a new clone every 15 minutes.