Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
An e-payment company collects its service payment transaction events from its app installed in nearly 200,000 devices. Those events need to be stored for further time-series analysis and fraud detection. Which of the following approaches is recommended to implement?
A. Use Google Storage to store data. Use Dataproc with Apache Hive to do required queries on data.
B. Use Cloud SQL as a database. Make sure you launch a multi-regional instance for higher peformance.
C. Use BigTable as a database. Use short & wide tables when designing the schema and row key.
D. Use BigTable as a database. Use tall & narrow tables when designing the schema and row key.
Answer: D.
Storing time-series data in Cloud Bigtable is a natural fit. Cloud Bigtable stores data as unstructured columns in rows; each row has a row key, and row keys are sorted lexicographically.
For time series, you should generally use tall and narrow tables. This is for two reasons:
- Storing one event per row makes it easier to run queries against your data.
- Storing many events per row makes it more likely that the total row size will exceed the recommended maximum.
Answer A is incorrect: For this scenario, using BigTable is preferred over storing data in Google Storage as further data partitioning and file formatting is required to use Dataproc with Apache Hive.
Answer B is incorrect: Cloud SQL is a relational database. Event data might not have a fixed structure. Cloud SQL is not scalable to write thousands of rows in a given second.
Answer C is incorrect: Wide & short table schema is not optimal for time-series event data.
Source(s):
BigTable – Schema Design for Time Series Data: https://cloud.google.com/bigtable/docs/schema- design-time-series
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