Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You receive event data related to on-premise servers holding information about the servers CPU load, memory, disk space, I/O reads and writes, and another application performance stats every 60 seconds. These events are stored in Google Storage. It is decided to use the data to monitor the on-premise architecture. Metrics should be extracted from these events in time-series base for further calculations based on the timeline. Which of the following is best to achieve this?
A. Use Cloud SQL as a database. Move data from Google Storage to Cloud SQL.
B. Use Dataproc with Apache Hive to do required queries on data.
C. Move data to BigTable. Use tall & narrow tables when designing the schema and row key.
D. Move data to BigTable. Use short & wide tables when designing the schema and row key.
Correct Answer: C
Storing time-series data in Cloud Bigtable is a natural fit for the given scenario. 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:
1) Storing one event per row makes it easier to run queries against your data. 2) Storing many events per row makes it more likely that the total row size will exceed the recommended maximum.
Option A is incorrect: Cloud SQL is a relational database. Event data might require a flexible structure. Cloud SQL is not scalable to write thousands of rows in a given second.
Option B is incorrect: For this scenario, using BigTable is preferred over storing data in Google Storage as further data partitioning and file formatting, both are required to use Dataproc with Apache Hive.
Option D 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
Comments are closed, but trackbacks and pingbacks are open.