Ultimate access to all questions.
As a data engineer, you are tasked with creating visualizations for operations teams. The visualizations should meet the following criteria:
✑ The report must include telemetry data from all 50,000 installations collected over the most recent 6 weeks, with data sampling occurring once every minute. ✑ The report must be updated such that it is no more than 3 hours behind the live data. ✑ The report should focus on actionable insights by displaying only suboptimal links. ✑ The report should prioritize and sort the most suboptimal links at the top. ✑ The suboptimal links should be able to be grouped and filtered based on regional geography. ✑ The user response time to load the report must be less than 5 seconds.
Which approach meets these requirements?
A
Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
B
Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
C
Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
D
Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.