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Answer: 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.
The correct answer is D. Here's why this option is the most suitable: 1. Google BigQuery is a powerful data warehouse for processing and analyzing large datasets. It can efficiently handle the telemetry data from all 50,000 installations. 2. Google Data Studio 360 is designed for creating interactive and visually appealing reports and dashboards. Using Google Data Studio allows you to connect to BigQuery, calculate the required metrics, and apply filters to show only suboptimal links. 3. It can provide real-time or near-real-time data updates, ensuring that the report is not more than 3 hours delayed from live data. 4. Google Data Studio can also be used to sort and group suboptimal links and display them based on regional geography. 5. With the right design, you can ensure that user response time to load the report is less than 5 seconds. This approach leverages Google's cloud services effectively to meet the specified requirements. Other options like Google Sheets or Google Cloud Datastore might not efficiently handle such large datasets and real-time analysis needs.
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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.