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Answer: Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
Creating a small set of generalized charts and tables with criteria filters allows for dynamic data exploration without the need for static visualizations for every possible combination. This approach supports scalability and live data display within the required 3-hour delay, enabling users to filter by date ranges, geographic regions, and installation types for efficient exploration of suboptimal links. Other options either lack scalability and dynamic exploration (A and D) or introduce unnecessary complexity (C).
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MJTelco, a startup with innovative optical communications hardware patents, aims to build networks in underserved markets globally. They require a solution to scale their proof-of-concept (PoC) to support over 50,000 installations and refine their machine-learning models. They operate in three environments: development/test, staging, and production. Their needs include scaling production with minimal cost, ensuring data security, providing reliable data access for distributed research, and maintaining isolated environments for rapid machine-learning iteration. For operations, they need a visualization that includes telemetry from all installations for the last 6 weeks, updated within 3 hours, focusing on suboptimal links, with user response time under 5 seconds. Given you've created a data source for the last 6 weeks of data and visualizations that allow viewing multiple date ranges, regions, and installation types without monthly updates, what should you do?
A
Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
B
Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
C
Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
D
Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.