What should you do?

Your team uses thousands of connected IoT devices to collect device maintenance data for your oil and gas customers in real time. You want to design inspection routines, device repair, and replacement schedules based on insights gathered from the data produced by these devices. You need a managed solution that is highly scalable, supports a multi-cloud strategy, and offers low latency for these IoT devices.

What should you do?
A . Use Firestore with Looker.
B . Use Cloud Spanner with Data Studio.
C . Use MongoD8 Atlas with Charts.
D . Use Bigtable with Looker.

Answer: C

Explanation:

This scenario has BigTable written all over it – large amounts of data from many devices to be analysed in realtime. I would even argue it could qualify as a multicloud solution, given the links to HBASE. BUT it does not support SQL queries and is not therefore compatible (on its own) with Looker. Firestore + Looker has the same problem. Spanner + Data Studio is at least a compatible pairing, but I agree with others that it doesn’t fit this use-case – not least because it’s Google-native. By contrast, MongoDB Atlas is a managed solution (just not by Google) which is compatible with the proposed reporting tool (Mongo’s own Charts), it’s specifically designed for this type of solution and of course it can run on any cloud.