By combining Google Cloud IoT Core for device management and data ingestion, Google Cloud Bigtable for storing and processing IoT data with low latency, and Looker for advanced analytics and visualization, you can build a highly scalable, multi-cloud compatible, and low-latency solution to address your IoT device maintenance requirements effectively.

C . As its says to avoid refactoring.

Google will only pitch its own products

multi-cloud

I think Looker is multi-cloud as some of the colleagues said in this post. So I choose D. https://services.google.com/fh/files/misc/042420-ppm-multi-cloud-one-sheet-8-5x11-en-web-gc.pdf

I think Looker is multi-cloud as some of the colleagues said in this post. So I choose D. https://services.google.com/fh/files/misc/042420-ppm-multi-cloud-one-sheet-8-5x11-en-web-gc.pdf

As others said, MongoDB is the only one that support multi-cloud (otherwise D is also a candidate).

The answer is D. Use Bigtable with Looker. Bigtable is a fully managed, petabyte-scale NoSQL wide column database service for storing large amounts of data. It is designed for low latency, high throughput, and scalability. Bigtable is a good choice for storing IoT data because it can handle the high volume of data generated by IoT devices and provide low latency for real-time analysis. Looker is a business intelligence and data analytics platform that can be used to visualize and analyze data stored in Bigtable. Looker provides a variety of features that can be used to design inspection routines, device repair, and replacement schedules based on insights gathered from the data produced by IoT devices. Looker supports hosting on public clouds like AWS and GCP, and in multi-cloud and hybrid environments.

"Multi-cloud" is the key that rules out everything except C.

Every thing in this question points to D except for one scenario "multi-cloud". So just becuase multi cloud is specified, I choose 'C'

for everyone saying managed solution is the key, MongoDb is also a managed solution

Bigtable for IOT stuff

C. Supports Multi-cloud strategy and managed solution. This makes MongoDB Atlas the only option.

This is a curious question! 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. Therefore the only possible answer is C, even though it isn't a Google product!

D is the managed solution

D. The question says a managed solution, so that eliminates C. Firestore and Spanner do not have the scalability or low latency required. This leaves D. Bigtable by itself is a GCP thing, but Looker allows data visualization across multiple cloud environments. https://www.looker.com/google-cloud/

Based on the requirements provided, the best option would be to use Google Cloud's Bigtable with Looker. Bigtable is a highly scalable NoSQL database that can handle massive amounts of IoT data and can support a multi-cloud strategy. It also provides low latency, which is important for real-time data analysis. Looker is a business intelligence and data analytics platform that can connect to Bigtable to provide insights into the collected IoT data. Looker allows for the creation of custom dashboards and reports, making it easy to identify trends and patterns in the data. Firestore, Cloud Spanner, and MongoD8 Atlas are also capable solutions for handling large amounts of IoT data, but they may not provide the same level of scalability and low latency as Bigtable. Additionally, Data Studio and Charts are visualization tools that can be used in conjunction with Firestore and MongoD8 Atlas, respectively, but they do not offer the same level of data analysis capabilities as Looker.