Exam Professional Cloud Database Engineer topic 1 question 21 discussion

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.

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/

Best fit for the stated requirements: C. Use MongoDB Atlas with Charts Multi-cloud Managed service Handles IoT time-series data well Low latency, high scalability Built-in visualization

Multi-Cloud Strategy Support MongoDB Atlas is a managed DBaaS available across AWS, Azure, and Google Cloud, enabling hybrid/multi-cloud deployments. This aligns with the need for geographic redundancy and compliance across regions.

a multicloud solution is needed and MongoDB Atlas fill all the checks. C is correct

Scalability: Bigtable is a fully managed, highly scalable NoSQL database that can handle large volumes of data with low latency, making it well-suited for real-time data ingestion from thousands of IoT devices. Low Latency: Bigtable is designed for high-throughput and low-latency read and write operations, which is essential for real-time data processing and insights. Multi-Cloud Strategy: While Bigtable itself is a Google Cloud service, it can integrate with other cloud services and tools, and data can be exported or accessed from other cloud environments if needed. Integration with Looker: Looker is a powerful business intelligence tool that can connect to Bigtable to provide insights and visualizations. This integration allows you to analyze the data and design inspection routines, repair schedules, and replacement schedules based on the insights gathered.

Managed solution hence Bigtable is right choice

Agree with D as You need a managed solution

Bigtable can't be integrated with looker

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.