Exam Professional Cloud Architect topic 1 question 8 discussion

I'll go with B. This is time series data. We also have no idea what kinds of data are being captured so it doesn't appear structurd. A does not seem reasonable because a flat file is not easy to query and analyze. B seems reasonable because this accommodates unstructured data. C seems unreasonable because we have no idea on the structure of the data. D seems unreasonable beacause there is no such Google database type.

B is the suitable option for unstructured data

A / D : No very good in order to exploit the data C : Relational DB would have been a good candidate for managing sensor data only. But it is said to managing other data type which are not detailled. So B is the best

We don't know the data type. Only nosql make sense.

Answer is B. 1. High Volume and Velocity of Data: You have 1000 rooms reporting data every second, resulting in a massive amount of data with high velocity. NoSQL databases are designed to handle this kind of volume and speed efficiently. 2. Simple Data Structure: The data from the motion sensor is relatively simple (sensor ID and discrete information). NoSQL databases are well-suited for storing and processing this type of data without the need for complex schemas. 3. Flexible Schema: NoSQL databases offer schema flexibility, allowing you to easily adapt to changes in the data structure if needed. This is important as your tracking requirements might evolve over time. 4.Scalability: NoSQL databases are highly scalable, making it easy to accommodate future growth in the number of meeting rooms or data volume.

With frequencies of data (per second), the best case would be using pub/sub and NoSQL. Relational DB/BlobStore/FlatFile are not good for Near realtime data.

C. Relational database. Here's why: Scalability: A relational database can handle the data volume from 1000 sensors reporting every second effectively. Structure: It provides a well-defined schema for organizing data like sensor ID, timestamp, motion status, account owner, and office location, making it easily queryable and understandable for analysts. Relationships: It allows establishing relationships between tables, such as linking sensor data to specific meeting rooms and their corresponding owners and locations. This facilitates analyses involving multiple data sources. Flexibility: Relational databases offer flexibility for expanding data collection beyond motion sensors in the future to include other sensor types or meeting room details.

B, It is

Unstructured realtime aata

b [ You need seperate fields and keys -- you do not need to relate them

NOSQL DB's are meant for these kind of workloads

The requirement to join the data to other data sets implies RDBMS. BigQuery can handle 1GB/s when streaming inserts, I doubt these 1000 sensors will send that much data. Bigtable seems over the top and not able to fulfil all the requirements.

This will be time-series data. The best DB would be a Big Table (also sensorID can be used in the row key for faster retrieval of data)

B for me

B is right

B (No SQL should be the right answer)

surprised by the options given, this is a great use case of Bigtable so NoSQL