You need to create a data store for a directory of small and medium-sized businesses (SMBs). The data store must meet the following requirements: *…

Azure Cosmos DB is a multi-model database that supports various data models, such as documents,

key-value, graph, and column-family3. The core content-model of Cosmos DB’s database engine is

based on atom-record-sequence (ARS), which allows it to store and query different types of data in a

flexible and efficient way3.

To optimize the data store for reading data, you should consider the following factors:

The size and shape of your data

The frequency and complexity of your queries

The latency and throughput requirements of your application

The trade-offs between storage efficiency and query performance

Based on these factors, one possible design that you could implement is B. In a company container,

create a document for each company. Embed the users into company documents. Use the company

ID as the partition key.

This design has the following advantages:

It stores companies and users as self-contained documents that can be easily retrieved by company

ID1.

It avoids storing redundant data or creating additional containers for users1.

It allows you to browse by company and browse the users by company with simple queries1.

It shows a details page for the company and all the related users by fetching a single document1.

It leverages the benefits of embedding data, such as reducing the number of requests, improving

query performance, and simplifying data consistency2.

This design also has some limitations, such as:

It may not be suitable for some users who have data that is greater than 2 KB, as it could exceed the

maximum document size limit of 2 MB2.

It may not be optimal for scenarios where users need to be associated with more than one company

or queried independently from companies2.

It may not be scalable for companies that have more than 1,000 users, as it could result in hot

partitions or throttling2.

Depending on your specific use case and requirements, you may need to adjust this design or choose

a different one. For example, you could use a hybrid data model that combines embedding and

referencing data2, or you could use a graph data model that expresses entities and relationships as

vertices and edges.