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.

This is because the write concern is mapped to the default consistency level configured on your Azure Cosmos DB account, which is strong in this case. Strong consistency ensures that every write operation is synchronously committed to every region associated with your Azure Cosmos DB account. The eventual consistency level that the application uses only applies to the read operations. Eventual consistency offers higher availability and better performance, but it does not guarantee the order or latency of the reads.

you can migrate data from various data sources to Azure Cosmos DB using different tools and methods. The choice of the migration tool depends on factors such as the data source, the Azure Cosmos DB API, the size of data, and the expected migration duration1. Some of the common migration tools are:
Azure Cosmos DB Data Migration tool: This is an open source tool that can import data to Azure Cosmos DB from sources such as JSON files, MongoDB, SQL Server, CSV files, and Azure Cosmos DB collections. This tool supports the SQL API and the Table API of Azure Cosmos DB2.
Azure Data Factory: This is a cloud-based data integration service that can copy data from various sources to Azure Cosmos DB using connectors. This tool supports the SQL API, MongoDB API, Cassandra API, Gremlin API, and Table API of Azure Cosmos DB3.
Azure Cosmos DB live data migrator: This is a command-line tool that can migrate data from one Azure Cosmos DB container to another container within the same or different account. This tool supports live migration with minimal downtime and works with any Azure Cosmos DB API4.
For your scenario, if you want to migrate data from a third-party application that is exposed through an OData endpoint to a container in Azure Cosmos DB for NoSQL, you should use Azure Data Factory. Azure Data Factory has an OData connector that can read data from an OData source and write it to an Azure Cosmos DB sink using the SQL API5. You can create a copy activity in Azure Data Factory that specifies the OData source and the Azure Cosmos DB sink, and run it on demand or on a schedule.