Begin this course by exploring index policies in Cosmos DB and how these influence the performance of queries, as well as how many request units are consumed by them. Next, you will modify the default index policy and explicitly define the fields of the items which must be included in the index and how fields can be left out of it. Then, you will focus on composite indexes and run various types of queries against your data, including aggregation queries. You will build a Python application that can connect to and query a Cosmos DB container using the azure-cosmos package and various interfaces, such as a DatabaseProxy and ContainerProxy. Finally, you will create a new Cosmos DB account, this time using the MongoDB API, in order to observe the similarities between MongoDB API and MongoDB server.

In this course, you will be introduced to the fundamental concepts of Cosmos DB and begin using this service by setting up a database and container. Begin by highlighting the differences between NoSQL and SQL databases to recognize exactly where Cosmos DB fits within this landscape. Then, explore the features of and use cases for Azure Cosmos DB. Next, find out how Cosmos DB organizes and stores data and enables access via various APIs such as the MongoDB API and the Apache Cassandra API and examine the factors that help you select the right Cosmos DB API for your own use cases. Finally, you will create a Cosmos DB account on the Azure cloud and provision a database and container within that service.

In this course, you will examine how data is loaded into Cosmos DB, including data migration from a relational database and how to query that data. Begin by using the Cosmos DB web console to load items into a container, focusing on the types of data that can be loaded, like arrays and nested JSON structures. Then, you will query these items using an SQL-like syntax and explore array indexing, in-partition and cross-partition queries, as well as intra-document joins in order to unpack arrays. Then, you will migrate data from a relational Azure SQL Database to Cosmos DB using Azure's Data Factory service, which allows you to define and launch a pipeline for a migration using an intuitive web UI. Finally, you will define a step to copy specific data from a source database and then migrate it over to a Cosmos DB container, assembling all the little pieces required for such a pipeline.