Explore fundamentals of Azure Cosmos DB

https://docs.microsoft.com/en-us/learn/modules/explore-non-relational-data-stores-azure/

Relational databases store data in relational tables, but sometimes the structure imposed by this model can be too rigid, and often leads to poor performance unless you spend time implementing detailed tuning. Other models, collectively known as NoSQL databases, exist. These models store data in other structures, such as documents, graphs, key-value stores, and column family stores.

Azure Cosmos DB is a highly scalable cloud database service for NoSQL data.

Azure Cosmos DB

https://docs.microsoft.com/en-us/learn/modules/explore-non-relational-data-stores-azure/2-describe-azure-cosmos-db

Azure Cosmos DB supports multiple application programming interfaces (APIs) that enable developers to use the programming semantics of many common kinds of data store to work with data in a Cosmos DB database. The internal data structure is abstracted, enabling developers to use Cosmos DB to store and query data using APIs with which they’re already familiar.

Cosmos DB uses indexes and partitioning to provide fast read and write performance and can scale to massive volumes of data. You can enable multi-region writes, adding the Azure regions of your choice to your Cosmos DB account so that globally distributed users can each work with data in their local replica.

When to use Cosmos DB

• Cosmos DB automatically allocates space in a container for your partitions
• each partition can grow up to 10 GB in size.
• Indexes are created and maintained automatically.
• There’s virtually no administrative overhead.

Cosmos DB has been used by many of Microsoft’s products for mission critical applications at global scale, including Skype, Xbox, Microsoft 365, Azure, and many others. Cosmos DB is highly suitable for the following scenarios:

IoT and telematics. These systems typically ingest large amounts of data in frequent bursts of activity. Cosmos DB can accept and store this information quickly. The data can then be used by analytics services, such as Azure Machine Learning, Azure HDInsight, and Power BI. Additionally, you can process the data in real-time using Azure Functions that are triggered as data arrives in the database.

Retail and marketing. Microsoft uses Cosmos DB for its own e-commerce platforms that run as part of Windows Store and Xbox Live. It’s also used in the retail industry for storing catalog data and for event sourcing in order processing pipelines.

Gaming. The database tier is a crucial component of gaming applications. Modern games perform graphical processing on mobile/console clients, but rely on the cloud to deliver customized and personalized content like in-game stats, social media integration, and high-score leaderboards. Games often require single-millisecond latencies for reads and write to provide an engaging in-game experience. A game database needs to be fast and be able to handle massive spikes in request rates during new game launches and feature updates.

Web and mobile applications. Azure Cosmos DB is commonly used within web and mobile applications, and is well suited for modeling social interactions, integrating with third-party services, and for building rich personalized experiences. The Cosmos DB SDKs can be used to build rich iOS and Android applications using the popular Xamarin framework.

Azure Cosmos DB APIs

https://docs.microsoft.com/en-us/learn/modules/explore-non-relational-data-stores-azure/3-cosmos-db-apis

• Azure Cosmos DB supports multiple APIs
• enabling developers to easily migrate data from commonly used NoSQL stores and apply their existing programming skills.
• When you provision a new Cosmos DB instance, you select the API that you want to use.

Core (SQL) API

• the native API in Cosmos DB manages data in JSON document format
• despite being a NoSQL data storage solution, uses SQL syntax to work with the data.

A SQL query for a Cosmos DB database containing customer data might look similar to this:

SELECT *
FROM customers c
WHERE c.id = "joe@litware.com"

The result of this query consists of one or more JSON documents, as shown here:

{
   "id": "joe@litware.com",
   "name": "Joe Jones",
   "address": {
        "street": "1 Main St.",
        "city": "Seattle"
    }
}

MongoDB API

• MongoDB is a popular open source database in which data is stored in Binary JSON (BSON) format.
• The Azure Cosmos DB MongoDB API enables developers to use MongoDB client libraries to and code to work with data in Azure Cosmos DB.

MongoDB Query Language (MQL) uses a compact, object-oriented syntax in which developers use objects to call methods. For example, the following query uses the find method to query the products collection in the db object:

db.products.find({id: 123})

The results of this query consist of JSON documents, similar to this:

{
   "id": 123,
   "name": "Hammer",
   "price": 2.99}
}

Table API

• The Table API is used to work with data in key-value tables
• similar to Azure Table Storage.
• The Azure Cosmos DB Table API offers greater scalability and performance than Azure Table Storage.

For example, you might define a table named Customers like this:

You can then use the Cosmos DB Table API through one of the language-specific SDKs to make calls to your service endpoint to retrieve data from the table. For example, the following request returns the row containing the record for Samir Nadoy in the table above:

https://endpoint/Customers(PartitionKey='1',RowKey='124')

Cassandra API

• The Cassandra API is compatible with Apache Cassandra
• a popular open source database that uses a column-family storage structure.
• Column families are tables, similar to those in a relational database, with the exception that it’s not mandatory for every row to have the same columns.

For example, you might create an Employees table like this:

SELECT * FROM Employees WHERE ID = 2

Gremlin API

The Gremlin API is used with data in a graph structure; in which entities are defined as vertices that form nodes in connected graph. Nodes are connected by edges that represent relationships, like this:

The example in the image shows two kinds of vertex (employee and department) and edges that connect them (employee “Ben” reports to employee “Sue”, and both employees work in the “Hardware” department).

Gremlin syntax includes functions to operate on vertices and edges, enabling you to insert, update, delete, and query data in the graph. For example, you could use the following code to add a new employee named Alice that reports to the employee with ID 1 (Sue)

g.addV('employee').property('id', '3').property('firstName', 'Alice')
g.V('3').addE('reports to').to(g.V('1'))

The following query returns all of the employee vertices, in order of ID.

g.V().hasLabel('employee').order().by('id')