Cosmos DB overview and use cases in Azure - Step-by-Step Execution

Process Flow - Cosmos DB overview and use cases

Start: Need for global data

↓

Choose Cosmos DB

↓

Create Cosmos DB account

↓

Select API (SQL, Mongo, Cassandra, etc.)

↓

Design data model

↓

Deploy and replicate data globally

↓

Use Cosmos DB for apps

↓

Scale and monitor performance

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Meet low latency and high availability needs

This flow shows the steps from deciding to use Cosmos DB for global data needs, through setup, to using it in applications with scaling and monitoring.

Execution Sample

Azure

Create Cosmos DB account
Choose API (e.g., SQL)
Create container
Insert item
Query item
Scale throughput

This sequence shows basic Cosmos DB operations from setup to data use and scaling.

Process Table

Step	Action	Input/Condition	Result/Output
1	Create Cosmos DB account	Provide account name and region	Account created with global distribution option
2	Select API	Choose SQL API	API set for SQL queries
3	Create container	Define database and container with partition key	Container ready to store JSON documents
4	Insert item	Add JSON document {"id":1, "name":"Alice"}	Item stored in container
5	Query item	Query for id=1	Returns document {"id":1, "name":"Alice"}
6	Scale throughput	Increase RU/s from 400 to 1000	Throughput scaled, better performance
7	Replicate data	Enable multi-region write	Data replicated globally with low latency
8	Monitor	Check metrics	Performance and availability monitored
9	Use in app	App reads/writes data	App experiences fast, reliable data access
10	Exit	No more actions	End of Cosmos DB basic workflow

💡 All key Cosmos DB setup and usage steps completed

Status Tracker

Variable	Start	After Step 3	After Step 4	After Step 6	Final
Account	None	Created	Created	Created	Created
API	None	SQL selected	SQL selected	SQL selected	SQL selected
Container	None	Created	Created	Created	Created
Data Items	Empty	Empty	1 item inserted	1 item inserted	1 item inserted
Throughput (RU/s)	400 (default)	400	400	1000 (scaled)	1000
Replication	Single region	Single region	Single region	Single region	Multi-region enabled

Key Moments - 3 Insights

Why do we choose an API like SQL or Mongo when creating Cosmos DB?

Because Cosmos DB supports multiple APIs to match different app needs; choosing SQL API (see step 2) lets you use familiar SQL queries on JSON data.

What happens when we scale throughput in Cosmos DB?

Scaling throughput (step 6) increases the Request Units per second (RU/s), allowing faster and more concurrent operations, improving app performance.

How does global replication affect data access?

Enabling multi-region replication (step 7) copies data to multiple regions, reducing latency and increasing availability for users worldwide.

Visual Quiz - 3 Questions

Look at the execution table, what is the throughput after step 6?

A 400 RU/s

B 200 RU/s

C 1000 RU/s

D Unlimited

💡 Hint

Check the 'Throughput (RU/s)' variable in the variable_tracker after step 6

At which step is the first data item inserted into Cosmos DB?

A Step 4

B Step 3

C Step 5

D Step 6

💡 Hint

Look at the execution_table row where 'Insert item' action happens

If we do not enable multi-region replication, what remains the replication state?

A Multi-region enabled

B Single region

C No replication

D Replication disabled

💡 Hint

See the 'Replication' variable in variable_tracker before step 7

Concept Snapshot

Cosmos DB is a globally distributed database service.
Choose an API (SQL, Mongo, Cassandra) to match your app.
Create containers to store JSON documents.
Scale throughput (RU/s) for performance.
Enable multi-region replication for low latency and high availability.
Use Cosmos DB for apps needing fast, global data access.

Full Transcript

This visual execution shows how to use Azure Cosmos DB from start to finish. First, you create a Cosmos DB account and select the API type, such as SQL. Then you create a container to hold your data. You insert items as JSON documents and query them using the chosen API. You can scale throughput to improve performance. Enabling multi-region replication copies data globally for fast access everywhere. Monitoring helps keep track of performance and availability. This process supports apps that need reliable, low-latency data worldwide.

Practice

1. What is the main benefit of using Azure Cosmos DB for global applications?

easy

A. It provides low latency and high availability worldwide.

B. It only supports SQL queries.

C. It requires manual scaling of resources.

D. It is designed for local, single-region apps only.

Solution

1. Step 1: Understand Cosmos DB's global distribution

   Cosmos DB is designed to replicate data across multiple regions to provide fast access and high availability.

2. Step 2: Identify the benefit for global apps

   This replication ensures low latency and continuous availability for users worldwide.

3. Final Answer:

   It provides low latency and high availability worldwide. -> Option A

4. Quick Check:

   Global apps need low latency and high availability = A [OK]

Hint: Global apps need fast, reliable data access worldwide [OK]

Common Mistakes:

• Thinking Cosmos DB only supports SQL
• Assuming manual scaling is required
• Believing it's for single-region use only

2. Which of the following is a supported API model in Azure Cosmos DB?

easy

A. Oracle SQL API

B. MySQL API

C. MongoDB API

D. PostgreSQL API

Solution

1. Step 1: Recall Cosmos DB supported APIs

   Azure Cosmos DB supports multiple APIs including SQL, MongoDB, Cassandra, Gremlin, and Table APIs.

2. Step 2: Identify the correct API from options

   MongoDB API is officially supported, while Oracle, MySQL, and PostgreSQL APIs are not.

3. Final Answer:

   MongoDB API -> Option C

4. Quick Check:

   MongoDB API is supported by Cosmos DB = B [OK]

Hint: Remember Cosmos DB supports MongoDB API, not Oracle or MySQL [OK]

Common Mistakes:

• Confusing Cosmos DB with relational databases
• Assuming all SQL-based APIs are supported
• Selecting unsupported database APIs

3. Given this Cosmos DB use case: A global e-commerce app needs to store product catalog data with fast reads and writes worldwide. Which Cosmos DB feature best supports this?

medium

A. Local SSD caching only

B. Multi-region replication with automatic failover

C. Single-region write with manual backups

D. Manual sharding without replication

Solution

1. Step 1: Analyze the requirement for global fast reads and writes

   The app needs data available quickly everywhere and must handle writes globally.

2. Step 2: Match Cosmos DB features to requirements

   Multi-region replication with automatic failover allows writes and reads in multiple regions with high availability and low latency.

3. Final Answer:

   Multi-region replication with automatic failover -> Option B

4. Quick Check:

   Global fast reads/writes need multi-region replication = C [OK]

Hint: Global writes need multi-region replication with failover [OK]

Common Mistakes:

• Choosing single-region write limits scalability
• Confusing caching with replication
• Ignoring automatic failover importance

4. You created a Cosmos DB container but notice your app experiences high latency when accessing data globally. What is the most likely cause?

medium

A. The container is configured with single-region write and no multi-region replication.

B. The container uses multi-region replication with automatic failover.

C. The container has indexing enabled for all properties.

D. The container is using the MongoDB API.

Solution

1. Step 1: Understand latency causes in global apps

   High latency often happens if data is only written and read from one region far from users.

2. Step 2: Identify configuration causing latency

   Single-region write without multi-region replication means users far from that region experience delays.

3. Final Answer:

   The container is configured with single-region write and no multi-region replication. -> Option A

4. Quick Check:

   Single-region write without replication causes high latency = A [OK]

Hint: Check if multi-region replication is enabled to reduce latency [OK]

Common Mistakes:

• Assuming indexing causes latency
• Confusing API choice with latency issues
• Believing multi-region replication increases latency

5. A company wants to build a globally distributed IoT app that collects sensor data continuously and requires automatic scaling and low latency. Which Cosmos DB feature combination best fits this use case?

hard

A. Gremlin API with no replication and fixed throughput

B. Single API support with manual scaling and single-region writes

C. Table API with single-region replication and manual failover

D. Multi-model support with multi-region writes and automatic scaling

Solution

1. Step 1: Identify IoT app requirements

   The app needs to handle continuous data ingestion globally with automatic scaling and low latency.

2. Step 2: Match Cosmos DB features to these needs

   Multi-model support allows flexible data types; multi-region writes enable global data ingestion; automatic scaling handles variable loads.

3. Final Answer:

   Multi-model support with multi-region writes and automatic scaling -> Option D

4. Quick Check:

   IoT needs global writes and scaling = D [OK]

Hint: IoT needs multi-region writes plus auto scaling [OK]

Common Mistakes:

• Choosing single-region writes limits global data ingestion
• Ignoring automatic scaling for variable IoT loads
• Selecting APIs without replication support