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1. CENTRAL UNIVERSITY OF SOUTH BIHAR Microsoft u ses CosmosDB SUBMITTED BY: ABHINEET KUMAR (CUSB2302312001) MOHIT KUMAR (CUSB2302312010) SUBMITTED TO: DR. PRABHAT RANJAN ASSOCIATE PROFESSOR, CUSB

2. Contents Introduction Microsoft Azure Cosmos DB Features of Azure Cosmos DB Architecture of Cosmo dB Storage mechanism Some Examples Comparing Cosmos DB and MongoDB Advantages & Disadvantages Conclusion

3. Microsoft Microsoft Corporation Founded: April 4, 1975 Founders: Bill Gates and Paul Allen Headquarters: Redmond, Washington, USA CEO: Satya Nadella Industry: Technology Revenue: $211.9 billion (FY 2023) Employees: ~221,000 (2023) Microsoft is a global leader in technology, known for its software products like the Windows operating system and Microsoft Office suite. It also provides a range of cloud services through Azure, develops hardware like Surface devices and Xbox gaming consoles, and is a key player in AI development, with investments in OpenAI and generative AI products such as Copilot in Office applications. Microsoft’s cloud segment, Azure, is a major revenue driver, positioning the company as a leader in cloud computing.

4. AZURE COSMOS DB Azure Cosmos DB is a globally distributed, fully managed NoSQL database service by Microsoft, designed for mission-critical applications with high scalability and low-latency performance. Use Cases : Ideal for applications requiring real-time data access, such as IoT, gaming, retail, and social media platforms.

5. Azure cosmosdb’s history 2010 – Origin as "Project Florence" Developed internally to store large-scale unstructured data for Microsoft services. 2014 – Launch as DocumentDB Initially released as DocumentDB , focusing on document storage with limited functionality. 2017 – Rebranded and Public Release as Azure Cosmos DB Evolved into a globally distributed, multi-model database with horizontal partitioning for scalability. 2018 – Multi-Master Feature Announcement Introduced multi-master capabilities, allowing multiple write regions, boosting scalability and reliability. Key Features & Innovations Elastic Scalability : Automatic scaling of throughput and storage. Global Distribution : Low-latency access across multiple Azure regions. Multiple Data Models : Supports document, key-value, graph, and more. 99.99% Availability : High availability with comprehensive SLAs.

6. Why we need Rapid Scaling & Low-Latency Ideal for apps needing fast scaling and low-latency responses. Multi-Model Support Supports key-value, document, graph, and columnar models via APIs like MongoDB, Gremlin, NoSQL, and Table. High Performance & Availability SSD-backed storage, millisecond latencies, 99.99% availability, and multi-region replication with failover options. Flexible Consistency Models Offers 5 models (Strong, Bounded-Staleness, Session, Consistent Prefix, Eventual) for cost-performance balance. Elastic Scaling Independent scaling of throughput and storage; trillions of requests per day using Request Units (RUs). Automatic Partitioning Optimizes performance and scalability using partition keys for large-scale applications. Change Data Capture (CDC) Real-time data monitoring via Change Feed, supporting event-driven workflows. Cost-Effective for Serverless Apps Ideal for web, mobile, IoT, and gaming applications with high throughput and unlimited storage. SLAs for Mission-Critical Workloads Financially backed SLAs for throughput, latency, availability, and consistency

7. Birth of Azure Cosmos DB Scalability : Seamlessly scales storage and throughput to meet growing demands. Low Latency : Optimized for fast read/write operations with low-latency responses. High Availability : Provides 99.99% availability with multi-region replication. Flexible Consistency Models : Offers 5 consistency models (Strong, Bounded-Staleness, Session, Consistent Prefix, Eventual) to balance performance and correctness. Multi-Model Support : Accommodates key-value, graph, document, and column-family data models. API Compatibility : Supports SQL and open-source APIs, enabling cloud-agnostic development. No Schema Management : Frees developers from managing schemas and indexes. Cost Efficiency : Designed for low-cost operation, ideal for high-volume applications. Use Cases : Perfect for handling unstructured data, such as user-generated content for social media.

8. Key Features of Azure Cosmos DB Globally Distributed : One-click data replication across multiple Azure regions. Linearly Scalable : Horizontally scales to handle millions of transactions per second. Schema-Agnostic Indexing : Automatically indexes all data without schema or index management. Multi-Model Database : Supports key-value, document, graph, and column-family data models with consistent features. Multi-API & Multi-Language Support : Compatible with SQL, MongoDB, Cassandra, Gremlin, and supports SDKs for Java, .NET, Python, and more. Multi-Consistency Support : Offers 5 consistency levels: Eventual, Prefix, Session, Bounded-Staleness, and Strong. Automatic Indexing : Every property is automatically indexed, with custom indexing options available. High Availability : 99.999% for multi-region with multi-region writes. 99.99% for single-region accounts. Supports automatic failover. Guaranteed Low Latency : 10 ms read/write latency at the 99th percentile. Multi-Master Support : Multi-master writes across regions, enabling elastic scaling for reads and writes.

9. Architecture of Azure Cosmos DB

10. Data Stored

11. Retail Marketing

12. Storage Mechanism

13. STORAGE MECHANISM Partitioning & Data Distribution Logical vs. Physical Partitions : Data is split into logical partitions (based on partition keys), stored in physical partitions. Automatic Scaling : As data grows, Cosmos DB automatically splits physical partitions to handle more data without downtime. Global Distribution & Multi-Region Replication Replicates data across multiple Azure regions, ensuring low-latency access and high availability. Offers multiple consistency models (e.g., Strong, Eventual) to balance performance and consistency. Request Units (RUs) & Throughput Scaling Measures database operations (reads, writes, queries) with RUs for unified pricing. Evenly distributes throughput across partitions to avoid throttling and high latency. Encryption at Rest & in Transit Encryption at Rest : Uses AES-256 to encrypt data on SSDs. Encryption in Transit : Uses secure HTTPS connections for data transmission. Indexing & Storage Optimization Automatically indexes data with customizable policies for performance and cost efficiency. Supports Time-to-Live (TTL) policies to purge old data and optimize storage. Backup & Disaster Recovery Continuous backups stored in Azure Blob Storage for recovery in case of accidental deletion or corruption.

14. from azure.cosmos import CosmosClient , PartitionKey url = 'https://cosmosrgeastus467823cc-9237-4d5c-b38edb.documents.azure.com:443/' key = '<nyQ525S6kZskc5gktn7eTxEtlf9YCnxOn1FQdFkk3iUoLXNhDbWVtlssnAunmjsKqCosp5Zov0QSACDbsxL3nA==>' client = CosmosClient ( url , credential=key) database_name = 'Tasks' container_name = 'Items' database = client.get_database_client ( database_name ) container = database.get_container_client ( container_name ) items_to_insert = [ { 'id': '1', ' ItemID ': '101', 'name': ' CosmosDB Task 1', 'description': 'Inserting first data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'in-progress' },

15. { 'id': '2', ' ItemID ': '102', 'name': ' CosmosDB Task 2', 'description': 'Inserting second data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'completed' }, { 'id': '3', ' ItemID ': '103', 'name': ' CosmosDB Task 3', 'description': 'Inserting third data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'pending' },

16. { 'id': '4', ' ItemID ': '104', 'name': ' CosmosDB Task 4', 'description': 'Inserting fourth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'in-progress' }, { 'id': '5', ' ItemID ': '105', 'name': ' CosmosDB Task 5', 'description': 'Inserting fifth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'completed' },

17. { 'id': '6', ' ItemID ': '106', 'name': ' CosmosDB Task 6', 'description': 'Inserting sixth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'pending' }, { 'id': '7', ' ItemID ': '107', 'name': ' CosmosDB Task 7', 'description': 'Inserting seventh data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'in-progress' },

18. { 'id': '8', ' ItemID ': '108', 'name': ' CosmosDB Task 8', 'description': 'Inserting eighth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'completed' }, { 'id': '9', ' ItemID ': '109', 'name': ' CosmosDB Task 9', 'description': 'Inserting ninth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'pending' },

19. { 'id': '10', ' ItemID ': '110', 'name': ' CosmosDB Task 10', 'description': 'Inserting tenth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'in-progress' } ] # Insert each document into the container for item in items_to_insert : container.create_item (body=item) print("10 unique items inserted successfully.")

20. query = "SELECT * FROM bigdata" items = list( container.query_items ( query=query, enable_cross_partition_query =True )) for item in items: print(item) Query 1. query = "SELECT * FROM bigdata WHERE bigdata.status = 'in-progress'" items = list( container.query_items ( query=query, enable_cross_partition_query =True )) for item in items: print(item) Query 2.

21. Query 3 query = "SELECT * FROM bigdata WHERE bigdata.createdDate > '2024-10-20'" items = list( container.query_items ( query=query, enable_cross_partition_query =True )) for item in items: print(item) query = "SELECT * FROM bigdata WHERE bigdata.name = 'Mohit Kumar'" items = list( container.query_items ( query=query, enable_cross_partition_query =True )) for item in items: print(item) Query 4

22. Query 5 query = "SELECT VALUE COUNT(1) FROM bigdata" count = list( container.query_items ( query=query, enable_cross_partition_query =True )) print( f"Total number of items: {count[0]}")

23. Comparing Cosmos DB and MongoDB