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1. CENTRAL UNIVERSITY OF SOUTH BIHAR Microsoft u ses Cosmos DB 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 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 cosmos dB's history 2010 – Origin as "Project Florence" Developed internally to store large-scale unstructured data for Microsoft services. 2014 – Launch as Document DB Initially released as Document DB, 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.

6. Why we need ? Rapid Scaling & Low-Latency Multi-Model Support High Performance & Availability Flexible Consistency Models Elastic Scaling Automatic Partitioning Change Data Capture (CDC) Cost-Effective for Serverless Apps SLAs for Mission-Critical Workloads

7. Key Features of Azure Cosmos DB Globally Distributed Linearly Scalable Schema-Agnostic Indexing Multi-Model Database Multi-API & Multi-Language Support Multi-Consistency Support Automatic Indexing High Availability Guaranteed Low Latency Multi-Master Support

8. Architecture of Azure Cosmos DB

9. DATA STORED

10. Retail Marketing

11. Storage Mechanism

12. STORAGE MECHANISM Partitioning & Data Distribution Logical vs. Physical Partitions : Automatic Scaling : Global Distribution & Multi-Region Replication Request Units (RUs) & Throughput Scaling Encryption at Rest & in Transit Encryption at Rest : Encryption in Transit : Indexing & Storage Optimization Backup & Disaster Recovery

13. 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 = ‘bigdata24' container_name = ‘ bogdata ' database = client.get_database_client ( database_name ) container = database.get_container_client ( container_name ) items_to_insert = [ { 'id': '1', ' ItemID ': '101', 'name': ‘Mohit Kumar', 'description': 'Inserting first data item programmatically into Cosmos DB', ' createdDate ': '2024-10-24', 'status': 'in-progress' }, SOME EXAMPLES

14. { 'id': '2', ' ItemID ': '102', 'name': ‘Abhineet Kumar', 'description': 'Inserting second data item programmatically into Cosmos DB', ' createdDate ': '2024-10-23', 'status': 'completed' }, { 'id': '3', ' ItemID ': '103', 'name’: ‘Saurav Yadav', 'description': 'Inserting third data item programmatically into Cosmos DB', ' createdDate ': '2024-10-22', 'status': 'pending' },

15. { 'id': '4', ' ItemID ': '104', 'name': ‘Sonu Kumar , 'description': 'Inserting fourth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-21', 'status': 'in-progress' }, { 'id': '5', ' ItemID ': '105', 'name': ‘ Saquib Sabir', 'description': 'Inserting fifth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-20', 'status': 'completed' },

16. { 'id': '6', ' ItemID ': '106', 'name': ‘Pooja Kumari', 'description': 'Inserting sixth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-19', 'status': 'pending' }, { 'id': '7', ' ItemID ': '107', 'name': ‘Pritty Kumari', 'description': 'Inserting seventh data item programmatically into Cosmos DB', ' createdDate ': '2024-10-18', 'status': 'in-progress' },

17. { 'id': '8', ' ItemID ': '108', 'name': ‘Rishi Yadav', 'description': 'Inserting eighth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-17', 'status': 'completed' }, { 'id': '9', ' ItemID ': '109', 'name': ‘Deepali', 'description': 'Inserting ninth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-16', 'status': 'pending' },

18. { 'id': '10', ' ItemID ': '110', 'name’: ‘Sunny Kumari', 'description': 'Inserting tenth data item programmatically into Cosmos DB', ' createdDate ': '2024-10-16', 'status': 'in-progress' } ] # Insert each document into the container for bigdata in items_to_insert : container.create_item (body=bigdata) print("10 unique items inserted successfully.")

19. query = "SELECT * FROM bigdata" items = list( container.query_items ( query=query, enable_cross_partition_query =True )) for item in items: print(item) 1. Query {/} 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) 2. Query {/}

20. 3.Query {/} 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) 4.Query {/}

21. 5.Query {/} 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]}")

22. Category Azure Cosmos DB MongoDB Provider Managed service by Microsoft Azure. Open-source database developed by MongoDB Inc. Database Type Globally distributed, multi-model NoSQL database. Document-oriented NoSQL database. API Compatibility Supports multiple APIs: SQL, MongoDB, Cassandra, Gremlin (graph), Table APIs. Primarily supports its own MongoDB API (document model). Scalability Elastic scaling of throughput and storage globally, with auto-scaling options. Scalability achieved through sharding but requires manual setup and configuration. MongoDB Atlas offers auto-scaling. Performance Sub-10ms latency with automatic indexing of all data and low-latency reads/writes globally. Can achieve low latency with proper indexing and architecture but may require optimization for large datasets. Data Models Multi-model: Supports key-value, document, graph, and columnar data models. Document-based model using BSON (Binary JSON) format. Pricing Model Pay-as-you-go based on throughput (RU/s) and storage; offers per-request pricing. Pricing depends on the number of instances and resources in use. MongoDB Atlas offers pay-as-you-go pricing. Replication Automatic multi-master replication across regions, with failover support. Replica sets are available, but global replication requires additional setup. Security Offers encryption at rest, role-based access control, VNET, and private link support. Encryption, access control, and security features available, but configuration depends on the environment (e.g., Atlas vs. self-managed). Open-Source Proprietary, but APIs like MongoDB, Cassandra, and others are open-source compatible. Completely open-source (with MongoDB Inc.'s cloud offering, Atlas, available for managed services). Query Language Cosmos DB SQL-like query language (if using SQL API), supports MongoDB queries if using the Mongo API. MongoDB Query Language (MQL) for querying data.

23. Advantages of Cosmos DB: Disadvantages of Cosmos DB: Cost: Can become expensive at scale. Learning Curve: Complexity in consistency models and scaling. Complex Querying: Limited compared to traditional SQL databases. Vendor Lock-in: Difficult to migrate to other cloud providers. Limited Relational Support: Not ideal for complex relational data. Manual Indexing: May require custom indexing in some cases. Smaller Community: Less support compared to popular databases like MySQL. Flexible Consistency Models: Offers 5 consistency levels to balance performance and consistency. Global Distribution: Replicate data across regions for low-latency and high availability. Multi-Model Support: Supports document, key-value, graph, and column-family models. Automatic Scaling: Scales throughput automatically based on demand. Low Latency: Millisecond read and write latencies. Elastic Throughput: Scales resources efficiently. High Availability: 99.99% uptime SLA. Fully Managed: No need to handle infrastructure, scaling, or patches. Azure Integration: Works seamlessly with other Azure services.

24. Conclusion Azure Cosmos DB is a powerful, globally distributed, and fully managed NoSQL database service ideal for applications requiring low-latency access, automatic scaling, and multi-model flexibility. Its strengths lie in global distribution, high availability, and seamless integration with the Azure ecosystem. However, it comes with challenges like a steeper learning curve, higher costs at scale, and limited relational data handling. Cosmos DB is a great choice for large-scale, globally distributed applications that need high performance and flexibility but may not be the best fit for smaller projects or those deeply reliant on traditional relational data models.