Description

Introduction of Cosmos DB &Graph API

This course explores the integration of Cosmos DB with the Graph API, focusing on the concepts and techniques of graph data modeling. Participants will learn how to model complex relationships and efficiently store, query, and analyze graph-based data in Cosmos DB. The course covers practical use cases, graph database design, and best practices for leveraging Cosmos DB’s Gremlin API for graph queries.

Prerequisites

• Basic Knowledge of Cosmos DB: Familiarity with Cosmos DB and its core features.
• Understanding of NoSQL Databases: General knowledge of NoSQL database concepts.
• Graph Theory Basics: Understanding of graph concepts, including nodes, edges, and properties.
• Experience with Gremlin Query Language: Familiarity with Gremlin, the graph traversal language used by Cosmos DB.

Table of Contents

1. Introduction to Cosmos DB and Graph Data Modeling
1.1. What is a Graph Database?
1.2. Key Concepts: Nodes, Edges, and Properties
1.3. Introduction to Cosmos DB’s Gremlin API
1.4. Comparing Graph Data Models with Other Database Models

2. Setting Up Cosmos DB for Graph Data
2.1. Creating a Cosmos DB Account with Gremlin API
2.2. Configuring Cosmos DB for Graph Data Storage
2.3. Understanding Partitioning and Scalability in Graph Data
2.4. Choosing Between Gremlin and SQL API for Graph Data

3. Designing a Graph Data Model
3.1. Identifying Graph Entities and Relationships
3.2. Structuring Nodes and Edges for Complex Data
3.3. Modeling Real-World Scenarios: Social Networks, Recommendations, and IoT
3.4. Data Consistency and Relationships in Graph Modeling

4. Working with Gremlin API in Cosmos DB
4.1. Overview of Gremlin Query Language
4.2. Basic Gremlin Queries: Traversals, Filters, and Projections
4.3. Advanced Graph Queries: Aggregations, Pattern Matching, and Pathfinding
4.4. Gremlin Query Performance Tuning and Optimization

5. Inserting and Managing Graph Data in Cosmos DB
5.1. Inserting Nodes and Edges Programmatically
5.2. Managing Large-Scale Graph Data
5.3. Updating and Deleting Graph Elements
5.4. Handling Data Integrity and Constraints in Graph Models

6. Querying Graph Data in Cosmos DB
6.1. Traversing Graphs: Basic and Advanced Queries
6.2. Using Filters and Projections to Extract Graph Data
6.3. Real-Time Queries for Graph Analytics
6.4. Using Cosmos DB’s Gremlin API for Graph Analytics

7. Performance Optimization for Graph Queries
7.1. Understanding Graph Query Performance Factors
7.2. Best Practices for Indexing Graph Data
7.3. Optimizing Gremlin Queries for Scalability
7.4. Query Execution Plan Analysis and Tuning

8. Advanced Graph Data Modeling Techniques
8.1. Hierarchical Graph Models for Multi-Level Data
8.2. Handling Cyclic Graphs and Complex Relationships
8.3. Using Graph Algorithms for Analysis: Centrality, Shortest Path, etc.
8.4. Integrating Graph Models with Other Data Sources and APIs

9. Use Cases for Cosmos DB Graph API
9.1. Social Media and Network Graphs
9.2. Recommendation Engines and Graph-Based Search
9.3. Fraud Detection and Anomaly Detection with Graphs
9.4. IoT Networks and Device Relationships

10. Security and Compliance in Cosmos DB Graph API
10.1. Managing Access Control with Azure Active Directory(Ref: Cosmos DB with Python: Developing Modern Applications)
10.2. Securing Graph Data with Role-Based Access Control (RBAC)
10.3. Data Privacy and Compliance Considerations in Graph Databases
10.4. Best Practices for Securing Graph Data in Cosmos DB

11. Integrating Cosmos DB Graph Data with Other Applications
11.1. Using Cosmos DB’s Graph Data with Azure Functions and Logic Apps
11.2. Visualizing Graph Data with Power BI
11.3. Integrating Graph Data into Web and Mobile Applications
11.4. Advanced Integrations with Machine Learning and AI

12. Conclusion of Cosmos DB &Graph API
12.1. Recap of Key Graph Data Modeling Concepts
12.2. Best Practices for Cosmos DB Graph Development
12.3. Future Trends in Graph Databases and Cosmos DB
12.4. Next Steps for Further Learning and Exploration

Conclusion

Upon completion of this course, participants will have a solid understanding of how to model, store, and query graph data using Cosmos DB’s Gremlin API. They will be equipped with the skills to implement graph data solutions for complex real-world scenarios, optimize performance, and integrate graph models with other applications. This knowledge will empower them to leverage the power of graph databases in building scalable, high-performance data-driven applications on Azure.

Reference