Description

Introduction of Cosmos DB for Data Engineers

This course is designed for data engineers looking to master Azure Cosmos DB, focusing on best practices for database design and querying. Participants will learn how to design scalable, high-performance Cosmos DB architectures, optimize queries, and ensure data consistency and reliability. By the end of the course, attendees will have the skills to efficiently work with Cosmos DB in data engineering environments, handling large datasets and complex queries with ease.

Prerequisites

• Basic Knowledge of NoSQL Databases: Familiarity with NoSQL concepts, including key-value, document, and column-family databases.
• Experience with Azure Cloud Services: Basic understanding of Azure and its services.
• SQL and Querying Skills: Comfortable with SQL or similar query languages, as this course delves into Cosmos DB’s querying capabilities.
• Fundamentals of Data Engineering: Understanding of data pipelines, data modeling, and data workflows.

Table of Contents

1. Introduction to Cosmos DB for Data Engineers
1.1. What is Cosmos DB and Why Data Engineers Use It
1.2. Understanding the NoSQL Model and Cosmos DB’s Multi-Model Architecture
1.3. Overview of Cosmos DB Data APIs: SQL, MongoDB, Cassandra, Gremlin, and Table
1.4. Data Consistency and Latency Considerations in Cosmos DB

2. Designing Cosmos DB for Data Engineering Workflows
2.1. Designing Scalable Cosmos DB Architectures for High Throughput
2.2. Choosing the Right Partition Key for Performance and Scalability
2.3. Data Modeling Strategies in Cosmos DB
2.4. Handling Multi-Tenant and Multi-Region Architectures

3. Cosmos DB Data Models and Best Practices
3.1. Document vs. Column-Family Models: When to Use Each
3.2. Designing Schemas for Efficient Querying
3.3. Optimizing Cosmos DB for High-Volume Data Ingestion
3.4. Data Aggregation and Transformation in Cosmos DB

4. Cosmos DB Querying Best Practices
4.1. Optimizing SQL Queries in Cosmos DB
4.2. Writing Efficient Queries with Partitioning and Indexing
4.3. Using Cosmos DB’s Analytical Store for Complex Queries
4.4. Query Performance Tuning: Identifying Bottlenecks

5. Managing Data Consistency and Availability
5.1. Cosmos DB Consistency Levels: Eventual, Strong, Bounded Staleness, Consistent Prefix
5.2. Choosing the Right Consistency Level for Your Application
5.3. Configuring Replication and Multi-Region Distribution for High Availability
5.4. Handling Failover and Disaster Recovery

6. Cosmos DB Indexing Strategies for Query Optimization
6.1. Overview of Cosmos DB’s Indexing Mechanism
6.2. Custom Indexing Policies: Creating, Updating, and Managing Indexes
6.3. Managing and Optimizing Default Indexing for Queries
6.4. Best Practices for Reducing Costs and Improving Query Speed

7. Performance Optimization Techniques
7.1. Query Optimization and Cost Management(Ref: Cosmos DB with .NET: Integrating Applications Seamlessly )
7.2. Best Practices for Request Units (RUs) and Throughput Management
7.3. Load Balancing Across Regions and Scaling Cosmos DB
7.4. Using Cosmos DB’s Autoscale and Provisioned Throughput Models

8. Cosmos DB Security Best Practices for Data Engineers
8.1. Implementing Role-Based Access Control (RBAC)
8.2. Secure Data Ingestion and Querying in Cosmos DB
8.3. Data Encryption: At Rest and In Transit
8.4. Managing and Auditing Access to Cosmos DB

9. Integrating Cosmos DB into Data Pipelines
9.1. Cosmos DB as a Source and Sink in ETL Pipelines
9.2. Using Cosmos DB for Stream Processing and Real-Time Analytics
9.3. Integrating Cosmos DB with Azure Data Factory and Azure Synapse Analytics
9.4. Automating Data Operations with Azure Functions and Logic Apps

10. Advanced Cosmos DB Querying Techniques
10.1. Handling Complex Queries with Joins and Aggregations
10.2. Using Cosmos DB for Geospatial Queries
10.3. Implementing Full-Text Search and Full-Text Indexing
10.4. Querying Across Multiple Cosmos DB Containers

11. Cosmos DB Troubleshooting for Data Engineers
11.1. Diagnosing Common Query and Performance Issues
11.2. Troubleshooting Partitioning and Scaling Issues
11.3. Managing Latency and Data Inconsistencies
11.4. Using Diagnostics and Monitoring Tools for Cosmos DB

12. Conclusion
12.1. Recap of Key Design and Querying Best Practices
12.2. Real-World Use Cases for Cosmos DB in Data Engineering
12.3. Future Trends in Cosmos DB and NoSQL Data Engineering
12.4. Next Steps in Mastering Cosmos DB for Advanced Data Workflows

Conclusion

By completing this course, data engineers will acquire the skills necessary to design and manage Cosmos DB databases optimized for data engineering tasks. They will be able to handle large-scale data workloads, optimize complex queries, and ensure performance and consistency across their Cosmos DB deployments. This knowledge will empower them to build scalable, efficient, and high-performance data engineering solutions in the cloud.

Reference