Azure for Architects: Design a Storage Strategy

Introduction

• Data storage in Azure
• What you should know

1. Recommend a Data Storage Solution

• Understanding types of data
• Features of relational data
• Solutions for relational data on Azure
• Features of semistructured data
• Solutions for semistructured data on Azure
• Features of unstructured data
• Solutions for unstructured data on Azure

2. Designing for Unstructured Data in Azure

• Unstructured data and Azure Storage
• Considerations for data redundancy and Azure Storage
• Authorizing access to data in Azure Storage
• Network security options for Azure Storage
• Encryption and secure data transfer for Azure Storage
• Access tiers and rehydration for Blobs in Azure Storage
• Lifecycle management for Blobs in Azure Storage
• Data protection for Blobs in Azure Storage
• Immutable storage for Blobs in Azure Storage
• Considerations for Azure Files

3. Designing for Structured Relational Data in Azure

• Introduction to Azure SQL DB and Azure SQL Managed Instance (MI)
• Purchasing models for Azure SQL DB and SQL MI
• Azure SQL DB and SQL MI service tiers
• Managing pre-provisioned compute for Azure SQL DB and SQL MI
• Serverless and Hyperscale compute tier for Azure SQL DB
• Scaling options for Azure SQL DB and SQL MI
• Encryption at rest and in transit on Azure SQL DB and SQL MI
• Always Encrypted for Azure SQL DB and SQL MI
• Dynamic data masking for Azure SQL DB and SQL MI
• Data discovery, classification, and audit for Azure SQL DB and SQL MI
• High availability and business continuity for Azure SQL DB and SQL MI
• Performance monitoring and tuning for Azure SQL DB and SQL MI
• IaaS for relational data with Azure SQL VM
• Azure SQL VM Agent for SQL on Azure VMs
• Azure SQL at the edge

4. Designing for Semistructured Data in Azure

• Introduction to Cosmos DB and Azure Tables
• The elements of Azure Cosmos DB and choosing an API
• Understanding options for Azure Cosmos DB consistency model
• Capacity management for Azure Cosmos DB
• Partitioning data in Azure Cosmos DB

5. Designing for Data Integration in Azure

• Considerations for data integration
• Design storage for big data analytics
• Design for ETL/ELT and data orchestration
• Design for SQL Analysis, data warehousing, and integrated BI
• Design for stream processing, data science, and ML

Conclusion

• Learning more about Azure data storage