In this presentation I guide different options Microsoft Azure provides to develop a rich data solution. This was demonstrated to students of University of Ruhuna, Sri Lanka

1. Building data solutions in Cloud
Dinusha Kumarasiri

2. About Me
 Senior Solution Architect at LB Finance PLC
 Former Microsoft MVP
 Microsoft Certified Trainer (MCT)
 Cloud Enthusiast
 Love to share what I know

3. Workshop Agenda
 Introduction to cloud computing
 Cloud service models
 Data solutions
 Azure App Service
 Azure Cognitive Services
 Building Cloud Native applications with Azure

4. Agenda for Today
 Building data solutions
 Working with unstructured data
 Working with structured data
 Working with semi-structured data

5. Building data solutions

6. Building data solutions in Cloud

7. Storage accounts
 To contain all Azure data objects
Points to consider
 Redundancy levels
 LRS, ZRS, GRS, GZRS, RA-GRS, RA-GZRS
 Performance tiers / Lifecycle management
 Hot, Cold, Archive
 Security
 Access keys, Shared access signature, RBAC
 Manage
 Portal, CLI, PowerShell, REST API, Azure Storage Explorer

8. Unstructured data – Blob Storage
 Scalable and secure object storage in cloud
 Block Blob
 Page Blob
 Append Blob
 REST API
https://contosouor.blob.core.windows.net/vehi
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9. Unstructured data - Files
 Managed file shares in the cloud
 Supports common protocols (NFS, SMB)
 Synchronize with on-premises file shares (Azure File Sync)

10. Unstructured data - Queues
 Durable queues for large-volume cloud services
 To decouple application components with asynchronous message queueing
 Improve resiliency of the total solution
 Highly scalable to improve the elasticity of solution

11. Structured data – Azure SQL
 Relational database service build for Cloud
 Fully managed PaaS offering
 Highly scalable with auto scale

12. Semi-structured data - Table
 NoSQL key/value store with a schemaless design
 Fully managed PaaS offering
 Suitable for applications that require a flexible schema
 Performs OData-based queries
 Highly scalable

13. Semi-structured data – Cosmos DB
 NoSQL database service with open-source API support and varying
consistency models
 Open-source API support
 SQL/ Mongo DB (Document storage)
 Cassandra (Column storage)
 Table (Key/Value storage)
 Gremlin (Graph storage)
 Allows multi-region writes
 Varying consistency models
 Strong consistency
 Session consistency
 Eventual consistency

14. dinushaonline.blogspot.com @kumarasiri048 dinushak Dinusha Kumarasiri