DP-203: Data Engineering on Microsoft Azure

DP-203: Data Engineering on Microsoft Azure

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Introduction

  • 1. How to keep using Azure Portal for FREE after 12 months?
    5m

Design Azure Data Storage Solutions (40-45%)

  • 1. SECTION 1 - Introduction and overview
    4m

Recommend an Azure data storage solution based on requirements

  • 1. Learning Objectives
    2m
  • 2. Data Types
    12m
  • 3. Data Storage Types
    18m
  • 4. Select Azure Store for application
    4m
  • 5. Azure Data Platform Architecture
    6m
  • 6. Design and Troubleshoot Partition Distribution type
    6m
  • 7. Learning Outcome and Checklist
    3m

Design non-relational cloud data stores

  • 1. Learning Objectives
    3m
  • 2. What is partitioning and partition key
    6m
  • 3. Dedicated vs Shared throughput
    7m
  • 4. Avoiding hot partition
    5m
  • 5. Single partition vs Cross partition
    4m
  • 6. Composite Key
    3m
  • 7. Partition key best practice
    8m
  • 8. Globally Distribution
    13m
  • 9. Multi Master
    9m
  • 10. Manual vs Automatics Failover
    8m
  • 11. Throughput and request units
    9m
  • 12. Cosmos DB - 5 consistent levels
    14m
  • 13. Cosmos DB Multi Model 5 APIs
    12m
  • 14. How Data Lake Gen 2 Evolved?
    5m
  • 15. Data Lake vs Blob
    5m
  • 16. High Availability vs Disaster Recovery
    7m
  • 17. RTO and RPO
    4m
  • 18. Azure Storage - HA and DR Options
    12m
  • 19. Cosmos DB - HA and DR Options
    17m
  • 20. Scenarios - Designing a solution for CosmosDB vs Data Lake vs Blob Storage
    10m
  • 21. Learning Outcome and Checklist
    3m

Design relational cloud data stores

  • 1. Learning Objectives
    4m
  • 2. Purchasing models and Service Tier
    13m
  • 3. Storage and Sharding patterns
    5m
  • 4. Data Distribution and Distributing Keys
    6m
  • 5. Data Types and Table Types
    5m
  • 6. Partitioning
    5m
  • 7. Scaling Azure Database
    11m
  • 8. Scaling Azure Datawarehouse
    3m
  • 9. Azure SQL Database High Availability and Disaster Recovery options
    29m
  • 10. Azure SQL Database Backup and Restore
    19m
  • 11. Azure SQL Datawarehouse Backup and Restore
    11m
  • 12. Azure SQL Database vs Data Warehouse
    3m
  • 13. Scenarios - Designing for SQL Database vs Data warehouse
    6m
  • 14. Learning Outcome and Checklist
    6m

Design Data Processing Solutions (25-30%)

  • 1. SECTION 2 - Introduction and Overview
    3m

Design batch processing solutions

  • 1. Learning Objectives
    4m
  • 2. Design Batch Processing Solutions using Data Factory and DataBricks
    17m
  • 3. Data Ingestion Methods
    13m
  • 4. Tools to Ingest Data
    11m
  • 5. Ingest using Portal and SE
    9m
  • 6. Demo: Ingest Data using azcopy
    6m
  • 7. Demo: Blob to Gen 2
    9m
  • 8. Demo: SQL Server to Gen2
    14m
  • 9. Demo: Amazon S3 to Gen2
    8m
  • 10. Learning Outcome and Checklist
    3m

Design real-time processing solutions

  • 1. Learning Objectives
    2m
  • 2. Real Time Processing
    12m
  • 3. Azure Streaming Analytics Service
    6m
  • 4. Streaming Analytics - What is time windowing
    2m
  • 5. Tumbling Window
    3m
  • 6. Hopping Window
    2m
  • 7. Sliding Window
    2m
  • 8. Session Window
    6m
  • 9. Design and Provision Compute Resources
    9m
  • 10. Lambda Architecture
    10m
  • 11. Learning Outcome and Checklist
    4m

Design for Data Security and Compliance (25-30%)

  • 1. SECTION 3 - Introduction and Overview
    2m

Design security for source data access

  • 1. Learning Objectives
    2m
  • 2. Plan for Secure Endpoints (Public/Private)
    7m
  • 3. Access Keys
    3m
  • 4. Shared Access Signature (SAS)
    7m
  • 5. Active Directory (Azure AD)
    5m
  • 6. Role Based Access Control (RBAC)
    6m
  • 7. Learning Outcome and Checklist
    2m

Design security for data policies and standards

  • 1. Learning Objectives
    2m
  • 2. Encrypt data at rest and in transit
    26m
  • 3. Data Auditing
    4m
  • 4. Data Masking
    21m
  • 5. Data Privacy and Data Classification
    7m
  • 6. Data Retention and Archiving Strategy
    14m
  • 7. Learning Outcome and Checklist
    4m