DP-203: Data Engineering on Microsoft Azure

Best Seller 81 Lectures 10h 17m
Prepare for your Microsoft examination with our training course. The DP-203 course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Microsoft certification exam. Pass the Microsoft DP-203 test with flying colors.
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Curriculum For This Course
  • 1. SECTION 1 - Introduction and overview 4m
  • 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
  • 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
  • 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
  • 1. SECTION 2 - Introduction and Overview 3m
  • 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
  • 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
  • 1. SECTION 3 - Introduction and Overview 2m
  • 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
  • 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

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