Cloud Data Warehousing

Cloud data warehousing helps to meet the challenges of legacy data warehouses that struggle to keep up with growing data volumes, changing service level expectations, and the need to integrate structured warehouse data with unstructured data in a data lake. Cloud data warehousing provides many benefits, but cloud migration isn’t fast and easy. Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema or rebuilding of data pipelines is needed.

This session describes the benefits, techniques, and challenges of migrating an existing data warehouse to the cloud. We’ll overview the major cloud data warehouse providers and examine some technologies that can ease the pain of cloud migration. Migration pragmatics are described within the framework of a step-by-step approach.

Moving an existing data warehouse to the cloud is not quick, and it isn’t easy, but there are real and substantial benefits. Most companies avoid the “big bang” and migrate incrementally. The techniques and technologies described here will help you to start quickly and navigate through the migration roadmap smoothly.

This is a participative workshop. Participants should arrive with knowledge of one or more on-premises data warehouses that they are considering moving to the cloud. You’ll perform exercises to analyze the upside and downside of cloud migration, to make migration decisions, and to develop a migration plan.

Learning objectives

• The what and why of cloud data warehousing
• The benefits of cloud data warehousing
• The challenges of cloud data warehousing
• Cautions about cloud migration
• How to perform migration analysis and make migration decisions
• A step-by-step approach to data warehouse migration
• Considerations for incremental migration
• The cloud data warehousing technology landscape
• The cloud migration technology landscape
• How to develop a cloud migration plan

Who is It For?

Data management and analytics program and project managers, Data management and analytics architects, Technology strategists and planners, Data engineers and data integration specialists, Anyone with a role in migrating legacy data warehouses to the cloud.

Course Outline

Cloud Data Warehousing – What and Why?

• The cloud migration trend
• Challenges of conventional data warehousing
• Overcoming the challenges

Benefits of Cloud Data Warehousing

• Scalability
• Elasticity
• Managed infrastructure
• Cost savings
• Processing speed
• Deployment speed
• Fault tolerance
• Disaster recovery
• Security and governance
• RDBMS in the cloud

Cloud Data Warehousing: Look Before You Leap

• Data movement
• Design and development
• Security and compliance
• Cloud and on-premises hybrid
• Portability

To Cloud or Not to Cloud

• Data warehouse architecture
• Technical debt
• Technology architecture
• Networking
• Data warehouse operations
• Data center operations
• Disaster recovery and business continuity
• Scalability and elasticity
• Data types
• Agility
• Governance
• Security
• Compliance
• Data quality
• Self-service
• Commitment

Cloud Migration SWOT Analysis

• Some guidelines
• Exercise 1 – SWOT for Migrating Your Data Warehouse
• Exercise 2 – Using the Migration SWOT Analysis
• Exercise 3 – SWOT for Not Migrating Your Data Warehouse
• Exercise 4 – Using the No Migration SWOT Analysis
• Exercise 5 – To Cloud or Not to Cloud: Making the Decision

Technologies for Cloud Data Warehousing

• Cloud data warehouse functions
  • Data storage
  • Database loading
  • Data processing
  • SQL queries
  • Workload management
  • Metadata management
  • Security and access controls
  • Data protection
• Cloud data warehouse platforms
• Migration tools
  • Integration Platform as a Service (iPaaS)
  • Data warehouse automation
  • Data virtualization

Step-by-Step Data Warehouse Migration

• The big picture
• The business case
• Architectural assessment
• Migration strategy
• Technology selection
• Migration
  • Planning
  • Schema migration
  • Data migration
  • ETL migration and data pipelines
  • Metadata migration
  • Migrating users and applications