Data Warehouse Modernization is implemented by Cloud Data Architects, Cloud Engagement Managers and Cloud Data Engineers in a five phase approach.

Agenda

Kickoff (onsite)

• Intensive discovery with Data / Cloud experts working to uncover business priorities, success drivers, and pain points.

Discovery and Assessment (onsite/remote)

• Analysis of the current data landscape and architecture; and the technical aspects of the data warehouse to be modernized - Workloads, Schemas, & Data to create the migration plan and get sign off.

Migration (onsite/remote)

• Schema conversion (using SCT or similar tool), data loading, remediate stored procedures & functions, repoint/source/sink application to new data warehouse.

Testing, final sync, and cutover (onsite/remote)

• ETL pipeline validation, functional and non-functional testing, run catch-up sync, run final UAT, and cutover to new production systems.

Closure (onsite)

• Meet at customers office to present the results of the migration along with any related consultative guidance.

Deliverables

1. Detailed inventory spreadsheet for workloads, database, and tables (Spreadsheet)

• Workload dependencies with Data Sources, Database, ETL pipelines and data models

• 5-Years Estimated Run Costs

• Main Shared Services costs assumption

2. Final Report (Document)

• Definition of migration strategies and criteria used

• Assessment for databases & workloads

• Run Costs estimates (5-years TCO & Managed Services)

• Results of migration to Cloud data warehouse, functional and stress testing

• High-Level view of the migration overall

3. Executive Summary (Slide Deck)

• Summary of migration strategies considered, decisions criteria and assumptions taken

• Migration strategies stats for Data Warehouses & applications components

• Run costs and Managed Services costs estimates