Data Warehouse technology has become an accepted tool for business decision making and a core component to support reporting and analytics within the IT organization’s technology infrastructure. Most early data warehouses were focused on delivering integrated data to support desktop reporting. Today’s systems have expanded into centralized data hubs that support data acceptance and provisioning to numerous platforms (internal operational applications, external cloud platforms, reporting systems, and data marts). As business requirements have evolved and technical capabilities advanced, many of the age-old and accepted design methods and architectural approaches are no longer appropriate. Today’s multi-processor, parallel-enabled systems have been built to address many of the limitations of the past. Unfortunately, very few environments have evolved to capitalize on current day technology capabilities. And in many instances, folks aren’t aware that their basic design and architecture beliefs are out of date.
In this session, Evan Levy takes on some theSacred Cows of Data Warehousing. He’ll provide you the facts and detailsthat illustrate pros and cons of key data warehousing technologies, architectural approaches, and implementation methods. Thispoint/counterpoint view will supply you with the means of challengingor substantiating their adoption or deployment. Whether you need tolearn more about these key technologies, defend your position, or justifyadditional budget money, attend Evan’s irreverent session and learn how toposition some key solutions in your own organization.
The topics reviewed will include
- Traditional DW architecture approaches – their strengths and weaknesses
- Technology hype, folk lore, and reality
- Challenging age-old and modern day development methods
- Data architecture alternatives (academic views, blue sky ideas, real world reality)
- The distraction of technology components (storage, processing, the myths, the lies, and facts)
- The pros and cons methodology and architecture change
- Using common sense and numbers (and not emotion) to drive data warehouse direction