The requirements we have today are to accept any data, not just data in rows and columns; to accept that data at any speed, not just what a database can keep up with; and to support any process – not just queries but also algorithms and transformations. We aren't designing for 'big data' or 'small data' – it's all data. The data warehouse is sufficient for a portion of the data, but not for all of it.
What are the design principles that lead to good functional design and a workable data architecture? What are the assumptions that limit old approaches? How can one integrate with older environments? How does this affect data management? Answering these questions is key to building long-term infrastructure.
This presentation will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture. Our goal in most organizations is to build a multi-use data infrastructure that is not subject to past constraints.
Target Audience: BI and analytics leaders and managers; data architects, modelers, and designers; architects, designers, and implementers; anyone with data management responsibilities who is challenged by recent and upcoming changes in the data landscape
Prerequisites: Understanding of data warehousing and BI