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Data Modeling Webcast Series

In this series of webcasts, we'll examine how prudent database design can improve cloud deployment performance and success all while potentially lowering monthly cloud costs.  Traditional “theoretical” relational concepts such as normalization will be presented in a non-academic fashion that shows real-world performance problems and their related costs.  For modern data warehousing and data analytics of very large data sets, database features to “squeeze performance out of the proverbial turnip” will be highlighted, particularly as they relate to the cloud. Moreover, we’ll discuss when and why to choose from among the key four NoSQL technologies as it relates to your first major database design decision. Finally, we’ll cover some of the cloud vendor-proprietary database offerings that you also might select again as your first major database design decision.

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A pedido

Feb.

16

Adding to the complexity is the fact that many cloud vendors offer their own proprietary databases in addition to the ones we all know. What challenges do these new types of repositories require for data modeling? View this on demand webcast and leave with a basic base of the many database alternatives and their data modeling ramifications as deployed in the cloud.

A pedido

Jan.

19

For data lakes and lakehouses, data modeling for both structured and unstructured data is novell and tricky. This introduction to some advanced NoSQL database design techniques and avoidance of common pitfalls is important. View this on-demand session to get a firm foundation for data lakes and lakehouses as they move to the cloud.

A pedido

Dec.

1

View this recording of a live webcast where we provide a firm foundation for data modeling of data marts and warehouses as they move to the cloud.

A pedido

Nov.

3

View this recording of a live webcast where we provided attendees with a solid foundation for data modeling of OLTP system as they move to the cloud.

Seleccione uno o más webcasts relevantes en los que desee participar antes de registrarse.

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