Meeting/Event Information

Esteemed DAMA Philadelphia members, 

We'd like to welcome you back to our fall program as we explore how data modeling is relevant and necessary in a not-just-relational world.

Graph. Ledger.  Cloud-native. Document DB.  Key-Value.  In-memory.  Columnar.   Many persistence layer technologies are complementing traditional RDBMS schema-on-write systems. Does physical modeling take on a different shape; what additional considerations become relevant?  Do logical and conceptual models continue to play a role in documenting business concepts and common definitions?  Join our discussion on the latest strategies on applying our skills in this changing landscape!

We'd like to thank Alation for sponsoring this meeting, and KPMG for hosting

April Reeve has spent the last 25 years working as an enterprise architect and program manager. Now she is incorporating her enterprise data management knowledge as Director of Enterprise Information Strategy Architecture for Celgene Corporation, a bio-pharma in Summit, NJ.  April is an expert in multiple Data disciplines including Data Integration, Big Data, Data Conversion, Data Warehousing, Business Intelligence, Master Data Management, Document Management, and Data Governance. She is the author of “Managing Data In Motion: Best Practice Data Integration Techniques and Technologies”  

How is modeling in alternatives to Relational Database Management Systems different? How should we approach modeling when using Key/Value stores, Document Databases, or Graph Database?  During the 1900s and 2000s, relational databases became so ubiquitous that most programmers and organizations forgot there were other ways to store and manage data besides RDBMS. At the end of the 2000s, however, the large technology companies, like Google, Amazon, Ebay, etc., were
finding that relational databases were not a good solution for the huge amounts of data that needed to be managed on the internet: they were too slow for managing and analyzing massive amounts of data and too expensive. Hence, the rise in use of alternative, sometimes called "NoSQL”, database management systems, especially for analytics use cases.

This presentation will cover:
 A very short review of relational database modeling for transaction processing
 How the model changes for analytics: denormalization and dimensional modeling
 Key/value stores for VERY high-volume and very fast analytics and updates
 Document databases for repeating groups and keeping metadata with data
 Graph databases for hierarchies, relationships, and logistics (but not high volumes)