Content tagged with: data modeling
Data modelling is considered a staple in the world of data management. The skill of the data modeler and their knowledge of the business plays a large role in successful Enterprise Information Management across many organizations. Data modeling requires formal accountability, attention to metadata and getting the business heavily involved in data requirement development. These are all traits of solid Data Governance programs.
Data warehousing commonly implies complex data flows, either because of the large number of steps data transformations may consist of, or of the different types of data they carry. These issues rise interesting challenges concerning design-oriented modeling of data warehousing flows with UML.
Some databases have more complex requirements than do the more traditional applications. This led to the development of additional semantic data modeling concepts that were incorporated into conceptual data models such as the Entity-Relationship (ER) model.
This video introduces the viewer to the concept of conceptual data modeling and describes some of the details of the Entity-Relationship diagram (ERD).
This video discusses some of the data modeling issues encountered while transitioning from a relational database to NoSQL. Large data sets are driving adoption of NoSQL technologies and transitioning from relational persistence to NoSQL persistence is non-trivial.
This talk covers the key ideas of NOSQL databases, including motivating similarities and more importantly their different strengths and weaknesses. In more depth, we’ll focus on the characteristics of graph stores for connected data and the kinds of problems for which they are best suited.
This is a comparison of NoSQL databases from the data modeling point of view. There are many NoSQL system families, namely, Key-Value stores, BigTable-style databases, Document databases, Full Text Search Engines and Graph databases. He explains that NoSQL data modeling is typically driven by application-specific access patterns, i.e. types of queries to be supported. NoSQL data modeling often requires deeper understanding of data structures and algorithms than relational database modeling does.
Many applications are built around a single data model, typically persisted to a data store via an object-relational mapper (ORM) tool. Sometimes you want to have more flexibility, which requires multiple models. Understanding when one model of data just doesn’t fit all use cases is the challenge for the architect. This article discusses some strategies you can use to handle these situations and develop more layered and robust applications, using objects for data modeling.
This article proposes guidelines to create a conceptual (semantic or architectural) entity/relationship model using a Unified Modeling Language (UML) class diagrams.
This article discusses the different types and uses of data models, what they are good for, what the differences are between them. The author then analyzes the needs of an integration architecture and the special requirements it puts on a data model.