Data Modeling for the Business
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It would have been too confusing to see the entire house in a single diagram, so he broke up the diagram by floor: one for the main floor, one for the second floor, and another for the attic. He showed me the diagram for the main floor, a subset shown in Figure 1. This gave me a much better sense of the details of the house.
I wasn't initially sure what some of the symbols meant, but it didn't take me long to figure out that a slanted line meant that a door was opening in a certain direction and that double lines meant a window opening, etc. I got the basic idea, although I'm sure the builders would get much more from it than I did. I was able to understand it well enough to change some of the mistakes that I saw, too. Once I saw everything laid out in the picture, I could get a better sense of how things fit together and the relationships between them.
For example, I didn't want the master bedroom opening into the kitchen. I should have been clearer on these types of rules before, but a I wasn't able to articulate some things clearly enough to the architect and b I didn't realize there were mistakes until I saw them drawn out. Once I saw them in this picture, I was able to correct them easily. The picture was an excellent medium for communicating what the architect envisioned based on what I had told him and for me to identify and correct misunderstandings. The architect then showed me the various wiring and plumbing diagrams, subset shown in Figure 1.
These were much too technical for me to understand, but I'm glad somebody was taking care of this stuff. I definitely want the electricity to work, but don't bother me with the details.
In this house example, we used several layers of diagrams: a very high-level picture to align on the scope of the project Figure 1. Each level of the blueprint has several components broken down by a particular function: a picture of the front of the house vs. Each level has a particular audience, owner and purpose. In the high-level diagrams, I was even able to do some of the design myself. As we got more detailed, however, I needed the expertise of an architect to fill out the structural specifications. Once we got to the physical layer, both the architect and I needed to bring in a technical contractor to build the diagrams.
The same holds true for data models. We'll demonstrate some of the parallels in the rest of this chapter. The chapters that follow will go into much greater detail on the different levels of data models and the high-level data model in particular, but to summarize, data modeling traditionally starts with a very high-level diagram to align on scope and common meaning, then a high-level picture to help gather business requirements and clarify understanding of basic concepts.
The logical level follows, showing more detail while incorporating business logic and business rules.
The physical level shows the technical details for implementation as a database or data structure. This 'top-down' approach of starting with a very high-level design and moving successively into more detail is one way of looking at data design in an organization. Because we're often building on top of existing systems, it's more common to start from the 'bottom-up', similar to trying to visualize what the house is supposed to look like when only the physical wiring diagrams are shown. We'll go into more detail on various approaches to data design in Chapter 8 with top-down, bottom-up, and hybrid approaches.
For now, suffice it to say that the real world is rarely as well-organized as we're describing here. We hope this book will help change that, at least for data management.
World peace and an organized sock drawer come next. Now, let's walk through an example of each of these levels with a corresponding example for both house design and data design. We started our house example by using a very high-level picture to describe the scope and basic requirements. I needed to clarify to my architect what I meant by 'house', what this house was going to be used for, and what was going to be included in the project.
In a data model, we may use a picture containing a simple set of boxes that clarify the differences between, for example, a primary home and a vacation home. The house diagrams were created by the architect and me, a layperson.
Data Modeling: Ensuring Data You Can Trust - Talend
Similarly, in the data world, both a business person and a data architect would work together on the data model diagrams, with business people able to do much of the work themselves. After we've reached consensus on what the scope of the project is, it's time to go deeper into the details of the design. For my house example, the architect drew a high-level picture to show what he had in mind for the house — that it would have a front porch, be big enough to sleep a large number of people, etc.
For data, we use a high-level data model to clarify what information is important, how basic concepts are defined and how these concepts relate to each other. In Figure 1. There is a textual description of a house and we also show how the concept of house relates to other concepts I had in mind. For example, a house isn't a 'house' to me unless it has a front porch; and it must have multiple bedrooms.
We'll go into more detail regarding the exact notation for these models in Chapter 3, but for now, you should be able to understand what this model is trying to express. Note again the roles involved in creating this level of diagram. I was collaborating closely with the architect, contributing content and examples. He might have guided me in this process, but I was heavily involved, so I felt a greater sense of ownership in having designed 'my' house.
Once we've agreed on what a 'house' is, and how big of a project we have on our hands, we're ready to go into more detail. Click to learn more about author Thomas Frisendal.
The Complexities of Relational Data Modeling
I realized that I needed to know what the constituent parts of data models really are. Across the board, all platforms, all models etc.
Is there anything similar to atoms and the chemical bonds that enables the formation of molecules? My concerns were twofold: As part of […]. I took a tour again in the Data Modeling zone, trying to deconstruct the absolutely essential metadata, which data modelers cannot do without. Follow me on a quick tour of the Data Modeling evolution over time. Note that not all attributes are included for space-saving reasons. I like the explicitness of classic Chen modeling.
After Entity-Relationship modeling things took other directions leading the generic ER-diagrams in many styles.
Copyright c Clifford Heath. Note that the attributes have now been folded into the entities, which came to be called tables in the SQL sense. With the advent of SQL, the named relationships were not named anymore. Since foreign key relationships are constraints, and constraints may have names in most SQL implementations, it really is strange why this happened. From a business semantics perspective, this is a very sad loss of information. Before SQL there was the relational model, which was published by Dr. Ted Codd in Codd, E. Of the ACM 13, 6 June Functional dependency is originally a mathematical term, which indicates that something a concept is completely derived by way of a function from something that controls it and possibly many other concepts.
Is it describing the status of the Supplier or the City? What if it is depending functionally on CITY? We cannot see this since we just have the colocation as indication of dependencies. The example is used in several textbooks by Dr. Third normal form is considered the desired state to be in. Loosely stated it simply means that all attributes are functionally dependent on the primary key.
Graphical user interfaces came to dominate and they required advanced programming environments. Functionality like inheritance, sub-typing and instantiation helped programmers combat the complexities of highly interactive user dialogs. For many years, formal graphs have been part of mathematics and they are a well-researched area. In the Data Modeling community, however, graphs emerged remarkably late. The graph types of particular interest to data modelers are the directed graphs. This means that the relationships edges between nodes are directed. And that is a natural fit with the nature of semantics and, consequently, with the nature of data models which represent business semantics.
One of the great misconceptions of the Data Modeling community has been that relational databases are about relationships.
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You might consider this a 0th step to the process, but also one that should be revisited as a business or application evolves. A beginning modeler might prefer to skip the conceptual phase as unnecessary, while a manager might imagine that architects will seamlessly translate a business idea to a logical schema without input or discussion, but the conceptual model is the foundation for the entire process. To wit, this phase is independent of technological considerations because the business should drive the adoption and utilization of technologies. Concept modeling refines the original idea or purpose behind a project to its component business requirements and constraints.
Failing to make these definitions at the beginning incurs great risk of needing to revisit assumptions down the line. Even worse, there is a danger of passing along ambiguous or incorrect interpretations of entities and relationships, translating to erroneous data. As previously mentioned, a completed conceptual model is also a standalone communicable resource, potentially the only documentation which can be understood across an enterprise.
Conceptualization should always be a well-defined step in any new data modeling process.
here Again, logical modeling is the bridge between pure business requirements and technical constraints. It is the most important stage for validating compatibility between vision and technical possibility.
Before work on a physical model can even be considered, entity relationship diagrams ERDs must be carefully assembled and reviewed, and entities themselves discussed and classified into logical buckets uniquely identifiable entities with unique primary keys, child entities and their relational connection to parents, etc. Though this step is still removed from the pure technicalities which engineers will encode in implementation, it explores and validates the bare feasibility of organizing the data previously determined as a business requirement.
Logical barriers and ideas incompatible with structured data storage must be discovered and resolved here. The physical model synthesizes the result of all discussions while also creating the most detailed specifications for a data storage project. Here, the focus has moved from abstract perspective and prioritization of concepts to a fully-fledged set of technical guidelines. The specific objects and the technologies containing and serving them should be codified. Your physical schema must be an engineering reference that is complete, maximally specific, yet navigable.