Data modeling is the process of documenting a complex software system design as an easily understood diagram, using text and symbols to represent the way data  needs to flow. The diagram can be used as a blueprint for the construction of new software or for re-engineering a legacy application. 

This exclusive 60-page guide explores why you shouldn’t get distracted by new DB technology, how Facebook is using a RDBMS to do the data slicing and dicing they can’t in Hadoop, and much more.

Traditionally, data models have been built during the analysis and design phases of a project to ensure that the requirements for a new application are fully understood. A data model can be thought of as a  flowchart that illustrates the relationships between data. Although capturing all the possible relationships in a data model can be very time-intensive, it's an important step that shouldn't be rushed. Well-documented   conceptual, logical and physical data models  allow stake-holders to identify errors and make changes before any programming code has been written.

Like unobservable entities, models have been the subject of debate between scientific realists and antirealists. One’s position often depends on what one considers the truth-bearers in science to be. Those who take fundamental laws and/or theories to be true believe that models are true in inverse proportion to the degree of idealization used. Highly idealized models would therefore be (in some sense) less true. Others take models to be true only insofar as they describe the behavior of empirically observable systems. This empiricism leads some to believe that models built from the bottom-up are realistic, while those derived in a top-down manner from abstract laws are not.

Models also play a key role in the semantic view of theories. What counts as a model on this approach, however, is more closely related to the sense of models in mathematical logic than in science itself.

The heavy emphasis here on models in the physical sciences has more to do with the interests of philosophers than scientific practice. Physical models are used throughout the sciences, from immunoglobulin models of allergic reactions to macroeconomic models of the business cycle.

The Model-View-Control (MVC) pattern, originally formulated in the late 1970s, is a software architecture pattern built on the basis of keeping the presentation of data separate from the methods that interact with the data. In theory, a well-developed MVC system should allow a front-end developer and a back-end developer to work on the same system without interfering, sharing, or editing files either party is working on.

Even though MVC was originally designed for personal computing, it has been adapted and is widely used by web developers due to its emphasis on separation of concerns, and thus indirectly, reusable code. The pattern encourages the development of modular systems, allowing developers to quickly update, add, or even remove functionality.

In this article, I will go the basic principles of MVC, a run through the definition of the pattern and a quick example of MVC in PHP. This is definitely a read for anyone who has never coding with MVC before or those wanting to brush up on previous MVC development skills.

Data modeling is the process of documenting a complex software system design as an easily understood diagram, using text and symbols to represent the way data  needs to flow. The diagram can be used as a blueprint for the construction of new software or for re-engineering a legacy application. 

This exclusive 60-page guide explores why you shouldn’t get distracted by new DB technology, how Facebook is using a RDBMS to do the data slicing and dicing they can’t in Hadoop, and much more.

Traditionally, data models have been built during the analysis and design phases of a project to ensure that the requirements for a new application are fully understood. A data model can be thought of as a  flowchart that illustrates the relationships between data. Although capturing all the possible relationships in a data model can be very time-intensive, it's an important step that shouldn't be rushed. Well-documented   conceptual, logical and physical data models  allow stake-holders to identify errors and make changes before any programming code has been written.

Data modeling is the process of documenting a complex software system design as an easily understood diagram, using text and symbols to represent the way data  needs to flow. The diagram can be used as a blueprint for the construction of new software or for re-engineering a legacy application. 

This exclusive 60-page guide explores why you shouldn’t get distracted by new DB technology, how Facebook is using a RDBMS to do the data slicing and dicing they can’t in Hadoop, and much more.

Traditionally, data models have been built during the analysis and design phases of a project to ensure that the requirements for a new application are fully understood. A data model can be thought of as a  flowchart that illustrates the relationships between data. Although capturing all the possible relationships in a data model can be very time-intensive, it's an important step that shouldn't be rushed. Well-documented   conceptual, logical and physical data models  allow stake-holders to identify errors and make changes before any programming code has been written.

Like unobservable entities, models have been the subject of debate between scientific realists and antirealists. One’s position often depends on what one considers the truth-bearers in science to be. Those who take fundamental laws and/or theories to be true believe that models are true in inverse proportion to the degree of idealization used. Highly idealized models would therefore be (in some sense) less true. Others take models to be true only insofar as they describe the behavior of empirically observable systems. This empiricism leads some to believe that models built from the bottom-up are realistic, while those derived in a top-down manner from abstract laws are not.

Models also play a key role in the semantic view of theories. What counts as a model on this approach, however, is more closely related to the sense of models in mathematical logic than in science itself.

The heavy emphasis here on models in the physical sciences has more to do with the interests of philosophers than scientific practice. Physical models are used throughout the sciences, from immunoglobulin models of allergic reactions to macroeconomic models of the business cycle.

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Model | Define Model at Dictionary.com

Posted by 2018 article

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