Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. The goal is to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes.
Data models are built around business needs. Rules and requirements are defined upfront through feedback from business stakeholders so they can be incorporated into the design of a new system or adapted in the iteration of an existing one.
Data can be modeled at various levels of abstraction. The process begins by collecting information about business requirements from stakeholders and end users. These business rules are then translated into data structures to formulate a concrete database design. A data model can be compared to a roadmap, an architect’s blueprint or any formal diagram that facilitates a deeper understanding of what is being designed.
Data modeling employs standardized schemas and formal techniques. This provides a common, consistent, and predictable way of defining and managing data resources across an organization, or even beyond.
Ideally, data models are living documents that evolve along with changing business needs. They play an important role in supporting business processes and planning IT architecture and strategy. Data models can be shared with vendors, partners, and/or industry peers.
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Like any design process, database and information system design begins at a high level of abstraction and becomes increasingly more concrete and specific. Data models can generally be divided into three categories, which vary according to their degree of abstraction. The process will start with a conceptual model, progress to a logical model and conclude with a physical model. Each type of data model is discussed in more detail in subsequent sections:
They are also referred to as domain models and offer a big-picture view of what the system will contain, how it will be organized, and which business rules are involved. Conceptual models are usually created as part of the process of gathering initial project requirements. Typically, they include entity classes (defining the types of things that are important for the business to represent in the data model), their characteristics and constraints, the relationships between them and relevant security and data integrity requirements. Any notation is typically simple.
They are less abstract and provide greater detail about the concepts and relationships in the domain under consideration. One of several formal data modeling notation systems is followed. These indicate data attributes, such as data types and their corresponding lengths, and show the relationships among entities. Logical data models don’t specify any technical system requirements. This stage is frequently omitted in agile or DevOps practices. Logical data models can be useful in highly procedural implementation environments, or for projects that are data-oriented by nature, such as data warehouse design or reporting system development.
They provide a schema for how the data will be physically stored within a database. As such, they’re the least abstract of all. They offer a finalized design that can be implemented as a relational database, including associative tables that illustrate the relationships among entities as well as the primary keys and foreign keys that will be used to maintain those relationships. Physical data models can include database management system (DBMS)-specific properties, including performance tuning.
As a discipline, data modeling invites stakeholders to evaluate data processing and storage in painstaking detail. Data modeling techniques have different conventions that dictate which symbols are used to represent the data, how models are laid out, and how business requirements are conveyed. All approaches provide formalized workflows that include a sequence of tasks to be performed in an iterative manner. Those workflows generally look like this:
Data modeling has evolved alongside database management systems, with model types increasing in complexity as businesses' data storage needs have grown. Here are several model types:
Relational databases frequently employ structured query language (SQL) for data management. These databases work well for maintaining data integrity and minimizing redundancy. They’re often used in point-of-sale systems, as well as for other types of transaction processing.
Two popular dimensional data models are the star schema, in which data is organized into facts (measurable items) and dimensions (reference information), where each fact is surrounded by its associated dimensions in a star-like pattern. The other is the snowflake schema, which resembles the star schema but includes additional layers of associated dimensions, making the branching pattern more complex.
Data modeling makes it easier for developers, data architects, business analysts, and other stakeholders to view and understand relationships among the data in a database or data warehouse. In addition, it can:
Numerous commercial and open source computer-aided software engineering (CASE) solutions are widely used today, including multiple data modeling, diagramming and visualization tools. Here are several examples:
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