One of the main building blocks of all data models. This is a characteristic of an entity or object. It has a name and a data type.
In a relation or table, these are the columns, and they help to tie separate relations together.
In the file system, this would be referred to as a field.
These are also defined in the entity relationship model. Although not clearly defined in the diagram, when designing the database, you would list which of these each entity would have.
In the object-oriented data model, these describe the properties of objects.
These are easily defined using properly written business rules.
When naming these, you should use a term descriptive of the data contained within. Generally, these names are prefaced with either the name of the entity, or an abbreviation for the entity that it describes.
For example, when describing a customer's credit limit, the entity name might be CUSTOMER and the name of this might be CUS_CREDIT_LIMIT.
Proper naming conventions will help to make your data model self-documenting, as well as improve communication between the database designer, application programmer, and end user.
A movement to find new and better ways to manage large amounts of web-generated data and derive business insight from it, while simultaneously providing high performance and scalability at a reasonable cost.
According to a study by Gartner.com, the rapid growth of the amount of data in recent years is one of the biggest challenges facing companies today. Organizations are inundated with both structured and unstructured web data, including browsing patterns, purchasing histories, customer preferences, behavior patterns, and social media data from sites like Facebook or Twitter.
Because of this, this movement was created to manage and leverage a lot of converging trends (data growth, performance, scalability, and lower costs), and has led to NoSQL databases, as relational databases don't always match the needs of organizations with Big Data challenges.
It has stimulated the development of alternative ways to model, store, and manage data that represent a break with traditional data management.
A description of a policy, procedure, or principle within an organization. A brief, precise, and unambiguous description of a policy, procedure, or principle within a specific organization.
For example, a pilot cannot be on duty for more than 10 hours during a 24 hour period, or a professor may teach up to four classes during a semester.
In order to be effective, these must be easy to understand and widely disseminated, so that everyone within the organization shares the same interpretation of the rules.
These apply to any organization, no matter how large or small, that stores and uses data to generate information. In simple terms, they describe the main and distinguishing characteristics of the data as viewed by the company.
When beginning to build a data model, database designers consider what types of data exist in the organization, how the data are used, and in what time frame they are used. However, this information does not, by itself, yield a full understanding of the full business - that only comes from these, when they are properly defined.
When derived from a detailed description of an organization's operations, they help to create and enforce actions within that organization's environment. They must be put into writing and updated to reflect any changes in the organization's operational environment.
Properly written, these are used to easily define entities, attributes, relationships, and constraints, or they define them themselves.
The main sources of these are company managers, policy makers, department managers, and written documentation, such as the organization's procedures, standards, and operations manual.
These can also come from direct interviews with end users - however, these often lead to unreliable or inconsistent results, because different people have different perceptions of the policies. In the end, it pays to always verify the company's policies.
Verifying, identifying, and documenting an organization's rules pays off because it can help to standardize a company's view of data, it can serve as a communication tool between end users and designers, it allows designers to understand the nature, role, and scope of the data, it allows the designers to understand business processes, and it allows the designer to develop appropriate relationship participation rules and constraints and create an accurate data model.
As a general rule, nouns in these will equate to entities in the data model, and verbs will equate to relationships between those entities.
The output of the conceptual design process. It provides a global view of an entire database and describes the main data objects, avoiding details.
In other words, it integrates all external views (entities, relationships, constraints, and processes) into a single global view of the data in the enterprise.
A specific instance of this model is referred to as a conceptual schema.
This is the basis for the indentification and high-level description of the main data objects.
Effectively, this bird's-eye view of the data environment is a basic blueprint for the database.
It's independent of both the database software and hardware, meaning that if the software or hardware are changed, the conceptual model of the database doesn't have to be changed.
These are better suited for high level data modeling - they don't easily translate into an actual implementation of a database.
An example is the entity relationship data model, which is the most widely used conceptual model. However, some data models, such as the relational model and the object-oriented data model, could be used as both this and an implementation model.
A relatively simple representation, usually graphical, of more complex real-world data structures. This represents data structures and their characteristics, relations, constraints, transformations, and other constructs with the purpose of supporting a specific problem domain.
A representation, usually graphic, of a complex "real-world" data structure. They are used in the design phase of the Database Life Cycle.
An important thing to note is that this is merely an abstraction - you cannot draw the needed data from this. But, ideally, this should be a "blueprint" for how to build a database that meets all of the end user's needs.
Just as you can't build a good house without a good blueprint, you can't build a good database without a good data model.
Its evolution, from the early hierarchical and network models in the 1960s to the object/relational database management systems and NoSQL databases of today, have always been driven by the search to model and manage increasingly complex real-world data.
Throughout this evolution, several standards have come to be accepted. First, it has to have a conceptual simplicity, without compromising the completeness of the database. It doesn't make sense to have a data model that's harder to understand than the real world itself. However, at the same time, it has to be unambiguous and applicable to the problem domain.
Next, it must represent the real world as closely as possible. Throughout the different data model generations, this has been completed by adding more semantics, more terms and words, to the data model's data representations.
Each new generation of data models addressed the shortcomings of the previous generation.
This serves as a bridge between real world objects and the computer database. It also serves as a crucial communication piece between the database designer, the application programmer, and the end user. With this communication, everyone can understand better the organization's data, what it's used for, and how it should be implemented.
There are four main building blocks - entities, attributes, relationships, and constraints. When considering these four building blocks, database designers generally start with gaining a thorough understanding of what types of data exist in an organization, how the data are used, and in what time frames they are used.
When naming objects within this, the designer should use unique and easily distinguishable names for entities, attributes, relationships, and constraints. These names should also be descriptive and terminology familiar to the end users.
Proper naming conventions can help to make these self documenting, as well as improve communication between the designer, application manager, and end user.
The first step in the database design process. This is the process of creating a specific data model for a determined problem area.
This is an iterative, progressive process. When done correctly, you will effectively have a blueprint for how to build a database that will meet all of the end user's requirements.
In this process, you are bridging the gap between real world objects and the computer database.
This can reduce the complexities of database design to more easily understood abstractions that define the database's entities and the relationships between them, and thus can help simplify the communication between database designers, programmers, and database end-users.
Typically, database designers use good judgment when doing this, which can lead to some issues. "Good judgment" is very subjective and usually is developed over the course of many trials and errors. At the end of the day, though, the designer should come up with something that meets all of the end user's requirements.
A person, place, thing, concept, or event in for which data can be stored.
One of the main building blocks for all data models.
It represents a particular type of object in the real world - in other words, it's distinguishable. For example, one such instance of CUSTOMER would be distinguishable from other instances by the customer's name or other attributes.
They can be physical objects, such as people, or abstractions, such as flight routes.
When referred to by name in a data model, the names are CAPITALIZED.
In entity relationship diagrams, these are modeled using rectangles, also called entity boxes, with the names put inside the box. Usually, in ERDs, these are mapped to a relational table.
They are described by their attributes.
These are easily defined using properly written business rules. As a general rule, this will be easily translated from a noun in a business rule.
However, the names of these must be unique and easily distinguishable from other objects in the problem domain. The should also be descriptive and use terminology familiar to the user.
A proper name can go a long way in facilitating communication between the designer, application programmer, and end user, and can even help make your data model self-documenting.
A diagram that depicts an entity relationship model's entities, attributes, and relations.
Used in the entity relationship model, as created by Peter Chen in 1976, this uses graphical representations to model database components.
In this, there are three things represented: entities (represented by a rectangle, also referred to an as entity box, with the name of the entity in the box), attributes (characteristics of the entities), and relationships (associations among data).
There are also three different types of notation used in this: the original Chen notation (as developed by Chen), crow's foot notation, and class diagram notation.
Most modeling tools tend to use crow's foot notation or class diagram notation.
A data model that describes relationships (1:1, 1:M, M:N) among entities at the conceptual level with the help of graphical diagrams.
Developed by Peter Chen in 1976. The relational data model lacked features that made it useful for database design. Because, in the relational data model, the data was presented to the user in tables, designers tended to design their databases using graphical tools.
This model because popular because it complemented the relational model concepts - they combined to provide the foundation for highly structured database design.
However, this model lacks implementation tools. In other words, it's only useful for database design - it doesn't help when it comes to database implementation.
This model utilizes entity relationship diagrams, which use graphical representations to model database components.
This model is based off of entities, attributes (descriptions of entities), and relationships (the associations between data). There are three types of notation used in this model: Chen notation (the original format), crow's foot notation, and the class diagram notation.
This model's exceptional visual simplicity make it the dominant database modeling and design model, but improvements and better tools are always being sought after.
A metalanguage used to represent and manipulate data elements.
Unlike other markup languages, it permits the manipulation of a document's data elements, and it facilitates the exchange of structured documents, such as orders and invoices, over the internet.
It was developed when businesses realized they could use the internet as a communication tool to access, distribute, and exchange critical business information.
This emerged as the de facto standard for the efficient and effective exchange of structured, semistructured, and unstructured data.
When it began to be used widely, organizations quickly realized that they needed to handle large amounts of unstructured data, including word-processing documents, emails, web pages, and diagrams. Because of this, special databases based on this language were created to mange this data in a native format.
As these databases were spreading, object/relational database management systems were added support to integrate the XML databases into their relational databases.
The end user's view of the data environment.
Because end users are split up into different business units, and because each of these units use different subsets of the data, they view their data subsets differently, separately, and external of all other business units within the organization.
Because of this, this level of data abstraction causes many connected versions of this model that are connected together in the conceptual model.
A specific representation of an external model view is referred to as an external schema.
Also the application programmer's view of the data environment. Given its business focus, this model works with a data subset of the global database schema.
This model has several important advantages. One, it's easy to identify specific data that's required to support each business unit's operations.
Two, it makes the designer's job easy by providing feedback about the model's adequacy and accuracy from each business unit. A unit can check their model to ensure that it supports all processes as defined by the model.
Three, it helps to ensure security constraints for the entire database, as each business unit works with a different subset of data. Because each unit works with a different subset of data, it's more difficult to the database to be damaged.
Finally, it makes programming the applications much simpler, as the programmers have a roadmap to follow for each individual business unit.
In database modeling, a level of data abstraction that adapts the conceptual model to a specific DBMS model for implementation.
It is the representation of a database as "seen" by the DBMS. In other words, the internal model requires a designer to match the conceptual model's characteristics and constraints to those of the selected implementation model.
It can also be seen as the designer translating the conceptual model into the requirements of the chosen database management system. Ideally, the designer would use logical design and create a conceptual model that could be implemented in any database management system.
A detailed internal model is especially important to database designers who work with hierarchical or network data models, because those require precise specification of data storage locations and data access paths.
The relational model, though, requires less detail in this phase because most RDBMS handle data access paths transparently, where the designer isn't aware of how the data is accessed - the specificity in this phase isn't as necessary, but it does still help.
This is said to be software dependent, because a change in the software would affect the model - it would need to be changed to fit the characteristics and requirements of the chosen new model.
However, a change in the hardware won't affect the model, so it is still considered hardware independent. A change in the storage devices or even a change in the operating system won't affect the model.
A specific representation of this is referred to as the internal schema.
A data model based on a structure composed of two data elements, a key and a value, in which every key has a corresponding value or set of values.
This data model is also called the associative or attribute-value data model.
Early leaders in the NoSQL database model, such as Amazon's SimpleDB, Google's BigTable, and Apache's Cassandra, point to this structure being one of the leaders of the next generation of data models, as well as column stores.
This data model stores data in secondary storage, just like any other database. What separates this model from other data models is how the data is stored.
In this type of model, the table or database has two columns - the key, defining the attribute, and the value, defining the data attached to that attribute.
To accommodate for the variety of data types that could be in the value column, every piece of data in the value column is stored as a long string type. Because of this, it becomes difficult to index all of the data, and searching becomes difficult.
This is said to be schema-less, because it's so easy to add values - you merely add a new row, and attribute it to the correct object.
This is a good structure when it comes to data where the attributes are many but there aren't that many actual values for the attributes (scarce data). But, because of the issues of maintaining data integrity and relationships, and the increased complexity of any search query, with having every value as the same data type, this type of structure would be poor for every day business.
A data model standard created in the late 1960s that represented data as a collection of record types and relationships as predefined sets with an owner record type and a member record type in a perceived 1:M relationship.
It was created in order to represent complex data relationships more effectively than the hierarchical model, to improve database performance, and to impose a database standard.
This replaced the hierarchical model because it could be used to model complex 1:M relationships. However, it was replaced by the relational model due to the relational model's simpler data representation, superior data independence, and easy-to-use query language.
While more powerful than the hierarchical model, and while it did have limited data independence, it did lack the ability to handle ad hoc queries and lacked structural independence, so any change to the structure in one spot required changes all over.
Because of this, this put a lot of work on programmers, always writing cumbersome programs in order to generate the simplest of reports. Because of this, the relational model was born in the 1980s.
The user perceives the data as having a 1:M relationship, but in reality, this model allows for children to have more than one parent.
While this is no longer used today, it did give birth to several terms still used in data modeling today, including schema, subschema, data manipulation language (DML), and data definition language (DDL).
A new generation of database management systems that is not based on the traditional relational database model. It also supports distributed database architectures, provides high scalability, high availability, and fault tolerance, it supports large amounts of sparse data, and is geared towards performance rather than transaction consistency.
Despite these broad rules, there is no standard model for this type of database. However, examples of early success stories include Amazon's SimpleDB, Google's BigTable, and Apache's Cassandra, and they point to the use of key-value store and column store models.
This generation of databases was created due to the need of different organizations needing to handle Big Data needs. In recent years, companies have begun to be flooded with web data, including browsing patterns, purchasing histories, customer preferences, behavior patterns, and social media data from sites such as Facebook and Twitter.
The relational model doesn't always handle these Big Data needs efficiently, due to several reasons.
However, despite the issues with the relational model, it is still the preferred model for handling day-to-day transactions and structured data analytic needs. 98 percent of market needs are met by object/relational database management systems. The remaining two percent are served by databases built in this model.
These types of database do not enforce relationships among entities. Instead, that is up to the application programmer to manage in the program code. In addition, it's up to application programmers and to manage data integrity and validation.
These databases also use their own application programming interfaces (APIs), so they don't use SQL. Because of that, it's up to the programmers to code the correct way to retrieve and manipulate the data.
One of the big benefits of using this type of structure is that is can be implemented using cheap commodity servers, and it can be easily implemented using a distributed database of multiple nodes. However, this leads to issues of enforcing data consistency.
In order to combat this issue with data consistency, distributed databases will automatically make copies of data at multiple nodes in order to ensure high availability and fault tolerance. If the main node with the data goes down, then that data can be pulled from one of the nodes that has a copy.
However, if the network goes down during an update, the node won't always have the most updated information. This is one of the issues with this type of database - they sacrifice data consistency for performance. But some of these database have eventual consistency, which means that updates will trickle through the nodes until every node has the most up-to-date data.
Another benefit is that it can handle large amounts of data. It's actually built for sparse data, where there are a large amount of attributes, but not much actual data for each of those attributes.
These types of databases also support the ability to add nodes to the distributed database transparently and without any downtime, and also designed to keep working if one of the node fails.
Despite this being one of the most popular models in database design, it is only one of multiple emerging trends in data management.
A data model whose basic modeling structure is an object.
This was demanded by complex real world problems that demonstrated the need for a data model that closely represented the real world. The object, the basis for this model, more closely represents real world items. Objects store both data and their relationships.
This was developed to meet a specific need, as was the relational data model. This model was created to address specific engineering needs for more complex objects, not the wide-ranging needs of general data management. Because of this very specific need, this data model hasn't been as developed as the relational model ever since its inception.
However, the use of this data model increased when the internet was created, and businesses realized they could use the internet to access, distribute, and exchange critical business information, leading to the development of XML.
It introduced support for complex data within a rich semantic framework, and was combined with the relational data model in the extended relational data model.
These are typically depicted using UML class diagrams and UML notation.
The advances due to this model influenced many areas, from system development and design to programming. Many advanced programming languages, including Java, Ruby, Perl, and C#, have all adopted object-oriented concepts.
The added semantics created by this model allow for richer representation of complex objects, and more meaning gleaned from them. In turn, this allowed applications to support increasingly complex objects in innovative ways.
A DBMS based on the extended relational model (ERDM).
The ERDM, championed by many relational database researchers, constitutes the relational model's response to the OODM.
This model includes many of the object-oriented model's best features within an inherently simpler relational database structure.
It allows for relational databases that support advanced OO features, such as objects, with their encapsulated data and methods, extensible data types based on classes, and class inheritance.
Today, this is how most relational databases are set up. This represents the bulk of the market in relation to online transactional processing databases (OLTP; day-to-day operations) and online analytical processing databases (OLAP).
Its success can be attributed to several factors, including its conceptual simplicity, data integrity, easy-to-use query language, high transactional performance, high availability, security, scalability, and expandability, as compared to the OODBMS, which is only popular in niche technical markets that require support with more technical, complex objects.
After XML databases became widespread, these began to add support for them, to allow integration into the already build databases.
Despite the issues with using relational databases for big data needs, these types of DBMS remain to be used for 98 percent of market needs. The other two percent are served by NoSQL databases.
A database designed and implemented using the relational data model.
Today, most of these are actually implemented using an object/relational database management system. They represent a bulk of the market of online transactional processing databases (OLTP; day-to-day operations) and online analytical processing databases (OLAP).
However, when it comes to companies that have Big Data issues, these databases don't always have the tools required to analyze their data quickly and efficiently.
For example, it's not always easy to fit unstructured data, such as what you might get from a social media site, into the table format that this type of database is based on.
Due to the amount of new attributes and all of the formats being received by the organization on a daily basis, there is quickly a need for more space, more processing power, and sophisticated data analysis tools that aren't always available in this type of environment. These needs lead to the need of more hardware, which can come with a hefty price tag.
Finally, while OLAP tools have a lot of success with structured data, they do have issues sorting through the vast amounts of unstructured web data in order to get useful information.
Despite all of these issues, though, they remain the preferred approach when it comes to day-to-day transactions and the need to analyze structured data.
Developed by E.F. Codd of IBM in 1970, it represented a major breakthrough for users and designers because of its conceptual simplicity.
This replaced the network and hierarchical models due to its simpler data representation, superior data independence, and easy-to-use query language. These features made it the preferred data model for business applications.
This has not yet been replaced as the preferred data model, but it does now have several competitors in the object-oriented data model and the NoSQL database market.
Both this and the object-oriented data model were developed to address different issues. This model was developed for better general data management based on sound mathematical theory. It is based on mathematical set theory and represents data as independent relations.
Each relation (table) is conceptually represented as a matrix of intersecting rows and columns. Seperate relations are related to each other through the sharing of common entity characteristics, or attributes (values in columns).
Although the relations (tables) are separate from each other, it is easy for a user to associate different tables with each other. Because of this, there is limited, controlled data redundancy, but it helps the understanding of the data, rather than hurt it (as was the case in the file system).
A table resembles a file, but the main difference is that a relation is completely independent in terms of its data and structure, as it is a purely logical structure. The table has nothing to do with how the data is actually stored - the DBMS hides all of that from the user.
Another reason for this model's rise to power is the use of SQL, or other query languages. With these languages, the user specifies what needs to be done, but doesn't specify how - the DBMS handles that.
The conceptual simplicity set the stage for a genuine database revolution. Originally, the model was considered ingenious but impractical - contemporary computers didn't have the power to implement the model.
However, the power of computers and the efficiency of operating systems improved over time to where now, personal computers can implement easy-to-use databases based off of this model.
This is implemented through a special DBMS called a relational database management system, which hides all of the complexities behind the scenes, so that the user doesn't have to worry about all of that, and presents the data in logical and easy-to-understand tables. However, these tables are merely a logical presentation format - the data is stored separately, so that any change to the data or structure won't cause the designer to have to change everything.
Although this model is a vast improvement over the network and hierarchical data models, it still lacked features that would make it effective for database design. Because the data was presented graphically in the end, designers tended to use graphical elements when designing their databases. Thus, the entity relationship model was born.
Due to its robust foundation in broadly applicable principles, this model is easily extended to include new classes of capabilities, such as objects or XML.
When it comes to the needs of companies that have Big Data concerns, though, this model doesn't always match the needs.
For example, within this model, it's not always possible to fit unstructured data into the conventional relation structure of rows and tables. Adding millions of different attributes a day in different formats will lead to the need for more storage space, processing power, and sophisticated data analysis tools. Because of this need, there's a hefty price tag that comes with using this model for Big Data needs.
Data analysis using online analytical processing tools (OLAP) typically has a lot of success with this model. However, when it comes to Big Data, the amount of unstructured data makes this difficult to accomplish if this model is being used.
Despite these issues, databases based on this model remain the preferred database model to support most day-to-day transactions and structured data analytic needs.
An association between entities.
One of the main building blocks in all data models.
There are three types of these: one-to-one, one-to-many, and many-to-many. All of these relationships are bidirectional.
The entity relationship model defines these between separate entities, and uses the term connectivity to label them. They are shown in different ER diagrams using a line. Which ER diagram notation type you choose defines how the line is presented.
For example, in Chen notation, this is written out in a diamond between the two entity boxes. On either side, this is defined with either a "1" or an "M," signaling the type.
In crow's foot notation, the verb is written on above the line, with separate lines either going through the main line or coming off - the "many" relationship is labeled with three lines going to the entity box while the "one" side of the relationship is labeled with two lines going perpendicular to the main line.
In class or UML notation, both sides of the bidirectional relationships are written beneath the relationship line. Above the line is the relationship type shorthand (1..1, /../, or 1..*),
These are easily defined using properly written business rules. As a general rule, these will be easily defined using verbs in business rules.