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Relational Database Terms Below are the unique terms and specific definitions that will help you understand what a RDB can do and how it works: Row: A set of data constituting a single item. For example, the data for a single employee e. A row can also be called a record, an entity, or a tuple. Column: Labels for elements of rows. A column gives context to the information contained in rows. For an employee database, the column headers could be the items listed above for employees.
A column is also known as an attribute or a field. Table: A group of rows that match the parameters set up for the table. The data in a table must all be related. An employee database may have separate tables for active employees, retired employees, and former employees. A table is also known as a relation or base revelar. Domain: The set of possible values for a given column. For example, the phone number and ZIP code columns would be numbers, while first and last names would be limited to letters.
Constraint: A narrowing of a domain. For example, the domain of the work location on a employee record would be alphanumeric, but it could be restricted to a predefined list rather than being a free-form field.
The phone number field would be constrained to 10 digits. Primary key: The unique identifier of a row in a table. Foreign key: The unique identifier of a row in another table. Distributed Database: A database that stores data in multiple locations, rather than on a single hard drive or server.
More About Keys Primary keys and foreign keys are used to identify rows or records in a table. There are a few ways to ensure a unique value for each key when a new data record is added to a table: Generate One: Keys can be created based on an algorithm. It could be a random or sequential number, or based on the data in the record. Create One: You can combine columns to create a unique key. To create a key for a list of work locations, you should be able to create a unique value by combining the name of the building and its ZIP code.
Enter One: A user can type a value when entering the data. However, ensure uniqueness by putting business rules and data checks into place. Empower your teams to be productive while maintaining enterprise-grade security. Types of Database Relationships The power of a relational database is in the links and relations. There are three primary types of database relationships: One-to-One: One row in one table is connected to one and only one row in another table.
For example, a Social Security number is linked to a single employee. One-to-Many: One row in one table is connected to zero, one, or more than one rows in another table. For example, one work location can be linked to many employees. Many-to-Many: Zero, one, or many rows in one table are linked to zero, one, or many rows in another table.
For example, multiple employees can be assigned to multiple projects. The following are problems to be aware of with setup and use: RDBMSs can be complicated to implement. Performance issues can be difficult to predict, especially when the data is shared among multiple applications. Different design strategies are required for operational databases versus reporting databases. The history of databases can be divided into three eras, based on the dominant data model: Navigational: Data is stored in files, and is accessed by navigating through a tree-like structure.
There were two versions: hierarchical data is organized in a tree structure and network data is organized in a lattice structure. Relational: Data is stored in tables and is accessed via commands that display and combine records. Post-relational: This will be explored later in this article. Number Title Text 0 The Foundation Rule For any system that is advertised as or that claims to be a relational database management system, that system must be able to manage databases entirely through its relational capabilities.
However, there must be at least one language whose statements are expressible, per well-defined syntax as character strings, and that is comprehensive in supporting all of the following items: Data definition View definition Data manipulation interactive and by program Integrity constraints Authorization Transaction boundaries begin, commit, and rollback 6 The View Updating Rule All views that are theoretically updatable are also updatable by the system. Relational Databases vs.
Nonrelational Databases Besides relational, there are a number of other database design models and plenty of reasons why a nonrelational database might be better. In object-oriented models, objects can be defined by the developers to meet the needs of the business.
Object-oriented: A modular design approach that relies on creating and reusing objects. In databases, data is organized into objects rather than tables. Type and Description Strengths Weaknesses Flat file: Each file is independent, with no connection between them.
Example: db40 - Good when there are many complex data relationships - Easier navigation - Data model is similar to the real world - Lower efficiency when the data is simple - Requires complex programming - No ad-hoc queries Object-relational: A hybrid of object-oriented and relational database models.
How Relational Databases Operate Basic Functionality The basic functions of a DBMS are to read view data via queries , create add data, tables, rows, or columns , update change data, tables, rows, or columns , and delete data. Conditions can be added to perform more functions such as the following: View data that meet certain criteria. A store manager could view all items that have fewer than 10 units in stock, or that have been in the warehouse for more than three months.
Create a new table that is a subset of a base table. The marketing department could create a table that shows only customers who live within a mile radius of a new location. Connect the contents of two different tables. A contractor could view all the subcontractors who worked on a project and the amount each billed for their work, or a principal could see all students who have a GPA of 3.
Combine data in two tables: HR could view all actives employees and all retired employees to create an invitation list for the company holiday party. View data that have no relationship: This might be a list of all customers who created an account but who never placed an order, then delete them.
Delete data from existing records: Delete the automatic discount from customer accounts after its expiration date. Parameters are added to the command to specify the number and names of the columns. JOIN: Allows the data from multiple tables to be combined. Parameters are added to the command to specify the data in the columns. You could see the best-selling books of , but limit it to the top WHERE: A conditional statement that allows queries, additions, deletions, and changes to be limited to data that meet certain criteria.
Data Models, Designs, and Schemas A database model is an abstract representation of how data will be stored. Locking When data is being modified, locking prevents another user or transaction from altering that data. Database Normalization Database normalization is the foundation of relational databases.
Database normalization provides the following benefits: Duplicate data is minimized, and data storage needs decrease. If you go to the doctor, they might ask for your first name, last name, weight, and height. In practice, as you get more items of unstructured data, you can turn them into semi-structured data. Effectively you were designing your own storage mechanism from scratch every time you wrote a piece of software.
If you just wanted to save unstructured data, it was pretty easy. Write it to a file when it changes and read it from the file when you want to get it back. The problems started when you wanted to work with structured data. You could just write the column names out first and then save the information in a comma or tab separated format.
But if you wanted to retrieve a subset of the data say all the addresses of students living in California , it was inefficient as you had to read through every single record in the file to find the records that you wanted. That made retrieval operations very slow — especially for large data sets. One way to improve that situation was to store the information in two-dimensional tables — think an Excel spreadsheet — with the column names at the top and the data in the rows below it.
For example, if you index a list of mailing addresses by city, the database will take a little more time when saving every new address as it has to write to the index.
This is a particularly good trade off for data that you read more often than you write. Getting into the habit of reading research papers will serve you well as a data scientist and the math in this one is pretty straightforward! It might look something like this:. Do you notice the duplication within the table? This database will contain one table with the actual transactions, i. The product file will contain one row for each product available with a set of variables capturing various product characteristics.
The customer file will contain one row for each customer with associated customer information , while the store file will contain one row per store with store information. The specific joins to perform in this appliation will depend on your objectives.
For example, if you wanted to investigate how sales of a certain product group varied with the regional location of stores you would need to join both the product file to get information about which product IDs belong to the group of interest and the store file to get the regional location of each store with the transaction file. On the other hand, if you wanted to know the characteristics of your top customers, you would need to join the transaction file with the customer file. DonorsChoose is a non-profit organization that raises money for school projects.
Potential donors can go to the website and pick among active projects and donate to any project of their choosing. When a project is funded the organization channels the funds to the school in question.
The data for this case study is not included in the Rstudio project file you downloaded above. There are two data files or tables for in this database - donations and projects.
You can download the files in R format for and here. These are large files: mb for the donation file and 34mb for the projects file. If you wish to have a go at the full files, you can get the full donation file mb and projects file mb here. You should download the files and put them in the data folder for the current Rstudio project. In the following we will use the full database.
The full database contains a total of 1,, projects dating from to to which a total of 5,, donations have been made. There is a lot of information about both donors and projects you can see a sample of the data if you write glimpse projects in R. Let us first look at where the projects are located: How many projects are there for each state?
We can calculate that by using the table command:. Consider now the following question: How local is giving? In other words, do donors mainly care about their own state or do the give to projects in many states? A related question would be to ask where donations to projects located in certain state originated. Where do these donors give?
We can start by only considering the donation data for donors who reside in New York:. This leaves us with a donation file with donations. Now we need to calculate to where these donations were made. To do this we need information on the location on each project.
This is in the projects file. Therefore we need to join the ny. This file will have the same number of rows as the ny. We can now simply count the project locations:. So 32 percent of the donations originating in New York are made to school projects in New York.
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