TestOut 7.4 and 7.5 Quizlet

Big Data

Extremely large sets of data, usually automatically recorded, that can be analyzed to show insights into human behavior trends.

NoSQL

Nonrelational databases that arise from big data.

Data Mining

A broad and open-ended analysis of extracted big data.

Query

A command sent to a database. They are most commonly used for CRUD operations (create, read, update, delete).

SQL

A computer language that makes relational databases work.

Structured Query Language

A computer language used by most relational databases for creating queries.

Direct Access

A method of accessing a database directly using the database management system itself. Database management systems give administrators direct access to their databases. From here, they can write SQL commands to create new databases and interact with existing databases.

Query Builder

A method of accessing a database using a graphical user interface that allows users to select what actions they want to perform on a database. provides a graphical user interface where people can select what actions they want to perform on a database. Then, the builder translates the user's choices into SQL commands and runs them. This is helpful when a person may need to interact with a database but doesn't know how to write SQL commands.

Programmatic Access

A method of accessing a database using custom computer programs. Humans aren't the only ones who need access to databases; many computer programs need it too. Developers can write code into their software that reads, writes, updates, and deletes database records programmatically.

User Interface Access

A method of accessing a database using forms that allow users to interact with the database.

Unstructured Data

Data that doesn't follow a clear pattern. Imagine an archive of primary sources about the American Civil War that includes journals, newspapers, photographs, telegrams, and poems. A good way to keep track of all these different resources would be a document database. Document databases can store lots of different kinds of files. In our example, a user could enter a search term such as "newspaper" and get a list of the newspaper articles in the database. The file system on your computer is an example of a document database.

Structured Data

Data that can be divided into discrete categories. For example, all the books in a library catalog have a title, author, publication year, and so on. This kind of data is easy to store in a table, where every single book has a value for every single field, and is perfect for a relational database.

Semi-Structured Data

Data that has some discrete categories but also other information that doesn't follow a clear pattern. These data don't fit into the rigid rows and columns of a table but have some things in common. For example, think about classifying different kinds of animals. Every animal species has a name, average size, and life expectancy. However, a dog doesn't need a field to store its wingspan, and a butterfly doesn't need a field for its favorite prey. These kinds of data can be stored in a key-value database. A key is a category of information, kind of like a field. Using a key-value database, we could store information about a variety of animals. Some keys would be common to all the animals, but others would be different.

Data Definition Language

Database commands that change the structure, or schema, of the database.

Data Manipulation Language

Database commands that modify the records within the database and not the schema itself.

Big Data Analytics

The process of asking specific questions to help interpret big data. For example, a business might be interested in making an informed decision regarding product features. Medical researchers can use patient information and gene mappings to predict who might need specific treatments. Cities can take advantage of traffic sensor technology to better design roads and keep traffic moving smoothly.

Data Extraction

The process of gathering and organizing big data. This is a challenge because big data is typically unstructured and distributed over several locations. And because it's so complex, combining various types of information, big data can't be processed quickly into a relational database. Very often, it includes images, documents, complex numbers, and video. And the data are constantly expanding.