Table (database) in the context of Code point

A file format is the way that information is encoded for storage in a computer file. It may describe the encoding at various levels of abstraction including low-level bit and byte layout as well high-level organization such as markup and tabular structure. A file format may be standarized (which can be proprietary or open) or it can be an ad hoc convention.

Some file formats are designed for very particular types of data: PNG files, for example, store bitmapped images using lossless data compression. Other file formats, however, are designed for storage of several different types of data: the Ogg format can act as a container for different types of multimedia including any combination of audio and video, with or without text (such as subtitles), and metadata. A text file can contain any stream of characters, including possible control characters, and is encoded in one of various character encoding schemes. Some file formats, such as HTML, scalable vector graphics, and the source code of computer software are text files with defined syntaxes that allow them to be used for specific purposes.

A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files.

In the open data discipline, a data set is a unit used to measure the amount of information released in a public open data repository. The European data.europa.eu portal aggregates more than a million data sets.

There are many different numbering schemes for assigning nominal numbers to entities. These generally require an agreed set of rules, or a central coordinator. The schemes can be considered to be examples of a primary key of a database management system table, whose table definitions require a database design.

In computability theory, the simplest numbering scheme is the assignment of natural numbers to a set of objects such as functions, rational numbers, graphs, or words in some formal language. A numbering can be used to transfer the idea of computability and related concepts, which are originally defined on the natural numbers using computable functions, to these different types of objects.

In a relational database, a column is a set of data values of a particular type, one value for each row of a table. A column may contain text values, numbers, or even pointers to files in the operating system. Columns typically contain simple types, though some relational database systems allow columns to contain more complex data types, such as whole documents, images, or even video clips. A column can also be called an attribute.

Each row would provide a data value for each column and would then be understood as a single structured data value. For example, a database that represents company contact information might have the following columns: ID, Company Name, Address Line 1, Address Line 2, City, and Postal Code. More formally, a row is a tuple containing a specific value for each column, for example: (1234, 'Big Company Inc.', '123 East Example Street', '456 West Example Drive', 'Big City', 98765).

In a relational database, a row or "record" or "tuple", represents a single, implicitly structured data item in a table. A database table can be thought of as consisting of rows and columns. Each row in a table represents a set of related data, and every row in the table has the same structure.

For example, in a table that represents companies, each row might represent a single company. Columns might represent things like company name, address, etc. In a table that represents the association of employees with departments, each row would associate one employee with one department.

Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. Data cleansing can be performed interactively using data wrangling tools, or through batch processing often via scripts or a data quality firewall.

After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data.

In the relational model of databases, a primary key is a designated set of attributes (column(s)) that can reliably identify and distinguish between each individual record in a table. The database creator can choose an existing unique attribute or combination of attributes from the table (a natural key) to act as its primary key, or create a new attribute containing a unique ID that exists solely for this purpose (a surrogate key).

Examples of natural keys that could be suitable primary keys include data that is already by definition unique to all items in the table such as a national identification number attribute for person records, or the combination of a very precise timestamp attribute with a very precise location attribute for event records.

A flat-file database is a tabular flat-file in which each record is semantically independent – can meaningfully be interpreted and manipulated independent of other records of the table. The term flat loosely refers to data that is record-based and sequential yet lacks more complicated aspects such as nesting, relationships and metadata (with the exception of column headers). Relationships can be inferred from the data, but the format does not provide special accommodations for relationships.

In computer programming, a reference is a value that enables a program to indirectly access a particular datum, such as a variable's value or a record, in the computer's memory or in some other storage device. The reference is said to refer to the datum, and accessing the datum is called dereferencing the reference. A reference is distinct from the datum itself.

A reference is an abstract data type and may be implemented in many ways. Typically, a reference refers to data stored in memory on a given system, and its internal value is the memory address of the data, i.e. a reference is implemented as a pointer. For this reason a reference is often said to "point to" the data. Other implementations include an offset (difference) between the datum's address and some fixed "base" address, an index, or identifier used in a lookup operation into an array or table, an operating system handle, a physical address on a storage device, or a network address such as a URL.

A database index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index data structure. Indexes are used to quickly locate data without having to search every row in a database table every time said table is accessed. Indexes can be created using one or more columns of a database table, providing the basis for both rapid random lookups and efficient access of ordered records.

An index is a copy of selected columns of data, from a table, that is designed to enable very efficient search. An index normally includes a "key" or direct link to the original row of data from which it was copied, to allow the complete row to be retrieved efficiently. Some databases extend the power of indexing by letting developers create indexes on column values that have been transformed by functions or expressions. For example, an index could be created on upper(last_name), which would only store the upper-case versions of the last_name field in the index. Another option sometimes supported is the use of partial index, where index entries are created only for those records that satisfy some conditional expression. A further aspect of flexibility is to permit indexing on user-defined functions, as well as expressions formed from an assortment of built-in functions.

The database schema is the structure of a database described in a formal language supported typically by a relational database management system (RDBMS). The term "schema" refers to the organization of data as a blueprint of how the database is constructed (divided into database tables in the case of relational databases). The formal definition of a database schema is a set of formulas (sentences) called integrity constraints imposed on a database. These integrity constraints ensure compatibility between parts of the schema. All constraints are expressible in the same language. A database can be considered a structure in realization of the database language. The states of a created conceptual schema are transformed into an explicit mapping, the database schema. This describes how real-world entities are modeled in the database.

"A database schema specifies, based on the database administrator's knowledge of possible applications, the facts that can enter the database, or those of interest to the possible end-users." The notion of a database schema plays the same role as the notion of theory in predicate calculus. A model of this "theory" closely corresponds to a database, which can be seen at any instant of time as a mathematical object. Thus a schema can contain formulas representing integrity constraints specifically for an application and the constraints specifically for a type of database, all expressed in the same database language. In a relational database, the schema defines the tables, fields, relationships, views, indexes, packages, procedures, functions, queues, triggers, types, sequences, materialized views, synonyms, database links, directories, XML schemas, and other elements.