Change Data Capture
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Change Data Capture
In databases, change data capture (CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data. CDC is an approach to data integration that is based on the identification, capture and delivery of the changes made to enterprise data sources. CDC occurs often in data-warehouse environments since capturing and preserving the state of data across time is one of the core functions of a data warehouse, but CDC can be utilized in any database or data repository system. Methodology System developers can set up CDC mechanisms in a number of ways and in any one or a combination of system layers from application logic down to physical storage. In a simplified CDC context, one computer system has data believed to have changed from a previous point in time, and a second computer system needs to take action based on that changed data. The former is the source, the latter is the target. It is possible ...
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Database
In computing, a database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. The design of databases spans formal techniques and practical considerations, including data modeling, efficient data representation and storage, query languages, security and privacy of sensitive data, and distributed computing issues, including supporting concurrent access and fault tolerance. A database management system (DBMS) is the software that interacts with end users, applications, and the database itself to capture and analyze the data. The DBMS software additionally encompasses the core facilities provided to administer the database. The sum total of the database, the DBMS and the associated applications can be referred to as a database system. Often the term "database" is also used loosely to refer to any of the DBMS, the database system or an appli ...
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Latency (engineering)
Latency, from a general point of view, is a time delay between the cause and the effect of some physical change in the system being observed. Lag, as it is known in gaming circles, refers to the latency between the input to a simulation and the visual or auditory response, often occurring because of network delay in online games. Latency is physically a consequence of the limited velocity at which any physical interaction can propagate. The magnitude of this velocity is always less than or equal to the speed of light. Therefore, every physical system with any physical separation (distance) between cause and effect will experience some sort of latency, regardless of the nature of the stimulation at which it has been exposed to. The precise definition of latency depends on the system being observed or the nature of the simulation. In communications, the lower limit of latency is determined by the medium being used to transfer information. In reliable two-way communication syst ...
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Oracle Data Integrator
Oracle Data Integrator (ODI) is an Extract, load and transform (ELT) (in contrast with the ETL common approach) tool produced by Oracle that offers a graphical environment to build, manage and maintain data integration processes in business intelligence systems. History Oracle purchased Sunopsis in October 2006 and re-branded it as Oracle Data Integrator (ODI). The aim of this acquisition was to enhance the Oracle Fusion Middleware offering, which required broad support to heterogeneous sources and targets. After the purchase Oracle continued to offer separately ODI as well as its former ETL product Oracle Warehouse Builder. In January 2010 Oracle announced their intention to merge them into a single product (Oracle Data Integrator Enterprise Edition). Components Oracle Data Integrator comprises the following components: * The Modular Repository, made up of a master repository and of one or several work repositories to store metadata about the data integration process. Th ...
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Slowly Changing Dimension
A slowly changing dimension (SCD) in data management and data warehousing is a dimension which contains relatively static data which can change slowly but unpredictably, rather than according to a regular schedule. Some examples of typical slowly changing dimensions are entities as names of geographical locations, customers, or products. Some scenarios can cause referential integrity problems. For example, a database may contain a fact table that stores sales records. This fact table would be linked to dimensions by means of foreign keys. One of these dimensions may contain data about the company's salespeople: e.g., the regional offices in which they work. However, the salespeople are sometimes transferred from one regional office to another. For historical sales reporting purposes it may be necessary to keep a record of the fact that a particular sales person had been assigned to a particular regional office at an earlier date, whereas that sales person is now assigned to a ...
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Referential Integrity
Referential integrity is a property of data stating that all its references are valid. In the context of relational databases, it requires that if a value of one attribute (column) of a relation (table) references a value of another attribute (either in the same or a different relation), then the referenced value must exist. For referential integrity to hold in a relational database, any column in a base table that is declared a foreign key can only contain either null values or values from a parent table's primary key or a candidate key. In other words, when a foreign key value is used it must reference a valid, existing primary key in the parent table. For instance, deleting a record that contains a value referred to by a foreign key in another table would break referential integrity. Some relational database management systems (RDBMS) can enforce referential integrity, normally either by deleting the foreign key rows as well to maintain integrity, or by returning an error and n ...
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Slowly Changing Dimension
A slowly changing dimension (SCD) in data management and data warehousing is a dimension which contains relatively static data which can change slowly but unpredictably, rather than according to a regular schedule. Some examples of typical slowly changing dimensions are entities as names of geographical locations, customers, or products. Some scenarios can cause referential integrity problems. For example, a database may contain a fact table that stores sales records. This fact table would be linked to dimensions by means of foreign keys. One of these dimensions may contain data about the company's salespeople: e.g., the regional offices in which they work. However, the salespeople are sometimes transferred from one regional office to another. For historical sales reporting purposes it may be necessary to keep a record of the fact that a particular sales person had been assigned to a particular regional office at an earlier date, whereas that sales person is now assigned to a ...
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Database Trigger
A database trigger is procedural code that is automatically executed in response to certain events on a particular table or view in a database. The trigger is mostly used for maintaining the integrity of the information on the database. For example, when a new record (representing a new worker) is added to the employees table, new records should also be created in the tables of the taxes, vacations and salaries. Triggers can also be used to log historical data, for example to keep track of employees' previous salaries. Triggers in DBMS Below follows a series of descriptions of how some popular DBMS support triggers. Oracle In addition to triggers that fire (and execute PL/SQL code) when data is modified, Oracle 10g supports triggers that fire when schema-level objects (that is, tables) are modified and when user logon or logoff events occur. Schema-level triggers * After Creation * Before Alter * After Alter * Before Drop * After Drop * Before Insert The four main types of ...
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Noise (signal Processing)
In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal (signal processing), signal may suffer during capture, storage, transmission, processing, or conversion. Vyacheslav Tuzlukov (2010), ''Signal Processing Noise'', Electrical Engineering and Applied Signal Processing Series, CRC Press. 688 pages. Sometimes the word is also used to mean signals that are Randomness, random (Predictability, unpredictable) and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise. Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filter (signal processing), filters. The mathematical limits for noise removal are set by information theory. Types of noise Signal processing noise can be classified by its statistical properties (sometimes ca ...
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Data Model
A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. The term data model can refer to two distinct but closely related concepts. Sometimes it refers to an abstract formalization of the objects and relationships found in a particular application domain: for example the customers, products, and orders found in a manufacturing organization. At other times it refers to the set of concepts used in defining such formalizations: for example concepts such as entities, attributes, relations, or tables. So the "data model" of a banking application may be defined using the entity-relationship "data model". This article uses the term in both senses. A data model explicitly determines the ...
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Data Integrity
Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire Information Lifecycle Management, life-cycle and is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context even under the same general umbrella of computing. It is at times used as a proxy term for data quality, while data validation is a prerequisite for data integrity. Data integrity is the opposite of data corruption. The overall intent of any data integrity technique is the same: ensure data is recorded exactly as intended (such as a database correctly rejecting mutually exclusive possibilities). Moreover, upon later Data retrieval, retrieval, ensure the data is the same as when it was originally recorded. In short, data integrity aims to prevent unintentional changes to information. Data integrity is not to be confus ...
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Log Shipping
Log shipping is the process of automating the backup of transaction log files on a primary (production) database server, and then restoring them onto a standby server. This technique is supported by Microsoft SQL Server, How to Perform SQL Server Log Shipping
, "What is Log Shipping". Retrieved on 2008-12-16.
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