PD.Relational Data Management

	PD	Relational Data Management	Version:	0.3
	Document type:	Pattern Definition	Owner:	S.A.D. Jumelet

Description

This Pattern Definition serves the creation of models in the Service Category "Data Management". This Pattern provides the function layout to model "strictly structured data" handling, i.e. it controls the creation, updating and querying of sets of organized data. It is called 'relational data management', because a schema is used to store data in a predefined way, with predefined relations between data objects. The provisioning of the following elements is important with this kind of data handling:

• a "modeling language", to define schemas;
• a "query language", to enable data object reading, creating, updating, and deleting;
• a "transaction mechanism", to enable interaction with other (instances of) data management facilities and applications.

These elements are provided by the Data Engine. Data objects are stored using a predefined (structured) Data Store. Both functions form the core of this Pattern.

Services realized

This Pattern realizes the following service(s):

• Application Database (This service provides a data store for applications with a predefined structure)

Functional and Integration view

This is the graphic representation of the function model of this Pattern Definition:

Relational Data Management pattern

Pattern Definition Composition

This pattern is an aggregation of the following (mandatory and optional) functions, expressed in Data/Object Types and Fundtion Definitions:

Function	Inclusion	Description/Rationale
Data Engine	recommended	The Data Engine is the heart of the pattern, as it offers the intelligence to control strictly structured data handling.
Table Store	recommended	The Table Store offers the predefined storage structure. This is one of the key characteristics of relational data management.
Traffic Encryption	optional	When data is exchanged over unsecured data transport facilities, it might be a good measure to encrypt the data at engine level.
Transaction Caching	optional	Retaining and keeping available of frequent issued transactions/queries, in order to improve performance.
Transaction Replication	optional	Multiplying transactions over multiple database instances, to maintain exact copies on data engine level. Mostly this is done to create a higher availability
Table Replication	optional	Multiplying tables over multiple database instances, to create an exact copy of a database on data engine level. This can be done to create a new development/test environment or to start creating a higher availability
File Export	optional	Extraction of data from the database and putting this in a file. This is used to execute a bulk data transfer from one instance to another. It can be used to create a replica or to import a dataset from one database into the other (ETL). Because it does not require a connection between Data Engines, it offers more flexibility and security, compared to direct Data Engine interaction. In both cases, the proper interfaces should be available.
DOT. Import	recommended	Loading of data from a file, to store this data in a database. This is used to restore a database from a replica or to import a large dataset from another database (ETL). Because it does not require a connection between Data Engines, it offers more flexibility and security, compared to direct Data Engine interaction. In both cases, the proper interfaces should be available.