Data Schema Concept outlines the procedure with which integrated data resource is managed. An integrated data resource or a data warehouse is a repository of high level volume of data and updates and refreshes happen at very close intervals every house.
Because of the complexity of data warehouse implementation, there should be a data management and data integration tools that are very well suited for exchanging information in a semantically meaningful manner.
But frequently, data integrated data resources and data warehouses suffer from two significant problems.
The first problem pertains to the requirement of a very comprehensive schema design before a data warehouse can be used to gather, store and share data and information.
The second problem pertains to the difficulty in dealing with extensions because schema evolution is a herculean task and may break backwards compatibility.
The reality today is that data comes and ago at such exponential rates and when not planned and managed well, data explosion may cause negative impacts on business organizations.
The key to implementing a robust, stable, accurate and reliable integrated data resource is in careful planning. At this stage everything must have to be near perfect. The planning must be based on a written data schema concept.
Technically, a schema refers to the collection of logical structures of data or schema objects used in database. An integrated data resource is composed of several relational databases so most the data schema concept has something to do with database.
A schema is owned by a database user and has the same name as that user. Each database user owns one schema. In order to create and manipulate schemas, one uses structured query language (SQL).
To understand more about data schema objects, it is good to be informed about schema objects in database. Some of the schema objects include: Clusters, Database links, Database triggers, Dimensions, External procedure libraries, Indexes and index types, Java classes, Java resources, and Java sources, Materialized views and materialized view logs, Object tables, object types, and object views, Operators, Sequences, Stored functions, procedures, and packages, Synonyms, Tables and index-organized tables and Views.
Schema objects are really description of structures for logical storage. They do not have any one-to-one correspondence to physical files on disk that store their information. In relational database implementations however, schema objects are sometimes stored logically within a database’s tablespace and each of the object’s data is physically stored in one or more of the data files in the tablespace.
In the case of other objects like tables, indexes and clusters, the database can be set to how much disk space is allocated for them within the tablespace’s datafiles. No relationship exists between tables and schemas. A tablespace can contain objects coming from various schemas and the objects for a schema can be stored in various tablespaces.
Having known these schema objects, it can already be easier to come up with draw a data schema concept from abstraction. Based on the assumed data architecture which has been based on the business rules, the data schema concept will is ready to be drawn on paper (or in digital files). Everything will have to be laid in detail.
The data entity will be given structures pertaining to its data types and all attributes and relationships are defined as well. When the tiny details about the data are defined, the database lay out will be readied as well. The whole framework will start to take shape. This includes what database applications to acquire, how computer servers will be arranged and the network paraphernalia to connect them.