There are two popular schemas. The star schema architecture is easy to design. It is called a star schema because diagram resembles a star, with points radiating from a center. The center of the star consists of the fact table, and the points of the star is dimension tables. The fact tables in a star schema which is third normal form whereas dimensional tables are de-normalized. The snowflake schema is an extension of the star schema.
In a snowflake schema, each dimension are normalized and connected to more dimension tables. Multidimensional data model in data warehouse is a model which represents data in the form of data cubes.
It allows to model and view the data in multiple dimensions and it is defined by dimensions and facts. Multidimensional data model is generally categorized around a central theme and represented by a fact table. Skip to content.
Dimensional Modeling Dimensional Modeling DM is a data structure technique optimized for data storage in a Data warehouse. Report a Bug. Previous Prev. Next Continue. Home Testing Expand child menu Expand. SAP Expand child menu Expand.
There can be situations where operational keys can be modified in the OLTP end. If that happens, you need to change all the values in a fact table. We need to understand that the fact table consists of many records. If you are changing a large number of records, the fact table will not be accessible until the modifications are completed.
Further, operational keys can be string columns and when joining from string columns, there can be performance issues. Since data warehouse designers do not have control over the operational keys, to mitigate the performance issues, we need to introduce a customized column. In the case of Type 2 SCDs, there can be situation multiple rows will exist for the same operational value.
In that situation, we can only create Primary Key by introducing a surrogate key. It is essential to declare the Fact grain at the design stage.
Every fact table should have one grain and you should be having multiple grain levels in the same fact table. In most incidents, at the designing stage of the data warehouse, you tend to declare and comply with the defined fact grain. However, when there are modifications requested by the end-users, in the interest of time, you tend to violate the grain. This violation will result in many other performance issues and a lot of maintenance issues later.
A data warehouse is a framework for data analytics. If it is a framework, during the time of requirement elicitation phase, it is essential to examine the entire process of the organization. However, end-users will provide requirements by means of reports.
During the Designing a Data Warehouse, if you are confined to these reports, the data warehouse will not be able to provide framework capabilities. Most of the users of a data warehouse are business users who do not have experience in writing queries by joining multiple tables.
You start with simple data marts which consist of a handful of facts and dimension tables. Over time, you will extend those fact and dimension tables to cover much broader requirements.
Though duplication is acceptable at designing a data warehouse for different grains, there is no value of the same data in multiple dimensions. In that situation, there can be a lot of administrative and performance issues.
Aggregates are one of the easiest methods by which query performance can be optimized. It is a collection of associated data items, consisting of measures and context data. It typically represents business items or business transactions. It is a collection of data which describe one business dimension.
Dimensions decide the contextual background for the facts, and they are the framework over which OLAP is performed. It is a numeric attribute of a fact, representing the performance or behavior of the business relative to the dimensions. Considering the relational context, there are two basic models which are used in dimensional modeling:. The star model is the underlying structure for a dimensional model. It has one broad central table fact table and a set of smaller tables dimensions arranged in a radial design around the primary table.
The snowflake model is the conclusion of decomposing one or more of the dimensions. Fact tables are used to data facts or measures in the business. Facts are the numeric data elements that are of interest to the company. The fact table includes numerical values of what we measure. For example, a fact value of 20 might means that 20 widgets have been sold. Each fact table includes the keys to associated dimension tables. These are known as foreign keys in the fact table.
Dimension tables establish the context of the facts. Dimensional tables store fields that describe the facts. Dimension tables contain the details about the facts. That, as an example, enables the business analysts to understand the data and their reports better.
The dimension tables include descriptive data about the numerical values in the fact table. That is, they contain the attributes of the facts. For example, the dimension tables for a marketing analysis function might include attributes such as time, marketing region, and product type. Since the record in a dimension table is denormalized, it usually has a large number of columns. The dimension tables include significantly fewer rows of information than the fact table. The attributes in a dimension table are used as row and column headings in a document or query results display.
Example: A city and state can view a store summary in a fact table. Item summary can be viewed by brand, color, etc. Customer information can be viewed by name and address. In this example, Customer ID column in the facts table is the foreign keys that join with the dimension table. By following the links, we can see that row 2 of the fact table records the fact that customer 3, Gaurav, bought two items on day 8.
A hierarchy is a directed tree whose nodes are dimensional attributes and whose arcs model many to one association between dimensional attributes team.
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