Data warehousing concepts ralph kimball pdf

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The Data Warehouse Toolkit: The Definitive Guide to Dimensional Ralph Kimball founded the Kimball Group. . Fundamental Concepts. The data warehousing industry certainly has matured since Ralph sional data warehousing, and we will describe advanced concepts for. Fundamental Concepts. .. Ralph Kimball introduced the data warehouse/ business intelligence industry to dimensional modeling in. with his seminal .

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Data Warehousing Concepts Ralph Kimball Pdf

About the Authors Ralph Kimball founded the Kimball Group. Since the mid- s, he has been the data warehouse and business intelligence Concepts such as conformed dimensions, slowly changing dimensions, heteroge- neous. We want to thank Julie Kimball of Ralph Kimball Associates for her The data warehousing industry certainly has matured since Ralph Kimball basic knowledge of relational database concepts such as tables, rows, keys. Deployment. Chapter 19 - Maintaining and Growing the Data Warehouse appendix to the full treatment of this subject in Ralph Kimball's earlier book, The Data Dimensional modeling concepts are discussed in Chapters Chapter 5.

Conformed dimension[ edit ] A conformed dimension is a set of data attributes that have been physically referenced in multiple database tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts. A conformed dimension cuts across many facts. Dimensions are conformed when they are either exactly the same including keys or one is a perfect subset of the other. Most important, the row headers produced in two different answer sets from the same conformed dimension s must be able to match perfectly. Dimension tables are not conformed if the attributes are labeled differently or contain different values. Conformed dimensions come in several different flavors. At the most basic level, conformed dimensions mean exactly the same thing with every possible fact table to which they are joined. The date dimension table connected to the sales facts is identical to the date dimension connected to the inventory facts. By creating an abstract dimension, these flags and indicators are removed from the fact table while placing them into a useful dimensional framework. The nature of these attributes is usually text or various flags, e. These kinds of attributes are typically remaining when all the obvious dimensions in the business process have been identified and thus the designer is faced with the challenge of where to put these attributes that do not belong in the other dimensions. One solution is to create a new dimension for each of the remaining attributes, but due to their nature, it could be necessary to create a vast number of new dimensions resulting in a fact table with a very large number of foreign keys. The designer could also decide to leave the remaining attributes in the fact table but this could make the row length of the table unnecessarily large if, for example, the attributes is a long text string. The solution to this challenge is to identify all the attributes and then put them into one or several Junk Dimensions. The designer can choose to build the dimension table so it ends up holding all the indicators occurring with every other indicator so that all combinations are covered.

Junk dimensions are also appropriate for placing attributes like non-generic comments from the fact table. Such attributes might consist of data from an optional comment field when a customer places an order and as a result will probably be blank in many cases. Therefore, the junk dimension should contain a single row representing the blanks as a surrogate key that will be used in the fact table for every row returned with a blank comment field. Degenerate dimensions are very common when the grain of a fact table represents a single transaction item or line item because the degenerate dimension represents the unique identifier of the parent.

Degenerate dimensions often play an integral role in the fact table's primary key. This is often referred to as a "role-playing dimension".

Dimension table[ edit ] In data warehousing , a dimension table is one of the set of companion tables to a fact table.

The fact table contains business facts or measures , and foreign keys which refer to candidate keys normally primary keys in the dimension tables. Contrary to fact tables, dimension tables contain descriptive attributes or fields that are typically textual fields or discrete numbers that behave like text. Dimension attributes should be: Verbose labels consisting of full words Descriptive Complete having no missing values Discretely valued having only one value per dimension table row Quality assured having no misspellings or impossible values Dimension table rows are uniquely identified by a single key field.

It is recommended that the key field be a simple integer because a key value is meaningless, used only for joining fields between the fact and dimension tables. Dimension tables often use primary keys that are also surrogate keys. Surrogate keys are often auto-generated e.

The use of surrogate dimension keys brings several advantages, including: Performance. Join processing is made much more efficient by using a single field the surrogate key Buffering from operational key management practices. This prevents situations where removed data rows might reappear when their natural keys get reused or reassigned after a long period of dormancy Mapping to integrate disparate sources Handling unknown or not-applicable connections Tracking changes in dimension attribute values Although surrogate key use places a burden put on the ETL system, pipeline processing can be improved, and ETL tools have built-in improved surrogate key processing.

The goal of a dimension table is to create standardized, conformed dimensions that can be shared across the enterprise's data warehouse environment, and enable joining to multiple fact tables representing various business processes. Every fact table is filtered consistently, so that query answers are labeled consistently.

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Queries can drill into different process fact tables separately for each individual fact table, then join the results on common dimension attributes. Reduced development time to market. The common dimensions are available without recreating them.

Over time, the attributes of a given row in a dimension table may change.

Ralph Kimball

For example, the shipping address for a company may change. Kimball refers to this phenomenon as Slowly Changing Dimensions. Strategies for dealing with this kind of change are divided into three categories: Type One. Simply overwrite the old value s. Type Two. Add a new row containing the new value s , and distinguish between the rows using Tuple-versioning techniques. Type Three. 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.

Snowflake Schema The snowflake schema is an extension of the star schema. In a snowflake schema, each dimension are normalized and connected to more dimension tables.

Rules for Dimensional Modelling Load atomic data into dimensional structures. Build dimensional models around business processes.

Need to ensure that every fact table has an associated date dimension table. Ensure that all facts in a single fact table are at the same grain or level of detail. It's essential to store report labels and filter domain values in dimension tables Need to ensure that dimension tables use a surrogate key Continuously balance requirements and realities to deliver business solution to support their decision-making Benefits of dimensional modeling Standardization of dimensions allows easy reporting across areas of the business.

Dimension tables store the history of the dimensional information. It allows to introduced entirely new dimension without major disruptions to the fact table.

Dimensional also to store data in such a fashion that it is easier to retrieve the information from the data once the data is stored in the database. Compared to the normalized model dimensional table are easier to understand. Information is grouped into clear and simple business categories. The dimensional model is very understandable by the business. This model is based on business terms, so that the business knows what each fact, dimension, or attribute means.

Dimensional models are deformalized and optimized for fast data querying. Many relational database platforms recognize this model and optimize query execution plans to aid in performance.

Dimensional modeling creates a schema which is optimized for high performance. It means fewer joins and helps with minimized data redundancy.

Dimension (data warehouse)

The dimensional model also helps to boost query performance. It is more denormalized therefore it is optimized for querying. Dimensional models can comfortably accommodate change. Dimension tables can have more columns added to them without affecting existing business intelligence applications using these tables.

Summary: A dimensional model is a data structure technique optimized for Data warehousing tools. Dimension provides the context surrounding a business process event.

The Attributes are the various characteristics of the dimension. A fact table is a primary table in a dimensional model.

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