Dimensional modeling: Difference between revisions

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{{Short description|Data modeling concept}}

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'''Dimensional modeling''' ('''DM''') is part of the ''[[The Kimball Lifecycle|Business Dimensional Lifecycle]]'' methodology developed by [[Ralph Kimball]] which includes a set of methods, techniques and concepts for use in [[data warehouse]] design.<ref name="ConBegg9"/>{{rp|1258-12601258–1260}}<ref name="MoodyKokink-1">{{cite web|url=http://neumann.hec.ca/sites/cours/6-060-00/MK_entreprise.pdf|title=From Enterprise Models to Dimensional Models: A Methodology for Data Warehouse and Data Mart Design|id=Dimensional Modelling|access-date=3 July 2018|first1=Daniel L.|last1=Moody|first2=Mark A.R.|last2=Kortink|url-status=live|archive-url=https://web.archive.org/web/20170517164505/http://neumann.hec.ca/sites/cours/6-060-00/MK_entreprise.pdf|archive-date=17 May 2017|df=dmy-all}}</ref> The approach focuses on identifying the key [[business process]]es within a business and modelling and implementing these first before adding additional business processes, as a [[Top-down and bottom-up design|bottom-up approach]].<ref name="ConBegg9"/>{{rp|1258-12601258–1260}} An alternative approach from [[Bill Inmon|Inmon]] advocates a top down design of the model of all the enterprise data using tools such as [[entity-relationship model]]ing (ER).<ref name="ConBegg9">{{cite book|title=Database Systems - A Practical Approach to Design, Implementation and Management|first1=Thomas|last1=Connolly|first2=Carolyn|last2=Begg|publisher=Pearson|isbn=978-1-292-06118-4|edition=6th|at=Part 9 Business Intelligence|date=26 September 2014|df=dmy-all}}</ref>{{rp|1258-12601258–1260}}

==Description==

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=== Dimension normalization ===

{{NPOV section|date=June 2018}}

Dimensional normalization or snowflaking removes redundant attributes, which are known in the normal flatten de-normalized dimensions. Dimensions are strictly joined together in sub dimensions.

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== Benefits of dimensional modeling ==

'''Commonly cited benefits of dimensional modeling include:'''<ref name="kimball2013">{{cite book |last1=Kimball |first1=Ralph |last2=Ross |first2=Margy |title=The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling |edition=3rd |year=2013 |publisher=Wiley |isbn=9781118530801 |page=43 |url=https://ia801609.us.archive.org/14/items/the-data-warehouse-toolkit-kimball/The%20Data%20Warehouse%20Toolkit%20-%20Kimball.pdf}}</ref>

{{third-party|section|date=June 2018}}

Benefits of the dimensional model are the following:<ref name="refname5"/>

* '''Understandability and simplicity.''' Dimensional models organize data by business processes and shared business terms (dimensions), which makes schemas easier for analysts to navigate than highly normalized designs.<ref name="kimball2013" />

* Understandability. Compared to the normalized model, the dimensional model is easier to understand and more intuitive. In dimensional models, information is grouped into coherent business categories or dimensions, making it easier to read and interpret. Simplicity also allows software to navigate databases efficiently. In normalized models, data is divided into many discrete entities and even a simple business process might result in dozens of tables joined together in a complex way.

* Query performance. Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating. The predictable framework of a dimensional model allows the database to make strong assumptions about the data which may have a positive impact on performance. Each dimension is an equivalent entry point into the fact table, and this symmetrical structure allows effective handling of complex queries. [[Query optimization]] for star-joined databases is simple, predictable, and controllable.

* '''Query performance for analytic workloads.''' Star-schema queries typically join a large fact table to a few small dimensions; many systems implement star-join optimizations, and benchmarks specifically evaluate this workload (e.g., the Star Schema Benchmark).<ref name="kimball2013" /><ref name="ssb2009">{{cite conference |last1=O'Neil |first1=Patrick |last2=O'Neil |first2=Elizabeth |last3=Chen |first3=Xuedong |last4=Revilak |first4=Stephen |title=The Star Schema Benchmark and Augmented Fact Table Indexing |book-title=Performance Evaluation and Benchmarking (TPCTC 2009) |year=2009 |publisher=Springer |doi=10.1007/978-3-642-10424-4_17 |url=https://link.springer.com/chapter/10.1007/978-3-642-10424-4_17|url-access=subscription }}</ref>

* Extensibility. Dimensional models are scalable and easily accommodate unexpected new data. Existing tables can be changed in place either by simply adding new data rows into the table or executing SQL alter table commands. No queries or applications that sit on top of the data warehouse need to be reprogrammed to accommodate changes. Old queries and applications continue to run without yielding different results. But in normalized models each modification should be considered carefully, because of the complex dependencies between database tables.

* '''Extensibility (resilience to change).''' New facts or dimensions can be added without breaking existing queries so long as the fact-table grain is preserved; this allows incremental evolution of the warehouse.<ref name="kimball2013" />

* '''Integration and consistency across subject areas.''' Reusable '''conformed dimensions''' enable consistent cross-process analysis and reduce duplication in future projects.<ref name="bus">{{cite web |title=Enterprise Data Warehouse Bus Architecture |website=Kimball Group |url=https://www.kimballgroup.com/data-warehouse-business-intelligence-resources/kimball-techniques/kimball-data-warehouse-bus-architecture/ |access-date=2025-08-15}}</ref><ref name="conformed">{{cite web |title=Conformed Dimensions |website=Kimball Group |url=https://www.kimballgroup.com/data-warehouse-business-intelligence-resources/kimball-techniques/dimensional-modeling-techniques/conformed-dimension/ |access-date=2025-08-15}}</ref>

* '''Support for time-variant analysis.''' Techniques for '''slowly changing dimensions''' record attribute history so that analyses reflect the state of a dimension member at the time of each fact.<ref name="scd">{{cite web |title=Slowly Changing Dimensions |website=Kimball Group |date=2008-08-07 |url=https://www.kimballgroup.com/2008/08/slowly-changing-dimensions/ |access-date=2025-08-15}}</ref>

== Dimensional models, Hadoop, and big data ==

{{NPOV section|date=June 2018}}

We still get the benefits of dimensional models on [[Apache Hadoop|Hadoop]] and similar [[big data]] frameworks. However, some features of Hadoop require us to slightly adapt the standard approach to dimensional modelling.{{cn|date=May 2019}}

* The [[Apache Hadoop#HDFS|Hadoop File System]] is [[Immutable object|immutable]]. We can only add but not update data. As a result we can only append records to dimension tables. [[Slowly changing dimension|Slowly Changing Dimensions]] on Hadoop become the default behavior. In order to get the latest and most up to date record in a dimension table we have three options. First, we can create a [[View (SQL)|View]] that retrieves the latest record using [[Select (SQL)#Window function|windowing functions]]. Second, we can have a compaction service running in the background that recreates the latest state. Third, we can store our dimension tables in mutable storage, e.g. HBase and federate queries across the two types of storage.

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<ref name="refname4">{{cite book|author1=Ralph Kimball |author2=Margy Ross |title=The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling|edition=Second |date=April 26, 2002|publisher=Wiley|ISBN=0-471-20024-7}}</ref>

<!-- <ref name="refname5">{{cite book|author1=Ralph Kimball |author2=Margy Ross |author3=Warren Thornthwaite |author4=Joy Mundy |author5=Bob Becker |title=The Data Warehouse Lifecycle Toolkit |edition=Second |date=January 2008|publisher=Wiley|ISBN= 978-0-470-14977-5}}</ref> -->

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