This leads to clear identification of business concepts and avoids data update anomalies. “Ralph Kimball Data Warehouse Architecture”. Updated new edition of Ralph Kimball’s groundbreaking book on dimensional modeling for data warehousing and business intelligence! The first edition of. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball’s classic guide is.
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We are living in the age of a data revolution, and more corporations are realizing that to lead—or in some cases, to survive—they need to harness their data wealth effectively. The data warehouse, due to its unique proposition as the integrated enterprise repository of data, is playing an even more important role in this situation.
There are two prominent architecture styles practiced today to build a data warehouse: This paper attempts to compare and contrast the pros and cons of each architecture style and to recommend which style to pursue based on certain factors. In terms of how to architect the data warehouse, there are two distinctive schools of thought: They both view the data warehouse as the central data repository for the enterprise, primarily serve enterprise reporting needs, and they both use ETL to load the data warehouse.
The key distinction is how the data structures are modeled, loaded, and stored in the data warehouse. This difference in the architecture impacts the initial delivery time of the data warehouse and the ability to accommodate datwaarehousing changes in the ETL design. When a data architect is asked to design and implement a data warehouse from the ground up, what architecture style should datawrehousing or she choose to build the data warehouse?
The Inmon approach to building a data warehouse begins with the bby data model. This model identifies the key subject areas, and most importantly, the key entities the business operates with and cares about, like customer, product, vendor, etc.
From this model, a detailed logical model is created for each major entity. For example, a logical model will be built for Customer with all the details related to that entity. There could be ten different entities rlaph Customer. All the details including business keys, attributes, dependencies, participation, and relationships will be captured in the detailed logical model. The key point here is that the entity structure is built in normalized form.
Data redundancy is avoided as much as possible.
Data Warehouse Design – Inmon versus Kimball
This leads to clear identification of business concepts and avoids data update anomalies. The next step is building the datawarejousing model. The physical implementation of the data warehouse is also normalized. This normalized model makes loading the data less complex, but using this structure for querying is hard as it involves many tables and joins.
So, Inmon suggests building data marts specific for departments. The data marts will be designed specifically for Finance, Sales, etc. Any data that comes into the data warehouse is integrated, and the data warehouse is the only source of data for the different data marts. This ensures that the integrity and consistency of data is kept intact across the organization.
The Kimball approach to building the data warehouse starts with identifying the key business datwarehousing and the key business questions that the data warehouse needs to answer. The key sources operational systems of data for the daawarehousing warehouse are analyzed and documented.
ETL software is used datawareehousing bring data from all the different sources and load into a staging area. From here, data is loaded into a dimensional model.
Here the comes the key difference: The fundamental concept of dimensional modeling is the star schema. In the star schema, there is typically a fact table surrounded by many dimensions.
The fact table has all the measures that are relevant to the subject area, and it also has the foreign keys from the different dimensions that surround the fact. The dimensions are denormalized completely so that datawareuousing user can drill up and drill down without joining to another table.
Multiple star schemas will be built to satisfy different reporting requirements. So, how is integration achieved in the dimensional model? The key dimensions, like customer and product, that are shared across the different facts will be built once and be used by all the facts Kimball et al. Datawarehousiing ensures that one thing doncepts concept is used the same way across the facts.
This is the document where the different facts are listed vertically and the conformed dimensions are listed horizontally. Where ever the dimensions play a foreign key role in the fact, it is marked in the document.
This serves as an anchoring document showing how the star schemas are built and what is left to build in the data warehouse. Now that we have seen the pros and cons of the Kimball and Inmon approaches, a question arises. Which approach should be used when? This question is faced by data warehouse architects every time they start building a data warehouse. Here are the deciding factors that can help an architect datawardhousing between the two:.
It has been proven that both the Inmon and Kimball approach work for successfully delivering data warehouses. In a hybrid model, the data warehouse is built using the Inmon model, and on top of the integrated data warehouse, the business process oriented data marts are built using the star schema for reporting.
We cannot generalize and say that one approach is better than the other; they both have their advantages and disadvantages, and they both work fine in different scenarios. The architect has to select an approach for the data warehouse depending on the different factors; a few key ones mimball identified in this paper.
Accessed May datawagehousing, Building the Data Warehouse, Fourth Edition. Accessed May 23, The Data Warehouse Toolkit: Accessed May 26, Accessed May 25, He is passionate about data modeling, reporting and analytics.
This was an editing error that I did not catch. It has now been corrected. Thank you for being a reader. Would really appreciate your opinion on some coursework I have for Business intelligence. GBI are a world class bike company with employees. They are a process orientated organisation and are located in US, with Three separate facilities that handle datawarehoysing, distribution and manufacturing.
They have a subsidiary company in Europe datawarehouxing two facilities one for manufacturing the other for distribution. They want to implement a BI strategy for solutions to gain competitive advantage, analyse data in regards to key performance indicators, account for local differences in its market and act in an agile manner to moves competitors might make, and problems in the supplier and dealer networks.
Which approach to you think is the most appropriate? GBI is a fake company used worldwide the full case can be found online. Would be much appreciated. I am looking for case studies of practical, real world implementations of 3NF physical table structures for atomic data warehouses a la Inmon CIF.
Ralph Kimball Data Warehouse Architecture
These should be non-teradata deployments, since that vendor recommends 3NF as the DW schema. I do not know anyone who has successfully done that except teradata but even it requires dimensional views to be usable.
I do know several attempts that failed. The biggest issues have always been the increased complexity and reduced performance caused by mandatory time variant extensions to 3NF data structures. If anyone has references or links to case studies of successful 3NF iimball data warehouse deployments, please share. Very well written article. Providee balanced and easy to understand comparison between the kimbwll approaches. The brief description of hybrid approach was quiet helpful. Introduction We are living in the age of a data revolution, and more corporations are realizing that to lead—or in some cases, to survive—they need to rakph their data wealth effectively.
Background In terms of how to architect the data warehouse, there are two distinctive schools of thought: The Inmon Approach The ,imball approach to building a data warehouse begins with the corporate data model. I really enjoyed this article.
Nicely organized and written.
Kimball vs. Inmon in Data Warehouse Architecture