BILL INMON VS RALPH KIMBALL PDF

If you are working in data warehousing project or going to work on data warehouse project, the two most commonly designed methods are introduced by Ralph Kimball and Bill Inmon. Lets understand the basic difference between Ralph Kimball and Bill Inmon approaches towards data warehouse. Bill Inmon, has defined a data warehouse as a centralized repository for the entire enterprise. The top-down approach is designed using a normalized enterprise data model. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse.

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In his recent article "Turbocharge Your Porsche - Buy An Elephant", Bill Inmon, "the father of data warehousing", criticizes Cloudera for associating Big Data with the data warehouse, two totally unrelated terms according to him. His old rival, Dr. Ralph Kimball, is taking the opposing view by presenting a webinar with Cloudera about building a data warehouse with Hadoop. This marks a new round in the fight between these two academic geezers, a decades-long argument over what is a data warehouse and how it should be implemented.

Inmon and Kimball published two radically different approaches in the s on how an organization should manage its data for reporting and analysis. Once the data warehouse is fully designed and put in place, only then can small data marts be added for different departments to query data from the central data warehouse and store it in various dimensions.

Kimball sees this differently. He suggests that an organization should first build small data marts for each department. The data marts should contain facts and dimensions relevant to the business area and store them in a star or snowflake schema. As far as Kimball is concerned, the data warehouse is essentially a union of all the data marts. Accordingly, his version is called bottom-up. Related Reading : Data Mart vs. Data Warehouse. Their methodologies have evolved over the years.

In a presentation made by Inmon himself, he disses Kimball for only realizing now what his approach suggested over 20 years ago. Why does Inmon criticize Cloudera for mixing up data warehouses with Big Data? Because according to him, a data warehouse is a methodology while Big Data is a technology.

Therefore, these terms are not in the same category and cannot be compared with one another. Kimball's approach of data marts seems to be more popular beyond the walls of the academia since companies prefer to start with something small that works rather than spec endlessly only to create a monster. Sometimes there is a data warehouse in place. Although Inmon argues that a data warehouse is just an architecture, people use the term on a day to day basis to refer to an actual technology e.

In that sense, Apache Hadoop could be part of the data warehouse, for example, as cheap data storage or as part of the data processing performed before analysis. Ironically, Big Data may fulfill the vision that Inmon preaches - a central repository with one version of the truth where structured and unstructured data can all be stored together.

Inmon insists on seeing a data warehouse as distant from Big Data as a Porsche is from an elephant. Also, if you are going to have one central data warehouse with all the information, it is going to have to handle data that comes in high volume, velocity, and variety. Why yes, it is! Inmon himself argues in his latest architecture for the need to store a variety of data as part of the data warehouse.

If so, why would Inmon protest so harshly against mentioning Big Data and data warehouse in the same sentence? Could there be another reason?

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Kimball vs. Inmon Data Warehouse Architectures

When it comes to data warehouse designing, two of the most widely discussed approaches are the Inmon method and Kimball method. For years, people have debated over which one is better and more effective for businesses. Initiated by Ralph Kimball, this data warehouse concept follows a bottom-up approach to data warehouse architecture design in which data marts are formed first based on the business requirements. The primary data sources are then evaluated, and an Extract, Transform and Load ETL tool is used to fetch different types of data formats from several sources and load it into a staging area. This model partitions data into fact table, which is numeric transactional data or dimension table, which is the reference information that supports facts. The star schema is the fundamental element of dimensional modeling in which a fact table is bounded by several dimensions.

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The differences between Kimball and Inmon approach in designing data-warehouse

In his recent article "Turbocharge Your Porsche - Buy An Elephant", Bill Inmon, "the father of data warehousing", criticizes Cloudera for associating Big Data with the data warehouse, two totally unrelated terms according to him. His old rival, Dr. Ralph Kimball, is taking the opposing view by presenting a webinar with Cloudera about building a data warehouse with Hadoop. This marks a new round in the fight between these two academic geezers, a decades-long argument over what is a data warehouse and how it should be implemented. Inmon and Kimball published two radically different approaches in the s on how an organization should manage its data for reporting and analysis. Once the data warehouse is fully designed and put in place, only then can small data marts be added for different departments to query data from the central data warehouse and store it in various dimensions.

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Inmon or Kimball: Which approach is suitable for your data warehouse?

When it comes to designing a data warehouse for your business, the two most commonly discussed methods are the approaches introduced by Bill Inmon and Ralph Kimball. Debates on which one is better and more effective have lasted for years. But a clear-cut answer has never been arrived upon, as both philosophies have their own advantages and differentiating factors, and enterprises continue to use either of these. Inmon defines a data warehouse as a centralised repository for the entire enterprise. Dimensional data marts are created only after the complete data warehouse has been created. Thus, the data warehouse is at the centre of the corporate information factory CIF , which provides a logical framework for delivering business intelligence.

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