Data Mart vs. Data Warehouse

A data marketplace is a subset of a data warehouse oriented to a specific business line. Data marts incorporate repositories of summarized data collected for analysis on a specific incision or unit within an organization, for exemplar, the sales department. A data warehouse is a big centralize repository of data that contains information from many sources within an constitution. The collate data is used to guide business decisions through analysis, report, and data mine tools .

Inmon vs. Kimball

Two data warehouse pioneers, Bill Inmon and Ralph Kimball differ in their views on how datum warehouses should be designed from the administration ‘s position. Bill Inmon’s approach path favours a top-down design in which the datum warehouse is the centralize data repository and the most important component of an arrangement ‘s data systems.

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The Inmon approach first builds the centralized corporate data exemplary, and the data warehouse is seen as the physical representation of this model. Dimensional data marts related to specific business lines can be created from the data warehouse when they are needed. In the Inmon model, data in the datum warehouse is integrated, meaning the data warehouse is the source of the data that ends up in the different data marts. This ensures data integrity and consistency across the organization. Ralph Kimball’s data warehouse design starts with the most important business processes. In this approach, an organization creates data marts that aggregate relevant data around subject-specific areas. The data warehouse is the combination of the organization ’ s individual data marts. With the Kimball approach, the data warehouse is the accumulate of a number of data marts. This is in contrast to Inmon ‘s approach, which creates data marts based on information in the warehouse. As Kimball said in 1997, “ the data warehouse is nothing more than the marriage of all data marts. ” *

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* Quoted from Kimball ‘s book, “ The Data Warehouse Lifecycle Toolkit ” .

Data Marts vs. Centralized Data Warehouse: Use Cases

The trace use cases highlight some examples of when to use each approach to data warehouse .

Data Marts Use Cases

  • Marketing analysis and reporting favor a data mart approach because these activities are typically performed in a specialized business unit, and do not require enterprise-wide data.
  • A financial analyst can use a finance data mart to carry out financial reporting.


Centralized Data Warehouse Use Cases

  • A company considering an expansion needs to incorporate data from a variety of data sources across the organization to come to an informed decision. This requires a data warehouse that aggregates data from sales, marketing, store management, customer loyalty, supply chains, etc.
  • Many factors drive profitability at an insurance company. An insurance company reporting on its profits needs a centralized data warehouse to combine information from its claims department, sales, customer demographics, investments, and other areas.  
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Are Data Marts Still Relevant in a Cloud Architecture?

Organizations that want to make data-driven decisions are faced with a challenge—when should they use data marts versus data warehouses to analyze and report on the datum they collect ? Data marts can guide tactical decisions at a departmental grade while data warehouses guide high-level strategic business decisions by providing a consolidate watch of all organizational data. There are two approaches to this challenge that reflect the authoritative Bill Inmon versus Ralph Kimball argument :

  • The first approach, based on Bill Inmon’s opinion, is to build the data warehouse as the centralized repository of all enterprise data, from which data marts can be created later on to serve particular departmental needs.
  • The second approach, in line with Ralph Kimball’s thoughts, is to initially create separate data marts that hold aggregate data on the most important businesses processes, before merging these data marts as a data warehouse later on.
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Data warehouses provide a commodious, single repository for all enterprise data, but the cost of implementing such a system on-site is much greater than building data marts. On-premise datum warehouse systems besides take a significant distance of time to build. however, cloud-based data warehouse services have made data warehouses much easier and quicker to set up, and cheaper to run, which negates the motivation for a “ begin small ” approach that recommends starting with data marts and merging them late on into a data warehouse. Since cloud-based data warehouse services are cost-efficient, scalable, and extremely accessible, organizations of all sizes can leverage cloud infrastructure and build a centralized data warehouse foremost .

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