What is a warehouse and what should a modern data warehouse contemplate?


A definition of what a warehouse is in relation to data., or more specifically a data warehouse, could be the following: A data warehouse is a system used to report and analyze data. The Data warehouses are central repositories of data. that integrate one or more disparate data sources. They store current and historical data and are used to create analytical reports for knowledge workers across the enterprise.


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To fully understand what the warehouse is and the data warehousing process The three general layers must be taken into account, although they are not mandatory, are used frequently, depending on the data storage architecture:

  • Integration layer in which the extracted raw data is stored and prepared.
  • Main data layer Deposit where the data is adapted to the homogeneous data model of the company.
  • Data marts and layer of strategic marts, which provides specific excerpts from the corporate data warehouse.

These layers often have complex internal transformation and business logic and, sometimes, cannot be easily distinguished as discrete building blocks. Recent developments have simplified this and allow for significant optimizations.

Understand the different components to know what a warehouse is

Today, Those who know what a repository is understand that this data repository no longer only feeds on the information generated by internal systems. Those times are behind us and the reality at the data architecture level of any organization is different. New formats, alternative data sources and different types of information They give complexity to an essential structure in the knowledge generation process.

Between the elements that make up the data warehouse, and whose understanding allows to know what is the warehouse, are the following:

  1. Data access services: Unlike traditional information stores, current ones require expanded data access service capabilities. Only in this way is it possible to guarantee access to NoSQL sources, flexible switching between data access methods, unstructured data transformation or adaptation to cloud sources. , access to NoSQL sources and quality and transformation capabilities for geocoding and unstructured data. All these different technologies must be managed and monitored by the logical data warehouse. It is important that data storage solutions offer a high degree of flexibility in this area.
  2. Data preparation: This component is responsible for performing the data reviews and repairs. Thanks to your intervention, source reliability can be guaranteed (data lineage), as well as that the minimum conditions of completeness are met, integrity and other attributes of data quality. The preparation of data is especially important for working with unstructured data, Yes OK, it is precisely these that require the participation of specialists in the process, supported by the right tools.
  3. Modeling– In a modern data storage environment, modeling needs are closely related to the ability to deal with different data semantics from various sources.. To understand what a warehouse is is to assume that The modeling approach should not be unique, but adaptable to business processes and the needs of the organization.
  4. Metadata: data on data takes on importance in new warehouses, since they need power absorb changes in the way data is represented. Metadata enables context and nature to be discovered regardless of its actual representation.

Today's diversified data warehouse must be ready to meet user needs., whether it is demands related to local information, as if it is big data or the cloud.

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