![]() ![]() It wants to make this data available to business users and customers to create their own reports and do some analysis. Uses traditional OLAP modeling constructs (cubes, dimensions, measures).Īn organization has data stored in a large database. Internally, metadata is inherited from OLAP modeling constructs (cubes, dimensions, measures). Uses relational modeling constructs (model, tables, columns). There are two primary types of semantic models: Traditionally, the semantic layer is placed over a data warehouse for these reasons. Data is often integrated from multiple sources.Time-oriented calculations are included.Business logic and calculations are defined.Aggregation behaviors are set so that reporting tools display them properly.This is mostly due to the nature of a typical semantic layer: Semantic modeling is predominately used for read-heavy scenarios, such as analytics and business intelligence (OLAP), as opposed to more write-heavy transactional data processing (OLTP). Also, usually columns are renamed to more user-friendly names, so that the context and meaning of the data are more obvious. This makes it easier for end users to query data without performing aggregates and joins over the underlying schema. ![]() ![]() Semantic modeling provides a level of abstraction over the database schema, so that users don't need to know the underlying data structures. There is no simple way to relate these values without a model that describes the relationship. For example, an inventory database might track a piece of equipment with an asset ID and a serial number, but a sales database might refer to the serial number as the asset ID. Organizations often have their own terms for things, sometimes with synonyms, or even different meanings for the same term. This is because OLAP databases are optimized for heavy read, low write workloads.Ī semantic data model is a conceptual model that describes the meaning of the data elements it contains. OLAP systems were designed to help extract this business intelligence information from the data in a highly performant way. Therefore, retrieving answers from these databases is costly in terms of time and effort. The databases that are used for OLTP, however, were not designed for analysis. Often they contain a great deal of information that is valuable to the organization. These databases usually have records that are entered one at a time. The databases that a business uses to store all its transactions and records are called online transaction processing (OLTP) databases. It can be used to perform complex analytical queries without negatively affecting transactional systems. Online analytical processing (OLAP) is a technology that organizes large business databases and supports complex analysis. ![]()
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