| Home >Tutorial > Entity Relational Modeling vs. Dimensional Modeling > Relational vs Dimensional |
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| Entity Relational Modeling vs. Dimensional Modeling |
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| Relational vs Dimensional |
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| Relational Data Modeling |
Dimensional Data Modeling |
| Data is stored in RDBMS |
Data is stored in RDBMS or Multidimensional databases |
| Tables are units of storage |
Cubes are units of storage |
| Data is normalized and used for OLTP. Optimized for OLTP processing |
Data is denormalized and used in datawarehouse and data mart. Optimized for OLAP |
| Several tables and chains of relationships among them |
Few tables and fact tables are connected to dimensional tables
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| Volatile(several updates) and time variant |
Non volatile and time invariant |
| SQL is used to manipulate data |
MDX is used to manipulate data |
| Detailed level of transactional data |
Summary of bulky transactional data(Aggregates and Measures) used in business decisions |
| Normal Reports |
User friendly, interactive, drag and drop multidimensional OLAP Reports |
| Typical data design used for business transaction systems |
Data design used for analysis systems |
| Goal – reduce every piece of information to it’s simplest form – a debit transaction, a customer record, an address. |
Goal – break up information into ‘Facts’ – things a company measures and ‘Dimensions’ - how we measure them: by time, region, or customer |
| Suited for concurrent handling of many small transactions by many users. Only a limited amount of data history is normally kept |
Suited for reading or analyzing large amounts of data by a modest numbers of users. Many years of data history may be kept. |
| User is usually constrained by an application that understands the data design. Users are typically operations staff. |
This simpler data design makes it easier for users to analyze data in any way they choose. Users are typically analysts, company strategists, or even executives |
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