# Er modeling and dimensional modeling. Data Modeling Interview Questions 2019-01-31

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## Difference between ER Modeling and Dimensional Modeling

Each dimension is an equivalent entry point into the fact table, and this symmetrical structure allows effective handling of complex queries. We will using this database for all hand-on in the remainder of this tutorials MyFlix is a business entity that rents out movies to its members. Table Names Standardization: Giving a full name to the tables, will give an idea about data what it is about. Why are recursive relationships are bad? A fact is something that is quantifiable Or measurable. In a distributed relational database we can co-locate records with the same primary and foreign keys on the same node in a cluster.

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## Difference between ER

A logical data model is the version of the model that represents all of the business requirements of an organization. First, the dimensional model is a predictable, standard framework. We have also learnt about various types of changing dimensions. Explain First , Second, Third Normalization? Some general guidelines are listed below that may be used as a prefix or suffix for the table. When should you consider denormalization? When should it be used? This is important because this information help us decide what columns are required to be stored in our fact table. In Figure 1, the facts are Dollars Sold, Units Sold, and Dollars Cost. With bio Ralph Kimball is the founder of the Kimball Group and Kimball University where he has taught data warehouse design to more than 10,000 students.

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## Creating Entity Relational or Dimensional Models

A sum function on balance does not give a useful result but max or min balance might be useful. Entities are represented as class diagrams. That is - dimensions are the 'things' about which something is being spoken. A conceptual model is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. As a result joins on Hadoop for two very large tables are quite expensive as data has to travel across the network. This could for instance be a sales situation in a retail store.

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## The 101 Guide to Dimensional Data Modeling

Step 3: Identify the attributes or properties of dimensions Now that we have decided we need 3 tables to store the information of 3 dimensions, next we need to know what are the properties or attributes of each dimension that we need to store in our table. Does Star schema and snow flake schema come under dimensional model? On the other side, dimensional model is not a good solution if your primary purpose of your data modeling is to reduce storage space requirement, reduce redundancy, speed-up loading time etc. What is data sparsity and how it effect on aggregation? The picture of this design is displayed below. Because of the more complex nature of these relationships, we will need slightly more complex methods of mapping them to a schema and displaying them in a stylesheet. These modelsare difficult to read and understand unless trained in the model methodology. Since a same attribute may be present in several entities, the attribute names and data types should be standardized and a conformed dimension should be used to connect to the same attribute present in several tables.

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## Difference between ER modelling and Dimensional Modelling

An example would be a Dimension called 'Product' which is a table containing product specific information fed into the Fact. Your diagram workspace should now look like the one shown below. It has both logical and physical model. A recursive relationship occurs when there is a relationship between an entity and itself. In relational terms, every column in a table must be functionally dependent on the whole primary key of that table.

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## Data modeling: Entity relationship (E

These data marts can be built on top of the data warehouse. The dimensions must be defined within the grain from the second step of the 4-step process. How do you resolve Many-Many Relationship? One of the important things to note is the standardization of the data model. It is oriented around understandability and performance. Each of these entities usually turns into a physical table when the database is implemented. Enterprise Data Modeling is sometimes called as a global business model and the entire information about the enterprise would be captured in the form of entities.

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## Difference between ER

Lookup table for product will consist of all products available. At its core, a data model depicts the underlying structure of an enterprise's data and the business rules governing it. What is a Surrogate key? Each star join will have four to 12 dimension tables. In our case, we need the information on a daily basis. In this example fact table will have three columns Product, Geographical region, Revenue. It is good for ad hoc query analysis.

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## Data Modeling Concepts

Because of the more complex nature of these relationships, we will need slightly more complex methods of mapping them to a schema and displaying them in a stylesheet. Standardization Needs Modeling data: Several data modelers may work on the different subject areas of a data model and all data modelers should use the same naming convention, writing definitions and business rules. If you want to read a quick and simple guide on dimensional modeling, please check our Guide to dimensional modeling. Recursive relationships are an interesting and more complex concept than the relationships you have seen in the previous chapters, such as a one-to-one, one-to-many, and many-to-many. Dimensional Data Modeling : It is a modeling technique used in data warehousing systems.

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