Dimensional Data Modeling Training

About Dimensional Data Modeling Course :

The Dimensional Data Modeling course approach is an optimization of data structuring for warehouse storage. Data retrieval can be made faster with dimensional modeling by optimizing the database. Ralph Kimball developed the concept of Dimensional Modelling based on "fact" and "dimension" tables. Within a data warehouse, a dimensional model is used to read, summarize, and analyze numerical data like values, balances, counts, weights, etc. Contrary to relation models, an Online Transaction System optimizes the addition, updating and deletion of data in real time.

There are specific advantages to these techniques for storing data that are unique to these dimensional and relational models.The normalization and ER models, for example, work together to reduce redundancy in data in the relational mode. However, data warehouse dimensions are organized so that locating information and generating reports are easier.

Dimensional Data Modelling : Key Features :

Due to its unique way of analyzing data in multiple Data Warehouses, Data Dimensional Modeling has gained popularity. Following are the three main features of DDM :

• Simple to Understand :

  It enables developers to quickly and easily design and build databases and schemas that can be easily understood by business users. A simple relationship between Dimensions and Facts can be read and understood.

• Ensure data quality :

  Facts and Dimensions are loaded into DDM schemas because the schemas enforce data quality. Foreign keys are used as constraints to prevent the loading of fraudulent data onto Schemas because Dimension and Fact are tying together.

• Performance optimization :

  DDM divides data into Dimensions and Facts and ties them together with foreign keys, reducing the amount of redundant data. Optimized data is stored in a smaller volume and is retrievable more quickly since it is stored in an optimized format.

Modeling Dimensional Data: The Components

The five most important components of any DDM are as follows. The following are a few of them.

• Dimension
• Facts
• Attributes/Measures
• Fact Tables
• Dimension Tables

Business dimensions are set of information containing information specific to a particular business measurement. For example, topographical information, item data, contact information, and so on. The creation of a Fact is contextualized by dimensions.

Various business processes produce measurements, metrics, transactions, etc., which form the facts. The data usually consists of business transactions and measurements.

A dimension table has attributes as its elements. An account Dimension, for instance, could have the following attributes :

• First Name
• Last Name
• Date of Birth, etc.
4. Fact Tables :

In businesses, facts tables are used to store measurements or transactions. A Fact can, for example, store the number of items requested in Internet business. The rows in Fact Tables are usually huge, and the columns are few. Unlike Dimension Tables, Fact Tables have keys that are known as Foreign Keys.

5. Dimension Tables :

Business Dimensions are stored in Dimension Tables and provide some context for Facts. A link between the descriptions and the fact tables can be found in them. Columns in Dimension Tables are usually large, and there are fewer rows. Dimension Tables are usually optimized tables. Here are a few examples:

• Names, addresses and phone numbers can be viewed in contact information.
• You can view product information by product-code, brand, color, etc.
• You can view store information based on your city, state, and country.

Duties and Responsibilities of the Data Modeler :

• Transform long-term solutions into appropriate data models by understanding and translating business needs.
• Developing conceptual data models and data flows with the Application Development team.
• To ensure high data quality, and to reduce duplicate data, develop logical and physical data models as per best practices.
• Ensure logical and physical data models are optimized and updated to support new projects.
• Develop and maintain conceptual, logical, and physical data models, corresponding metadata, and their links.
• Identify and develop best practices for standard naming conventions and coding practices to ensure consistency throughout the data model.
• Assess data model reusability opportunities in new environments.
• From databases and SQL scripts, reverse engineer a physical data model.
• Assess variances and discrepancies in data models and physical databases.
• Verify the accuracy and completeness of business data objects.
• Analyze the challenges and recommend solutions to integrate data-related systems.
• Developing data models in accordance with company standards.
• Ensure that System Analysts, Engineers, Programmers, and others know what project limitations and capabilities, and performance requirements, and how projects should be integrated.
• Improve the efficiency and performance of existing software by reviewing modifications.
• Review new application design and recommend any necessary corrections.

Modeling Dimensional Data : Types of Dimensions

When dealing with Dimensional Data Modelling, there are nine types of measurements. Here they are :

1. Conformed Dimension

2. Outrigger Dimension

3. Shrunken Dimension

4. Role-Playing Dimension

5. Dimension to Dimension Table

6. Junk Dimension

7. Degenerate Dimension

8. Swappable Dimension

9. Step Dimension

• Conformed Dimension :

  Dimensions that are Conformed are Dimensions that are similar to all Facts they are associated with. In Data Warehouse, Dimensions and Facts can be categorized using this type of Dimension.

• Outrigger Dimension :

  As the name suggests, an Outrigger Dimension is a type of Dimension that connects different Dimension Tables.

• Shrunken Dimension :

  Data entities of a Shrunken Dimensions subset can be viewed as a subset of more general data entities. As in the Subset Dimension, the common attributes between the subset and general set are represented in the same way as in the General Dimension.

• Role-Playing Dimension :

  Role-Playing Dimensions are a type of table that has multiple valid relationships depending on what else they relate to. Role-Playing Dimensions, such as time and customers, are common examples. They can be utilised in situations where certain Facts don't share the same concept.

• Dimension to Dimension Table :

  The Star Schema of a data warehouse contains tables that correspond to this type. Each Fact Table is surrounded by several Dimension Tables in a Star Schema. In every Dimension Table, there is a corresponding Dimension.

• Junk Dimension :

  The Junk Dimension refers to a dimension constituted by two or more low-cardinality Facts of related nature. These tables are also used to reduce the dimensions of dimension tables and to reduce the columns from fact tables.

• Degenerate Dimension :

  Alternatively, a fact dimension is referred to a degenerate dimension. Dimensions are built by using attribute columns of Fact Tables as columns in dimension definitions. To avoid duplication of data, Fact Tables are sometimes used.

• Swappable Dimension :

  During query processing, Swappable Dimensions can change into multiple versions of themselves. As with the original Dimension, this Dimension has a different structure and fewer data points than the original Dimension. This Dimension also has different inputs and outputs.

• Step Dimension :

  The purpose of this Dimensional Data Modeling online course is to explain how a particular step fits into the wider process. There are numerous steps required to complete each step, each of which is assigned its own step number.

Dimensional Data Modeling Benefits :

Knowing what Dimensional Data Modeling is and why it is so important, you can imagine how many benefits it provides to the company. Below are a few of those benefits :

1. Dimensional tables are used to store the history of data and they allow easy access to data across a wide array of businesses.

2. There is no need to alter existing Dimensions and Facts in the Schema to introduce new Dimensions.

3. Tables with dimensions and facts are easier to read and understand compared to regular tables.

4. The business understands the dimension models because they are written in terms of the business.

5. When creating a Data Warehouse Schema, dimensioned data modelling optimizes performance. It aids in reducing the amount of redundancy in data and also reduces joins between tables.

6. Performance is also boosted by using the Dimensional Data Model. Querying is more efficient because the table is denormalized.

7. The change can be accommodated comfortably with Dimensional Data Models. Columns can be added to Dimension Tables without affecting existing Business Intelligence applications that use these tables.

Dimensional Data Modeling Limitations :

Even though Dimensional Data Modelling is very important for any organization, there are certain limitations to consider for companies when implementing it. The following limitations are listed :

• Data domain knowledge is necessary for designing and creating Schemas.

• Loading the Data Warehouses with records from multiple operational systems is difficult to maintain the integrity of Facts and Dimensions.

• If an organization adopts Dimensional techniques and changes the way they do business, then it is difficult to modify its Data Warehouse operations.

• The DDM technique has despite these limitations been found to be among the easiest and most efficient methods of handling data in Data Warehouses.

Dimensional Modeling Objectives :

Dimensional Modeling Serves the Following Purposes :

1. The Goal is to provide an architecture that is easy to understand and to write queries for end-users.

2. To make queries as efficient as possible. Those goals are achieved by reducing the number of tables and the relationships among them.

Dimensional Modeling Advantages :

Dimensional Modeling has the Following Benefits :

• Dimensional Modeling is Easy to Understand : Database schemas can be created using dimension modeling methods that are easy to hold and understand by warehouse designers. Diagrams do not require extensive training, and different elements of the data do not need to be connected in complex ways. In order to increase the quality of data, it is necessary to run a three-dimensional model.

  The star schema enables warehouse administrators to enforce referential integrity checks on their data warehouses. Since the fact information key is compiled from the essentials of the dimensions for which it is related, a factual record is deemed active if its dimensions have been duly described in the database and are also present.

  DBAs can protect warehouse data from corruption by enforcing foreign key constraints as a form of referential integrity check.

• Aggregates can be Used to Optimize Performance : When the size of a data warehouse grows, performance optimization becomes more and more critical. Inefficient warehouses will leave customers discouraged when they are forced to wait hours to receive a reply. Optimizing query performance using aggregates is one of the simplest methods.

Career Outlook and Salary :

In order to become a dimensional data modeler, you must work with data analysts and architects to identify key information and dimensions to support the system requirements of the company or client. You must make sure that the data are managed and kept in a valuable state. For interpretation of results, it is crucial to have domain knowledge.

Dimensional data modelers typically begin their careers as analysts, before gaining experience in the lower ranks and moving up the hierarchy. A Dimensional data modeler is assured of a high salary and much scope for learning. Data modelers earn an average salary of 786K on the market, according to Glassdoor, Dimensional Data Modelers also earn a good salary, which is why there are ample monetary and career opportunities.