Data Warehouse related posts
The Data warehouse physical data model describes how the model will be built in the database. A physical database model shows all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between tables. Read: Data Warehouse Project Life Cycle and Design Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Features of Data Warehouse Physical Data Model Following are…
A Data warehouse logical data model describes model in more detail compared to the Data Warehouse conceptual data model. A data warehouse logical data model describes the data in as much detail as possible, this model does not describe how the model is implemented. Read: Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Data Warehouse Three-tier Architecture in Details Features of Data Warehouse Logical Data…
A Data warehouse conceptual data model is nothing but a highest-level relationships between the different entities (in other word different table) in the data model. Read: Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Data Warehouse Three-tier Architecture in Details Features of Data Warehouse Conceptual Data Model Following are the features of conceptual data model: This is initial or high level relation between different entities…
Basically, Data warehouse fact constellation schema is viewed as a collection of many star schemas. For each star schema or snowflake schema it is possible to create Fact Constellation schema. This schema is one of the widely used data warehouse design methodology and is also called Galaxy schema. Sophisticated application required the Fact constellation schemas. Read: Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Representation…
Data Warehouse design approaches are very important aspect of building data warehouse. Selection of right data warehouse design could save lot of time and project cost. There are two different Data Warehouse Design Approaches normally followed when designing a Data Warehouse solution and based on the requirements of your project you can choose which one suits your particular scenario. These methodologies are a result of research from Bill Inmon and Ralph Kimball. Bill Inmon - Top-down Data Warehouse Design Approach “Bill Inmon” is sometimes also referred to as the "father…
Extraction is the first step of the ETL(Extract, Transform and Load) process. Once the data is extracted, you can transform it and load to target data warehouse. Extraction is the process of extracting data from the source system for further use in the data warehouse environment. Related Reading: Data Warehouse Fact Constellation Schema and Design Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Data warehouse is an OLAP system, typically source system includes the transaction business processing application. For example, it could be sales order entry…
A dimension is something that qualifies the quantity or measures. Dimensions store the textual descriptions of the business. With help of dimension you can easily identify the measures.The different types of dimension tables are available as below: Read: Types of Fact Tables in a Data Warehouse Data Warehouse Fact Constellation Schema and Design Data Warehouse Three-tier Architecture in Details Types of Dimension Tables in a Data Warehouse Below are the commonly used dimension tables in data warehouse: Conformed Dimension A conformed dimension is the dimension that is shared across multiple data…
A fact table stores quantitative information for analysis and is often denormalized. A fact table holds the measures, metrics and other quantifiable information. The different types of fact tables are as explained below: Read: Data Warehouse fact-less fact and Examples Slowly changing dimension Types of Dimension Tables in a Data Warehouse Types of Facts There are three types of facts: Additive Facts Additive facts can be used with any aggregation function like Sum(), Avg() etc. Example is Quantity, sales amount etc. Semi Additive Facts Semi-additive facts are those where only…
A dimension is a fast changing or rapidly changing dimension if one or more of its attributes in the table changes very fast and in many rows. Handling rapidly changing dimension in data warehouse is very difficult because of many performance implications. As you know slowly changing dimension type 2 is used to preserve the history for the changes. But the problem with type 2 is, with each and every change in the dimension attribute, it adds new row to the table. If in case there are dimensions that are…
Data warehouse Snowflake schema is extension of star schema data warehouse design methodology, a centralized fact table references to number of dimension tables, however, one or more dimension tables are normalized i.e. dimension tables are connected with other dimension tables. Primary Keys from the dimensions flows into fact table as foreign key. Star Schema model in Data Warehouse Data Warehouse Fact Constellation Schema and Design Snowflake schema increases the level of normalization in data, the dimension table is normalized into multiple tables. This schema has a disadvantage in terms of data retrieval, we…