Database Table Denormalization Example

Bigdata technologies such as Hive, HBase, NoSQL taking over industry, thanks to its fast and distributed processing. Hadoop works on commodity hardware, so it is cheap too. Every organization wants to move its data to Bigdata world. If you are reading this article, your organization may be planning to migrate your relational database to Hadoop. Hadoop works best with denormalized tables. In this article, we will check how database Table denormalization works with an example. What is Table Denormalization? Before jumping into denormalization process, let us first understand what is…

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Data Warehouse fact-less fact Tables and Examples

A Data Warehouse fact-less fact table is a fact that does not have any measures stored in it. This table will only contain keys from different dimension tables. The fact-less fact is often used to resolve a many-to-many cardinality issue. Types of Fact-less fact tables in Data Warehouse? There are two types of fact-less fact tables Event capturing fact-less fact This type of fact table establishes the relationship among the various dimension members from various dimension tables without any measured value. For examples, Student attendance (student-teacher relation table) capturing table…

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Data Warehouse Two-tier Architecture in Details

Usually, data warehouse adapts either two-tier or three-tier architecture. We have discussed three-tire architecture in my other post 'Data Warehouse Three-tier Architecture'. In this article, we will discuss on the data warehouse two-tier architecture. Data Warehouse Two-tier Architecture  The data warehouse two-tier architecture is a client - server application. There is a direct communication between client and data source server, we call it as data layer or database layer. Usually, there is no intermediate application between client and database layer. Below diagram depicts data warehouse two-tier architecture: As shown in…

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Data Warehouse Surrogate Key Design – Advantages and Disadvantages

If you are working on Data warehouse project, than you might have heard lot about surrogate keys. Surrogate keys are widely accepted data warehouse design standard. In this article, we will check data warehouse surrogate key design, advantages and disadvantages. What are surrogate keys in Data warehouse? If you are a data warehouse developer, that you might be thinking what is surrogate key? How and where it is being used? You will get answers to all your questions here. Data warehouse surrogate keys are sequentially generated meaningless numbers associated with…

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Data Warehouse Project Life Cycle and Design

Building data warehouse is not different than executing other development project such as front-end application. You need to be technical and business person who understand technical details along with organizations business to successfully design and implement data warehouse project. In this article, we will check what the data warehouse project life cycle is and different steps in designing data warehouse project! Steps of Data Warehouse Project Life Cycle Design Following are steps generally followed in any data warehouse projects you can consider these steps as data warehouse lifecycle: Requirements gathering…

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Data Warehouse Fact Constellation Schema and Design

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…

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Various Data Warehouse Design Approaches:Top-Down and Bottom-Up

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…

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