Dimensional Modeling tutorial – OLAP, data warehouse design. In this dimensional modeling tutorial, we intend to teach people with basic SQL and relational database. Store Dimension. The Data Warehouse Toolkit. Data warehousing with MySQL, a free and popular database, has never been made easier with this step-by-step tutorial on building dimensional data warehouses. Topics include star-schema modeling, populating (Extract, Transform, and Load: ETL), testing, and dimensional querying. PDF Questions & Answers. Exam Code: 070-463. Exam Name: Implementing a Data Warehouse with Microsoft SQL Server 2012/2014. Updated: September 18, 2017. Total Q&As：230. Microsoft 070-463 Most Reliable Questions Free Download.

Dimensional Modeling tutorial – OLAP, data warehouse design In this dimensional modeling tutorial, we intend to teach people with basic SQL and relational database design skills. In this tutorial we show you the dimensional modeling developed by the legendary Ralph Kimball of the Kimball Group. Dimensional Modeling – Definition Many data warehouse designers use Dimensional modeling design concepts to build data warehouses.

Dimensional model is the underlying data model used by many of the commercial OLAP products available today in the market. In this dimensional model, we store all data in just two types of tables. Download Xneat Clipboard Manager. They are Fact Tables and Dimension Tables. The Fact table contains the main facts or measures.

Fact table links to many dimension tables thru foreign keys. We call this resulting schema as star schema because it looks like a star. Because of these multiple dimension tables, all connecting to single fact table, this design concept is named dimensional modeling.

Fig 1: Dimensional Modeling Schema, resembles a Star and hence called Star Schema Dimensional Modeling – Fact Table In a Dimensional Model, Fact table contains the measurements or metrics or facts of your business processes. If your business process is Sales, then a measurement of this business process such as “monthly sales number” is captured in the fact table. In addition to the measurements, the only other things a fact table contains are foreign keys for the dimension tables. Dimensional Modeling – Dimension Table In a Dimensional Model, context of the measurements are represented in dimension tables. You can also think of the context of a measurement as the characteristics such as who, what, where, when, how of a measurement (subject ). In your business process Sales, the characteristics of the ‘monthly sales number’ measurement can be a Location ( Where), Time ( When), Product Sold ( What).

The Dimension Attributes are the various columns in a dimension table. In the Location dimension, the attributes can be Location Code, State, Country, Zip code. Generally the Dimension Attributes are used in report labels, and query constraints such as where Country=’USA’.

The dimension attributes also contain one or more hierarchical relationships. Before designing your data warehouse, you need to decide what this data warehouse contains. Say if you want to build a data warehouse containing monthly sales numbers across multiple store locations, across time and across products then your dimensions are: • Location • Time • Product Each dimension table contains data for one dimension.

In the above example you get all your store location information and put that into one single table called Location. Your store location data may be spanning across multiple tables in your OLTP system (unlike OLAP). You need to de-normalize all that data into one single dimension table.

In this multi part tutorial we will learn the basics of dimensional modeling and we will see how to use this modeling technique in real life scenario. At the end of this tutorial you will become a confident dimensional data modeler. Prerequisite No previous knowledge in dimensional modeling (or any other modeling) is a prerequisite for this tutorial. However I assume that you already know what is a data warehouse, you have working knowledge in database and preferably you have seen or worked in a data warehousing project before.

What is dimensional modelling? Ok, so let's get started. Definition Dimensional modeling is one of the methods of data modeling, that help us store the data in such a way that it is relatively easy to retrieve the data from the database. All the modeling techniques give us different ways to store the data.

Different ways of storing data gives us different advantages. For example, ER Modeling gives us the advantage of storing data is such a way that there is less redundancy. Dimensional modeling, on the other hand, give us the advantage of storing data in such a fashion that it is easier to retrieve the information from the data once the data is stored in database. This is the reason why dimensional modeling is used mostly in data warehouses built for reporting.

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. Later on the tutorial we will learn why is it so. • Faster Data Retrieval • Better Understandability • Extensibility Now that we know the reasons behind creating a dimensional modeling, let's find out what exactly is done in this type of models. In dimensional model, everything is divided in 2 distinct categories - dimension or measures. Anything we try to model, must fit in one of these two categories.