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See how to enable scripts. Get started with Microsoft Edge. Select Language:. Choose the download you want. Download Summary:. Total Size: 0. Back Next. Microsoft recommends you install a download manager. Microsoft Download Manager. Manage all your internet downloads with this easy-to-use manager. It features a simple interface with many customizable options:.
Download multiple files at one time Download large files quickly and reliably Suspend active downloads and resume downloads that have failed. Yes, install Microsoft Download Manager recommended No, thanks. Again, denormalization in dimensions occurred in this case for the product subcategory, and the category will be placed into the product dimension with redundant values. However, this decision was made in order to avoid snow flaking and raise the performance of the join between the fact and dimensions.
We are not going to go in detail through the attributes of the store dimension. The most important part of this dimension is that it can have a relationship to the date dimension. For example, a store's opening date will be a key related to the date dimension.
This type of snow flaking is unavoidable because you cannot copy all the date dimension's attributes in every other dimension that relates to it. On the other hand, the date dimension is in use with many other dimensions and facts. So, it would be better to have a conformed date dimension.
Outrigger is a Kimball terminology for dimensions, such as date, which is conformed and might be used for a many-to-one relationship between dimensions for just one layer. In the previous example, you learned about transactional fact. Transactional fact is a fact table that has one record per transaction. This type of fact table usually has the most detailed Grain.
There is also another type of fact, which is the snapshot Fact table. In snapshot fact, each record will be an aggregation of some transactional records for a snapshot period of time. For example, consider financial periods; you can create a snapshot Fact table with one record for each financial period, and the details of the transactions will be aggregated into that record. Transactional facts are a good source for detailed and atomic reports.
They are also good for aggregations and dashboards. The Snapshot Fact tables provide a very fast response for dashboards and aggregated queries, but they don't cover detailed transactional records. Based on your requirement analysis, you can create both kinds of facts or only one of them. There is also another type of Fact table called the accumulating Fact table.
This Fact table is useful for storing processes and activities, such as order management. You can read more about different types of Fact tables in The Data Warehouse Toolkit , Ralph Kimball , Wiley which was referenced earlier in this chapter. We've explained that Fact tables usually contain FKs of dimensions and some measures.
However, there are times when you would require a Fact table without any measure. These types of Fact tables are usually used to show the non-existence of a fact. For example, assume that the sales business process does promotions as well, and you have a promotion dimension.
So, each entry in the Fact table shows that a customer X purchased a product Y at a date Z from a store S when the promotion P was on such as the new year's sales. This Fact table covers every requirement that queries the information about the sales that happened, or in other words, for transactions that happened. However, there are times when the promotion is on but no transaction happens!
This is a valuable analytical report for the decision maker because they would understand the situation and investigate to find out what was wrong with that promotion that doesn't cause sales.
So, this is an example of a requirement that the existing Fact table with the sales amount and other measures doesn't fulfill.
This Fact table doesn't have any fact or measure related to it; it just has FKs for dimensions. However, it is very informative because it tells us on which dates there was a promotion at specific stores on specific products. We call this Fact table as a Factless Fact table or Bridge table. Using examples, we've explored the usual dimensions such as customer and date. When a dimension participates in more than one business process and deals with different data marts such as date , then it will be called a conformed dimension.
Sometimes, a dimension is required to be used in the Fact table more than once. For example, in the FactSales table, you may want to store the order date, shipping date, and transaction date.
All these three columns will point to the date dimension. In this situation, we won't create three separate dimensions; instead, we will reuse the existing DimDate three times as three different names. So, the date dimension literally plays the role of more than one dimension. This is the reason we call such dimensions role-playing dimensions.
There are other types of dimensions with some differences, such as junk dimension and degenerate dimension. The junk dimension will be used for dimensions with very narrow member values records that will be in use for almost one data mart not conformed. For example, the status dimensions can be good candidates for junk dimension. If you create a status dimension for each situation in each data mart, then you will probably have more than ten status dimensions with only less than five records in each.
The junk dimension is a solution to combine such narrow dimensions together and create a bigger dimension. You may or may not use a junk dimension in your data mart because using junk dimensions reduces readability, and not using it will increase the number of narrow dimensions. So, the usage of this is based on the requirement analysis phase and the dimensional modeling of the star schema. A degenerate dimension is another type of dimension, which is not a separate dimension table.
In other words, a degenerate dimension doesn't have a table and it sits directly inside the Fact table. Assume that you want to store the transaction number string value.
Where do you think would be the best place to add that information? You may think that you would create another dimension and enter the transaction number there and assign a surrogate key and use that surrogate key in the Fact table. This is not an ideal solution because that dimension will have exactly the same Grain as your Fact table, and this indicates that the number of records for your sales transaction dimension will be equal to the Fact table, so you will have a very deep dimension table, which is not recommended.
On the other hand, you cannot think about another attribute for that dimension because all attributes related to the sales transaction already exist in other dimensions connected to the fact. So, instead of creating a dimension with the same Grain as the fact and with only one column, we would leave that column even if it is a string inside the Fact table.
This type of dimension will be called a degenerate dimension. Now that you understand dimensions, it is a good time to go into more detail about the most challengeable concepts of data warehousing, which is slowly changing dimension SCD. The dimension's attribute values may change depending on the requirement. You will do different actions to respond to that change. As the changes in the dimension's attribute values happen occasionally, this called the slowly changing dimension.
SCD depends on the action to be taken after the change is split in different types. In this section, we only discuss type 0, 1, and 2. Type 0 doesn't accept any changes. Let's assume that the Employee Number is inside the Employee dimension. Employee Number is the business key and it is an important attribute for ETL because ETL distinguishes new employees or existing employees based on this field.
So we don't accept any changes in this attribute. This means that type 0 of SCD is applied on this attribute. Sometimes, a value may be typed wrongly in the source system, such as the first name, and it is likely that someone will come and fix that with a change. In such cases, we will accept the change, and we won't need to keep historical information the previous name.
So we simply replace the existing value with a new value. This type of SCD is called type 1. The following screenshot shows how type 1 works:. In this type, it is a common requirement to maintain historical changes. For example, consider this situation; a customer recently changes their city from Seattle to Charlotte. You cannot use type 0 because it is likely that someone will change their city of living.
If you behave like type 1 and update the existing record, then you will miss the information of the customer's purchase at the time that they were in Seattle, and all entries will show that they are customers from Charlotte. So the requirement for keeping the historical version resulted in the third type of SCD, which is type 2.
Type 2 is about maintaining historical changes. The way to keep historical changes is through a couple of metadata columns: FromDate and ToDate. Each new customer will be imported into DimCustomer with FromDate as a start date, and the ToDate will be left as null or a big default value such as 29,, If a change happens in the city, the existing records in ToDate will be marked as the date of change, and a new record will be created as an exact copy of the previous record with the new city and with a new FromDate , which will be the date of change, and the ToDate field will be left as null.
Using this solution to find the latest and most up-to-date member information, you just need to look for the member record with ToDate as null. To fetch the historical information, you would need to search for it in the specified time span whether the historical record exists. The following screenshot shows an example of SCD type There are other types of SCD that are based on combinations of the first three types and cover other kinds of requirements.
You can read more about the different types of SCD and methods of implementing them in The Data Warehouse Toolkit referenced earlier in this chapter.
In this chapter, you learned what Business Intelligence is and what its components are. You studied the requirement for BI systems, and you saw the solution architecture to solve the requirements. Then, you read about data warehousing and the terminologies in dimensional modeling. If you come from a DBA or database developer background and are familiar with database normalization, then you will know that in dimensional modeling, you should avoid normalization in some parts and you would need to design a star schema.
You've learned that the Fact table shows numeric and additive values, and descriptive information will be stored in dimensions. You've learned different types of facts such as transactional, snapshot, and accumulating, and also learned about different types of dimensions such as outriggers, role playing, and degenerate. Data warehousing and dimensional modeling together constitute the most important part of the BI system, which is sometimes called the core of the system.
Reza Rad has more than 10 years of experience in databases and soft ware applications. Most of his work experience is in data warehousing and business intelligence. He has a Bachelor's degree in Computer Engineering. He has worked with large enterprises around the world and delivered high-quality data warehousing and BI solutions for them.
He has worked with industries in different sectors, such as Health, Finance, Logistics, Sales, Order Management, Manufacturing, Telecommunication, and so on.
Reza has written books on SQL Server and databases. His blog contains the latest information on his presentations and publications. Reza is a Mentor and a Microsoft Certified Trainer. He has been in the professional training business for many years. He conducts extensive handed-level training for many enterprises around the world via both remote and in-person training. Publication date: May Publisher Packt. Pages ISBN Chapter 1. Data Warehouse Design.
Understanding Business Intelligence. The architecture and components of a BI system. The data warehouse. Extract Transform Load. Data model — BISM. Data visualization. Master Data Management. Data Quality Services. Building the data warehouse. Dimensional modeling. Fact or measure. The Fact table. The star schema. An example of Internet sales. Choosing the business process. To get all of the project templates together in one SSDT, you need the newest version.
Otherwise, you probably need to install multiple previous versions to get all of the templates used in SQL Server. Starting with Visual Studio , the functionality of creating Database Projects has been integrated into the Visual Studio installation. Launch the Visual Studio Installer. In the Windows Start menu, you can search for "installer". If you installed SSDT via a separate install, then you need to upgrade using that method. For more information, see Create a network installation of Visual Studio Download the SSDT standalone installer.
While still online, execute one of the following commands to download all the files required for installing offline. Using the --layout option is the key, it downloads the actual files for the offline installation.
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