The current operating system will be specified under System type. After selecting the desired file, click on the Next button in the bottom-right corner and the download will begin. The downloaded file should be in. Double-click on it to run the installation:. The window for choosing directory to which the extracted files will be saved is shown. After choosing the directory click the Ok button and extraction will begin. After that, the following window will open:.

This is the primary installation screen and the other SQL tools installation can be started here as well. To proceed with the installation, read the license terms and then check the I accept the license terms checkbox. After checking the I accept the license terms , the Next button can be clicked to proceed. The next step is the Global Rules. Which rules are those in some extent depends on which operating system you are installing to but usually include the following: Whether the logged-in user is a system administrator with appropriate privileges a must be Whether there are any reboots pending from other installers Whether required.

If all the rules are passed, the Global Rules step will be processed and skipped automatically. Most of the known issues are elaborated with solutions and workarounds on the Microsoft forum. Such as, for some cases, we might be faced an issue that needs to determine the service pack level of the SQL Server. For this reason, the database administrator may always need to know the version and edition details of the SQL Server with whole details or at least know how to obtain it.

Now, we will go through different methods on how to find version details of the SQL Server. This set of numbers defines the product version of the SQL Server. Now, we will learn how to convert the version details from this meaningless set of numbers:. In the above screenshot, the product version is specified as The first two-digits 15 define the numeric definition of the SQL Server product name.

The following table shows SQL Server versions against the product name so that we can find out the product name rather quickly:. However, we achieved the product name only with the help of the first two-digits but we need more details about the SQL Server instance. The remaining numbers Other navigational aids include collapsing code blocks and incremental search , in addition to normal text search and regex search.

A management tool for code snippets is built in as well. These tools are surfaced as floating windows which can be set to automatically hide when unused or docked to the side of the screen. The Visual Studio code editor also supports code refactoring including parameter reordering, variable and method renaming, interface extraction, and encapsulation of class members inside properties, among others.

Visual Studio includes a debugger that works both as a source-level debugger and as a machine-level debugger. It works with both managed code as well as native code and can be used for debugging applications written in any language supported by Visual Studio. In addition, it can also attach to running processes, monitor, and debug those processes. If source code is not available, it can show the disassembly. The Visual Studio debugger can also create memory dumps as well as load them later for debugging.

The debugger can be configured to be launched when an application running outside the Visual Studio environment crashes. The Visual Studio Debugger allows setting breakpoints which allow execution to be stopped temporarily at a certain position and watches which monitor the values of variables as the execution progresses.

Code can be stepped over , i. When debugging, if the mouse pointer hovers over any variable, its current value is displayed in a tooltip “data tooltips” , where it can also be modified if desired. During coding, the Visual Studio debugger lets certain functions be invoked manually from the Immediate tool window.

The parameters to the method are supplied at the Immediate window. Visual Studio includes a host of visual designers to aid in the development of applications. These tools include:. Visual Studio allows developers to write extensions for Visual Studio to extend its capabilities.

These extensions “plug into” Visual Studio and extend its functionality. Extensions come in the form of macros , add-ins , and packages. Macros represent repeatable tasks and actions that developers can record programmatically for saving, replaying, and distributing.

Macros, however, cannot implement new commands or create tool windows. They are written using Visual Basic and are not compiled. Add-Ins can be used to implement new functionality and can add new tool windows. They can create designers and other tools, as well as integrate other programming languages. However, the managed API isn’t as comprehensive as the unmanaged one. Express Editions do not support hosting extensions. On top of that, other packages can be added to customize the installation.

The Isolated mode of the shell creates a new AppId where the packages are installed. These are to be started with a different executable. It is aimed for development of custom development environments, either for a specific language or a specific scenario. It serves as the central location for posting information about extensions to Visual Studio. Community developers as well as commercial developers can upload information about their extensions to Visual Studio.

NET through Visual Studio Users of the site can rate and review the extensions to help assess the quality of extensions being posted. An extension is stored in a VSIX file. One of the main advantages of these extensions is that they do not require Administrator rights to be installed. RSS feeds to notify users on updates to the site and tagging features are also planned. The Community edition was announced on November 12, , as a new free version, with similar functionality to Visual Studio Professional.

Prior to this date, the only free editions of Visual Studio were the feature-limited Express variants. Unlike the Express variants, Visual Studio Community supports multiple languages, and provides support for extensions.

Individual developers have no restrictions on their use of the Community edition. The following uses also allow unlimited usage: contributing to Open Source projects, academic research, in a classroom learning environment and for developing and testing device drivers for the Windows operating system.

All other use by an organization depends on whether you are classified as an Enterprise more than employees or more than 1 million USD in annual revenue, per Microsoft. As of Visual Studio , the Professional edition is the entry level commercial edition of Visual Studio.

Previously, a more feature restricted Standard edition was available. Windows Mobile development support was included in Visual Studio Standard, however, with Visual Studio , it is only available in Professional and higher editions. Windows Phone 7 development support was added to all editions in Visual Studio Development for Windows Mobile is no longer supported in Visual Studio It is superseded by Windows Phone 7.

In addition to the features provided by the Professional edition, the Enterprise edition provides a new set of software development, database development, collaboration, metrics, architecture, testing and reporting tools. The first version of Visual Studio was Visual Studio Microsoft first released Visual Studio codenamed Boston , [] for the city of the same name , thus beginning the VS codenames related to places [] in , bundling many of its programming tools together for the first time.

Visual Studio 97 was an attempt at using the same development environment for multiple languages. The next version, version 6. The v6 edition of Microsoft was the core environment for the next four releases to provide programmers with an integrated look-alike platform. This led Microsoft to transition the development on the platform independent. NET Framework.

Visual Studio 6. Microsoft released Visual Studio. NET VS. The biggest change was the introduction of a managed code development environment using the. Programs developed using. When a CIL application executes, it is compiled while being executed into the appropriate machine language for the platform it is being executed on, thereby making code portable across several platforms.

Programs compiled into CIL can be executed only on platforms which have an implementation of Common Language Infrastructure. Microsoft introduced C C-sharp , a new programming language, that targets. Visual J programs use Java’s language-syntax. Visual Basic changed drastically to fit the new framework, and the new version was called Visual Basic. NET and Web Services and, with an add-in, portable devices using the. NET Compact Framework. The internal version number of Visual Studio. NET is version 7.

Microsoft released Service Pack 1 for Visual Studio. NET in March In April , Microsoft introduced a minor upgrade to Visual Studio. NET called Visual Studio. NET , codenamed Everett for the city of the same name. NET Framework, version 1. NET or the. As of [update] it is no longer available and the Express Editions have superseded it. The Visual Studio. NET Enterprise Architect edition includes an implementation of Microsoft Visio ‘s modeling technologies, including tools for creating Unified Modeling Language -based visual representations of an application’s architecture, and an object-role modeling ORM and logical database-modeling solution.

Service Pack 1 was released September 13, Visual Studio , codenamed Whidbey a reference to Whidbey Island in Puget Sound region , [ citation needed ] was released online in October and to retail stores a few weeks later. Microsoft removed the “. NET” moniker from Visual Studio as well as every other product with. NET in its name , but it still primarily targets the.

NET Framework, which was upgraded to version 2. Visual Studio ‘s internal version number is 8. Visual Studio was upgraded to support all the new features introduced in. NET Framework 2. NET 2. NET web services. NET applications during development and testing. It also supports all SQL Server databases. Database designers were upgraded to support the ADO. Starting with the edition, Visual Studio also added extensive bit support. VSTA 1. NET object model. Version 2.

NET 3. NET Framework 3. Visual Studio is focused on development of Windows Vista , Office system, and Web applications. J is not included.

Visual Studio requires. NET Framework out of 2. NET Compact Framework the assembly runs on. It also includes a multithreaded build engine MSBuild to compile multiple source files and build the executable file in a project across multiple threads simultaneously. Visual Studio Debugger includes features targeting easier debugging of multi-threaded applications. In debugging mode, in the Threads window, which lists all the threads, hovering over a thread displays the stack trace of that thread in tooltips.

NET Framework 4. Visual Studio comes with. NET Framework 4 and supports developing applications targeting Windows 7.

The new tools allow the visualization of parallel Tasks and their runtime stacks. The Visual Studio code editor now highlights references; whenever a symbol is selected, all other usages of the symbol are highlighted. NET projects. Quick Search supports substring matches and camelCase searches. In this mode, IntelliSense does not auto-complete identifiers; this allows the developer to use undefined identifiers like variable or method names and define those later.

Visual Studio can also help in this by automatically defining them, if it can infer their types from usage. Dynamic help containing links to help items based on what the developer was doing at the time was removed in the final release, [] but can be added back using a download from Microsoft.

Visual Studio Service Pack 1 was released in March Visual Studio Ultimate also includes a historical debugger for managed code called IntelliTrace. Unlike a traditional debugger that records only the currently active stack, IntelliTrace records all events, such as prior function calls, method parameters, events and exceptions. This allows the code execution to be rewound in case a breakpoint was not set where the error occurred.

Microsoft allows configuration of how much data should be recorded, in effect, allowing developers to balance the speed of execution and resource usage. The Lab Management component of Visual Studio Ultimate uses virtualization to create a similar execution environment for testers and developers. The virtual machines are tagged with checkpoints which can later be investigated for issues, as well as to reproduce the issue.

These steps can then be played back to reproduce issues. The final build of Visual Studio was announced on August 1, and the official launch event was held on September 12, Unlike prior versions, Visual Studio cannot record and play macros and the macro editor has been removed.

This update added support for Windows XP targets and also added other new tools and features e. The source code of Visual Studio consists of approximately 50 million lines of code.

During Visual Studio 11 beta, Microsoft eliminated the use of color within tools except in cases where color is used for notification or status change purposes. However, the use of color was returned after feedback demanding more contrast, differentiation, clarity and “energy” in the user interface.

In Visual Studio RC, a major change to the interface is the use of all-caps menu bar, as part of the campaign to keep Visual Studio consistent with the direction of other Microsoft user interfaces, and to provide added structure to the top menu bar area.

The preview for Visual Studio was announced at the Build conference and made available on June 26, The final release of Visual Studio became available for download on October 17, along with. NET 4. Somasegar and hosted on events.

With this update, Visual Studio provides an option to disable the all-caps menus, which was introduced in VS Initially referred to as Visual Studio “15”, it was released on March 7, On November 14, , for a brief period of time, Microsoft released a blog post revealing Visual Studio product name version alongside upcoming features. On March 7, , Visual Studio was released for general availability. On March 14, , first fix was released for Visual Studio due to failures during installation or opening solutions in the first release.

On April 5, , Visual Studio On May 10, , Visual Studio An update to fix the dark color theme was released on May 12, On August 14, , Visual Studio NET Core 2. An update On October 10, , Visual Studio On December 4, , Visual Studio This update contained major performance improvements, new features, as well as bug fixes. On March 6, , Visual Studio It includes updates to unit testing and performance. On May 7, , Visual Studio It included updates across the board including, the installer, editor, debugger among others.

Almost all point releases, the latest of which is With the release of Visual Studio On September 20, , Visual Studio Tools for Xamarin now supports Xcode Visual Studio offers new features like support for EditorConfig a coding style enforcement framework , NGen support,.

On June 6, Microsoft announced Visual Studio version On December 4, Visual Studio Preview 1 was released. It is generally available GA since April 2, and available for download. On April 19, , Microsoft announced Visual Studio version On June 17, Visual Studio Preview 1 was released. On July 14, Visual Studio Preview 2 was released. On August 10, Visual Studio Preview 3 was released. On September 14, Visual Studio Preview 4 was released.

It is generally available GA since November 8, and available for download. On November 13, , Microsoft announced the release of a software as a service offering of Visual Studio on Microsoft Azure platform; at the time, Microsoft called it Visual Studio Online. Subscribers receive a hosted Git -compatible version control system, a load-testing service, a telemetry service and an in-browser code editor codenamed “Monaco”.

The Basic plan is free of charge for up to five users. Users with a Visual Studio subscription can be added to a plan with no additional charge. Visual Studio Lab Management is a software development tool developed by Microsoft for software testers to create and manage virtual environments. Microsoft Visual Studio LightSwitch is an extension and framework specifically tailored for creating line-of-business applications built on existing.

NET technologies and Microsoft platforms. LightSwitch includes graphical designers for designing entities and entity relationships, entity queries, and UI screens.

Business logic may be written in either Visual Basic or Visual C. LightSwitch is included with Visual Studio Professional and higher. The user interface layer is now an optional component when deploying a LightSwitch solution, allowing a service-only deployment.

The first version of Visual Studio LightSwitch, released July 26, , [] had many differences from the current [ when? Notably the tool was purchased and installed as a stand-alone product. If Visual Studio Professional or higher was already installed on the machine, LightSwitch would integrate into that. As of October 14, , Microsoft no longer recommends LightSwitch for new application development. It is built on open-source , [] and on April 14, was released as version 1. NET projects that analyzes the space and time complexity of the program.

NET memory allocation and resource contention. From Wikipedia, the free encyclopedia. Code editor and IDE. Windows 10 and later Windows Server and later [4]. List of languages. This section needs expansion. You can help by adding to it. June See also: Azure DevOps Server. Main article: Visual Studio Code. Retrieved February 19, Retrieved December 29, Archived from the original on September 12, Retrieved August 29, Archived from the original on December 26, Retrieved November 23, Archived from the original on August 5, Retrieved November 7, Archived from the original on April 1, Retrieved July 22, Pythonic Quest.

January 13, Archived from the original on January 18, Retrieved January 17, Microsoft Docs. October 26, Retrieved November 8, Archived from the original on October 19, Retrieved January 1, Archived from the original on January 13, Archived from the original on March 17, Archived from the original on January 12, Archived from the original on February 15,

 
 

A Brief History of MS SQL Server | replace.me.Microsoft SQL Server Business Intelligence Development Beginner’s Guide | Packt

 

One of the best ways of learning a concept and method is to see how it will be applied to a sample question. Assume that you want to build a data warehouse for the sales part of a business that contains a chain of supermarkets; each supermarket sells a list of products to customers, and the transactional data is stored in an operational system. Our mission is to build a data warehouse that is able to analyze the sales information.

Before thinking about the design of the data warehouse, the very first question is what is the goal of designing a data warehouse? What kind of analytical reports would be required as the result of the BI system? The answer to these questions is the first and also the most important step. This step not only clarifies the scope of the work but also provides you with the clue about the Grain.

Defining the goal can also be called requirement analysis. Your job as a data warehouse designer is to analyze required reports, KPIs, and dashboards. After requirement analysis, the dimensional modeling phases will start. Based on Kimball’s best practices, dimensional modeling can be done in the following four steps:.

In our example, there is only one business process, that is, sales. Grain, as we’ve described earlier, is the level of detail that will be stored in the Fact table.

Based on the requirement, Grain is to have one record per sales transaction and date, per customer, per product, and per store. Once Grain is defined, it is easy to identify dimensions. Based on the Grain, the dimensions would be date, store, customer, and product. It is useful to name dimensions with a Dim prefix to identify them easily in the list of tables.

The next step is to identify the Fact table, which would be a single Fact table named FactSales. This table will store the defined Grain. After identifying the Fact and dimension tables, it’s time to go more in detail about each table and think about the attributes of the dimensions, and measures of the Fact table.

Next, we will get into the details of the Fact table and then into each dimension. There is only one Grain for this business process, and this means that one Fact table would be required. To connect to each dimension, there would be a foreign key in the Fact table that points to the primary key of the dimension table.

The table would also contain measures or facts. For the sales business process, facts that can be measured numeric and additive are SalesAmount, DiscountAmount, and QuantitySold. The Fact table would only contain relationships to other dimensions and measures. The following diagram shows some columns of the FactSales :. As you can see, the preceding diagram shows a star schema. We will go through the dimensions in the next step to explore them more in detail.

Fact tables usually don’t have too many columns because the number of measures and related tables won’t be that much. However, Fact tables will contain many records.

The Fact table in our example will store one record per transaction. As the Fact table will contain millions of records, you should think about the design of this table carefully.

The String data types are not recommended in the Fact table because they won’t add any numeric or additive value to the table. The relationship between a Fact table and dimensions could also be based on the surrogate key of the dimension. The best practice is to set a data type of surrogate keys as the integer; this will be cost-effective in terms of the required disk space in the Fact table because the integer data type takes only 4 bytes while the string data type is much more.

Using an integer as a surrogate key also speeds up the join between a fact and a dimension because join and criteria will be based on the integer that operators works with, which is much faster than a string. If you are thinking about adding comments in this made by a sales person to the sales transaction as another column of the Fact table, first think about the analysis that you want to do based on comments.

No one does analysis based on a free text field; if you wish to do an analysis on a free text, you can categorize the text values through the ETL process and build another dimension for that. Then, add the foreign key-primary key relationship between that dimension to the Fact table. The customer’s information, such as the customer name, customer job, customer city, and so on, will be stored in this dimension. You may think that the customer city is, as another dimension, a Geo dimension.

But the important note is that our goal in dimensional modeling is not normalization. So resist against your tendency to normalize tables. For a data warehouse, it would be much better if we store more customer-related attributes in the customer dimension itself rather than designing a snow flake schema. The following diagram shows sample columns of the DimCustomer table:. The DimCustomer dimension may contain many more attributes. The number of attributes in your dimensions is usually high.

Actually, a dimension table with a high number of attributes is the power of your data warehouse because attributes will be your filter criteria in the analysis, and the user can slice and dice data by attributes. So, it is good to think about all possible attributes for that dimension and add them in this step.

As we’ve discussed earlier, you see attributes such as Suburb , City , State , and Country inside the customer dimension. This is not a normalized design, and this design definitely is not a good design for a transactional database because it adds redundancy, and making changes won’t be consistent.

However, for the data warehouse design, not only is redundancy unimportant but it also speeds up analytical queries and prevents snow flaking. The CustomerKey is the surrogate key and primary key for the dimension in the data warehouse. The CustomerKey is an integer field, which is autoincremented. It is important that the surrogate key won’t be encoded or taken as a string key; if there is something coded somewhere, then it should be decoded and stored into the relevant attributes.

The surrogate key should be different from the primary key of the table in the source system. There are multiple reasons for that; for example, sometimes, operational systems recycle their primary keys, which means they reuse a key value for a customer that is no longer in use to a new customer.

CustomerAlternateKey is the primary key of the source system. It is important to keep the primary key of the source system stored in the dimension because it would be necessary to identify changes from the source table and apply them into the dimension. The primary key of the source system will be called the business key or alternate key. The date dimension is one of the dimensions that you will find in most of the business processes. There may be rare situations where you work with a Fact table that doesn’t store date-related information.

This is obvious as you can fetch all other columns out of the full date column with some date functions, but that will add extra time for processing. So, at the time of designing dimensions, don’t think about spaces and add as many attributes as required. The following diagram shows sample columns of the date dimension:.

It would be useful to store holidays, weekdays, and weekends in the date dimension because in sales figures, a holiday or weekend will definitely affect the sales transactions and amounts. So, the user will require an understanding of why the sale is higher on a specific date rather than on other days.

You may also add another attribute for promotions in this example, which states whether that specific date is a promotion date or not. The date dimension will have a record for each date. The table, shown in the following screenshot, shows sample records of the date dimension:. As you can see in the records illustrated in the preceding screenshot, the surrogate of the date dimension DateKey shows a meaningful value.

This is one of the rare exceptions where we can keep the surrogate key of this dimension as an integer type but with the format of YYYYMMDD to represent a meaning as well. In this example, if we store time information, where do you think would be the place for time attributes? Inside the date dimension? Definitely not. The date dimension will store one record per day, so a date dimension will have records per year and records for 10 years. However, 5 million records for a single dimension are too much; dimensions are usually narrow and they occasionally might have more than one million records.

So in this case, the best practice would be to add another dimension as DimTime and add all time-related attributes in that dimension. The following screenshot shows some example records and attributes of DimTime :. Usually, the date and time dimensions are generic and static, so you won’t be required to populate these dimensions through ETL every night; you just load them once and then you could use them.

I’ve written two general-purpose scripts to create and populate date and time dimensions on my blog that you can use. The product dimension will have a ProductKey , which is the surrogate key, and the business key, which will be the primary key of the product in the source system something similar to a product’s unique number. The product dimension will also have information about the product categories.

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.

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Rate as 3 out of 5, 3 stars. Rate as 4 out of 5, 4 stars. Rate as 5 out of 5, 5 stars. Write a review Review must be at least 10 words. Free access for Packt account holders Instant updates on new Packt books Preface What this book covers What you need for this book Who this book is for Conventions Time for action — heading What just happened? Reader feedback Customer support Downloading the example code Downloading color versions of the images for this book Errata Piracy Questions 1.

Time for action — creating the first cube What just happened? Time for action — viewing the cube in the browser What just happened? Dimensions and measures Time for action — using the Dimension Designer What just happened?

Time for action — change the order of the Month attribute What just happened? Time for action — modifying the measure properties What just happened? Time for action — using a Named Query What just happened? Using dimensions Time for action — adding a Fact relationship What just happened? Hierarchies Time for action — creating a hierarchy What just happened?

Time for action — calculated members What just happened? Time for action — processing the data What just happened? Summary 3. Time for action — creating measures What just happened? Creating hierarchies Time for action — creating a hierarchy from a single table What just happened? Time for action — creating a hierarchy from multiple tables What just happened? Data Analysis eXpression, calculated columns, and measures Time for action — using time intelligence functions in DAX What just happened?

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As the BI system is based on data, the better the quality of data, the better the output of the BI solution. Because of this fact, working on data quality is one of the components of the BI systems.

As an example, Auckland might be written as “Auck land” in some Excel files or be typed as “Aukland” by the user in the input form.

As a solution to improve the quality of data, Microsoft provided users with DQS. DQS works based on Knowledge Base domains, which means a Knowledge Base can be created for different domains, and the Knowledge Base will be maintained and improved by a data steward as time passes.

There are also matching policies that can be used to apply standardization on the data. A data warehouse is a database built for analysis and reporting. In other words, a data warehouse is a database in which the only data entry point is through ETL, and its primary purpose is to cover reporting and data analysis requirements.

This definition clarifies that a data warehouse is not like other transactional databases that operational systems write data into. When there is no operational system that works directly with a data warehouse, and when the main purpose of this database is for reporting, then the design of the data warehouse will be different from that of transactional databases. If you recall from the database normalization concepts, the main purpose of normalization is to reduce the redundancy and dependency.

The following table shows customers’ data with their geographical information:. Let’s elaborate on this example. As you can see from the preceding list, the geographical information in the records is redundant.

This redundancy makes it difficult to apply changes. For example, in the structure, if Remuera , for any reason, is no longer part of the Auckland city, then the change should be applied on every record that has Remuera as part of its suburb. The following screenshot shows the tables of geographical information:.

So, a normalized approach is to retrieve the geographical information from the customer table and put it into another table. Then, only a key to that table would be pointed from the customer table. In this way, every time the value Remuera changes, only one record in the geographical region changes and the key number remains unchanged.

So, you can see that normalization is highly efficient in transactional systems. This normalization approach is not that effective on analytical databases. If you consider a sales database with many tables related to each other and normalized at least up to the third normalized form 3NF , then analytical queries on such databases may require more than 10 join conditions, which slows down the query response. In other words, from the point of view of reporting, it would be better to denormalize data and flatten it in order to make it easier to query data as much as possible.

This means the first design in the preceding table might be better for reporting. However, the query and reporting requirements are not that simple, and the business domains in the database are not as small as two or three tables.

So real-world problems can be solved with a special design method for the data warehouse called dimensional modeling. There are two well-known methods for designing the data warehouse: the Kimball and Inmon methodologies. The Inmon and Kimball methods are named after the owners of these methodologies.

Both of these methods are in use nowadays. The main difference between these methods is that Inmon is top-down and Kimball is bottom-up. In this chapter, we will explain the Kimball method. Both of these books are must-read books for BI and DW professionals and are reference books that are recommended to be on the bookshelf of all BI teams. This chapter is referenced from The Data Warehouse Toolkit , so for a detailed discussion, read the referenced book.

To gain an understanding of data warehouse design and dimensional modeling, it’s better to learn about the components and terminologies of a DW.

A DW consists of Fact tables and dimensions. The relationship between a Fact table and dimensions are based on the foreign key and primary key the primary key of the dimension table is addressed in the fact table as the foreign key. Facts are numeric and additive values in the business process. For example, in the sales business, a fact can be a sales amount, discount amount, or quantity of items sold.

All of these measures or facts are numeric values and they are additive. Additive means that you can add values of some records together and it provides a meaning. For example, adding the sales amount for all records is the grand total of sales. Dimension tables are tables that contain descriptive information. Descriptive information, for example, can be a customer’s name, job title, company, and even geographical information of where the customer lives. Each dimension table contains a list of columns, and the columns of the dimension table are called attributes.

Each attribute contains some descriptive information, and attributes that are related to each other will be placed in a dimension. For example, the customer dimension would contain the attributes listed earlier. Each dimension has a primary key, which is called the surrogate key.

The surrogate key is usually an auto increment integer value. The primary key of the source system will be stored in the dimension table as the business key. The Fact table is a table that contains a list of related facts and measures with foreign keys pointing to surrogate keys of the dimension tables.

Fact tables usually store a large number of records, and most of the data warehouse space is filled by them around 80 percent. Grain is one of the most important terminologies used to design a data warehouse. Grain defines a level of detail that stores the Fact table. For example, you could build a data warehouse for sales in which Grain is the most detailed level of transactions in the retail shop, that is, one record per each transaction in the specific date and time for the customer and sales person.

Understanding Grain is important because it defines which dimensions are required. There are two different schemas for creating a relationship between fact and dimensions: the snow flake and star schema. In the start schema, a Fact table will be at the center as a hub, and dimensions will be connected to the fact through a single-level relationship. There won’t be ideally a dimension that relates to the fact through another dimension. The following diagram shows the different schemas:.

The snow flake schema, as you can see in the preceding diagram, contains relationships of some dimensions through intermediate dimensions to the Fact table. If you look more carefully at the snow flake schema, you may find it more similar to the normalized form, and the truth is that a fully snow flaked design of the fact and dimensions will be in the 3NF.

The snow flake schema requires more joins to respond to an analytical query, so it would respond slower. Hence, the star schema is the preferred design for the data warehouse. It is obvious that you cannot build a complete star schema and sometimes you will be required to do a level of snow flaking.

However, the best practice is to always avoid snow flaking as much as possible. After a quick definition of the most common terminologies in dimensional modeling, it’s now time to start designing a small data warehouse. One of the best ways of learning a concept and method is to see how it will be applied to a sample question. Assume that you want to build a data warehouse for the sales part of a business that contains a chain of supermarkets; each supermarket sells a list of products to customers, and the transactional data is stored in an operational system.

Our mission is to build a data warehouse that is able to analyze the sales information. Before thinking about the design of the data warehouse, the very first question is what is the goal of designing a data warehouse? What kind of analytical reports would be required as the result of the BI system? The answer to these questions is the first and also the most important step. This step not only clarifies the scope of the work but also provides you with the clue about the Grain.

Defining the goal can also be called requirement analysis. Your job as a data warehouse designer is to analyze required reports, KPIs, and dashboards. After requirement analysis, the dimensional modeling phases will start. Based on Kimball’s best practices, dimensional modeling can be done in the following four steps:.

In our example, there is only one business process, that is, sales. Grain, as we’ve described earlier, is the level of detail that will be stored in the Fact table. Based on the requirement, Grain is to have one record per sales transaction and date, per customer, per product, and per store. Once Grain is defined, it is easy to identify dimensions. Based on the Grain, the dimensions would be date, store, customer, and product. It is useful to name dimensions with a Dim prefix to identify them easily in the list of tables.

The next step is to identify the Fact table, which would be a single Fact table named FactSales. This table will store the defined Grain. After identifying the Fact and dimension tables, it’s time to go more in detail about each table and think about the attributes of the dimensions, and measures of the Fact table. Next, we will get into the details of the Fact table and then into each dimension.

There is only one Grain for this business process, and this means that one Fact table would be required. To connect to each dimension, there would be a foreign key in the Fact table that points to the primary key of the dimension table. The table would also contain measures or facts. For the sales business process, facts that can be measured numeric and additive are SalesAmount, DiscountAmount, and QuantitySold.

The Fact table would only contain relationships to other dimensions and measures. The following diagram shows some columns of the FactSales :.

 

Microsoft sql server 2014 business intelligence development beginners guide free

 

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All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical servdr or reviews. Every effort has been made in the preparation of this book to ensure intellogence accuracy of the information presented.

However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages сами teach yourself visually microsoft word 2013 free моему or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals.

However, Packt Publishing cannot guarantee the accuracy of this information. Reza Rad has more than 10 years of experience in databases microsoft sql server 2014 business intelligence development beginners guide free software applications. Most of his work experience is in data warehousing intelligennce business intelligence. He has a Bachelor’s degree in Computer Engineering. He has worked with large enterprises around the world and delivered beginers 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 imcrosoft handed-level training for many enterprises around the world via both remote and in-person training. He has worked for more than 10 years with Oracle Corporation and has held various positions, including that of a Practice Manager. He had been co-running the North Business Intelligence and Warehouse Consulting practice, delivering business intelligence solutions to Fortune clients.

During this time, he steadily added business skills and business training to his technical background. InJohn decided to leave Oracle and become a founding member in a small business named iSeerix. This allowed him to focus on strategic partnerships with clients to design and build Business Intelligence and data warehouse solutions. John’s strengths include the ability to communicate the benefits of introducing a Business Intelligence solution to a client’s architecture.

He has gradually become a trusted advisor to his clients. His philosophy is based on responsibility and guise respect. He relies on the unique abilities of individuals to ensure success in different areas and strives to foster a team environment of creativity and achievement.

Through the years, he has worked in numerous industries with differing technologies. Businness broad experience base allows him to bring a unique perspective and understanding when designing and developing a data warehouse. The strong business background, coupled with technical expertise, and his certification in Project Management makes him developmenh valued asset to any data warehouse project. Goh Yong Hwee is a database specialist, systems engineer, beginnerz, and trainer based in Singapore.

Throughout his training, he has consistently maintained a Metrics that Matter score exceeding 8 out of He has also been instrumental in customizing and reviewing his training center’s training for its clients.

When imparting knowledge, his objective has been to make technologies easy and simple for everyone to learn. His no-frills approach to training has gained him recognition over the years from both clients and employers, where his clinching of the Жмите Instructor Award, an accolade conferred by his employer, microsoft sql server 2014 business intelligence development beginners guide free testimonial. Over the years, he has chosen to focus his work and specialization on Microsoft SQL Server and is currently begginners full-time employment with a Fortune company in Singapore, taking up the specialist, consultancy, developer, and management roles.

Raunak T. Jhawar is a graduate in Computer Science from the University of Pune and has more than five years of experience working as cree software professional working microsoft sql server 2014 business intelligence development beginners guide free BI, data visualization, and Hadoop.

Raunak is presently working with Aditi Technologies in Bangalore as a Technical Leader, working with clients and consulting them for their BI and продолжить engagements. He currently leads an ambitious Kicrosoft project for Betgenius Ltd. He started his career as a software developer, and then he was a DBA for 12 years. He has been a permanent microsoft sql server 2014 business intelligence development beginners guide free, consultant, contractor, and источник of his own business.

All these experiences, along with continuous learning, debelopment helped him to develop many successful data warehouse and BI projects. Their love, understanding, and support are what made this book possible.

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Microsoft sql server 2014 business intelligence development beginners guide free

 
 
Type 0 doesn’t accept any changes. About the Author Reza Rad has more than 10 years of experience in databases and soft ware applications. Based on the Grain, the dimensions would be date, store, customer, and product.