KNIGHTS MICROSOFT BUSINESS INTELLIGENCE 24 HOUR TRAINER PDF
Knight's Microsoft® Business Intelligence Hour Trainer: Leveraging Microsoft SQL Server® Integration, Analysis, and Reporting Services with Excel® and. Brian Knight, SQL Server MVP, is the founder of Pragmatic Works. Knight's Microsoft Business Intelligence Hour Trainer: Leveraging Excerpt 2: (PDF). Free Download Udemy Knight's Microsoft Business Intelligence Hour Trainer. With the help of this course you can Leveraging Microsoft SQL.
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Knight's Microsoft Business Intelligence Hour Trainer: Leveraging Microsoft SQL Server Integration, Analysis, and Reporting Services with Excel and. and was an author on the books Knight's Hour Trainer: Microsoft SQL Server. Integration Services, Knight's Microsoft Business Intelligence Hour Trainer, and SharePoint .. instructions if you learn better that way. Either way if you. i data Warehousing and Business intelligence . Knight's Microsoft® Business intelligence hour trainer: Leveraging Microsoft sQL .. requirements and hints and begin coding, or you can read the step-by-step instructions if you learn.
The natural key, which is also known as the business or alternate key, is also stored as a column in each dimension. This value is stored in the fact table. You may also be required to include some additional calculations or formulas before you load the fact table. For example, you may be loading line item data and your source data may contain only a Quantity value and an Item Cost value, although your requirements are to store a LineCost column.
This value is the result of multiplying the quantity and the cost. As a result, you use a derived column task to apply the formula and create a LineCost column. You may also encounter a situation in which you need to aggregate data because the data is at a more granular level than required.
You could use an aggregate transform to roll the data up. When configuring the transform, you should set the Item as the Group By value, then set the operation on SalesAmount to Sum and set the operation to Max on Date. In this Try It you will load a fact table called FactSales. The data that you will use is in Lesson9Data. Run Lesson9CreateTable.
Name the project BI Trainer Packages 9. Change the name of the connection to AdventureWorksDW Drag a flat file data source onto the Data Flow design surface. Double-click the source. Click the button labeled New on the Flat File Editor. Drag a Lookup transform onto the Data Flow design surface. Drag the data flow path green arrow from the flat file source onto the Lookup transform. Rename the transform to Customer Lookup. Double-click the Lookup transform.
Click Connection in the left navigation pane. Ensure that the AdventureWorksDW connection is selected. Rename the transform to Currency Lookup. Drag the data flow path green arrow from the Customer Lookup onto the Currency Lookup. Rename the transform SalesTerritory.
Rename the transform Date Lookup. Drag a Derived Column transform onto the design surface. Rename the transform Add Extended Total. Drag the data flow path green arrow from the Date Lookup onto the Derived Column transform.
Double-click the Derived Column transform. Rename it FactSales Destination. Drag the data flow path green arrow from Add Extended Total onto the destination. Double-click the destination source. Select dbo. Each approach ultimately accomplishes the same goal; however, the method of choice will probably depend on the number of packages you plan to deploy.
As with most Microsoft tools, you have numerous ways to accomplish the same task. There are three main deployment choices available to you: The fi rst two methods allow you to deploy only a single package at a time. The third approach, on the other hand, allows you to configure and deploy multiple packages simultaneously. As stated earlier, you can deploy a package to an instance of SQL Server or to a file location.
On this screen you will select the package location, which in this case will be File System. If you are going to connect with your Windows account, choose Windows Authentication. This authentication method will use your Windows domain credentials to deploy the package. You can accept the default or you can change the name of the package.
Finally, you must specify your package protection level. Once you are finished, click OK and your package will be deployed.
First, you can update only a single package at a time. If you have several packages to deploy, this could be a very cumbersome approach. Second, if your packages rely on any configuration files, you will be required to change the values manually either before or after deployment.
As a result, this method may not be optimal for a multi-package deployment that includes configuration files. First, open the SSIS project that contains the package you want to deploy. Once the project is open, you will need to open the package that will be deployed. You will follow the same steps outlined in the previous section to deploy your package. As when deploying a package using SSMS, you are limited to deploying one package at a time and cannot modify your package configurations at the time of deployment.
Keep this in mind when deciding on your package deployment strategy. There are three steps required to build the deployment utility, and one optional step: Create a package configuration file this is the optional step. Run the. When the screen is opened, ensure that either Configuration Properties or Deployment Utility is selected on the menu. No matter the choice, you can set three project properties. The first is AllowConfigurationChanges. If you set this property to true and your package contains configuration files, you will have the ability to change your configurations when you run the wizard.
The second property is CreateDeploymentUtility. You must set the value for this property to true so that the deployment wizard is created when you build the project.
Finally, using the DeploymentOutputPath you can specify where to save the wizard when the project is built. Once you have built the project, navigate to the file location that you specified as the output path. Copy the deployment folder to the target server.
Then you will need to run the. Once the wizard starts you will see the Microsoft wizard welcome screen. You can also decide if you want the deployment wizard to validate your packages when deployment is complete. After selecting your deployment destination, indicate the installation folder if you chose a file deployment, and the target SQL Server if you chose a server deployment.
If you want to use Windows Authentication instead, select Using Windows Authentication and your Windows credentials will be used during the deployment process. Select your deployment path and click OK.
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Once you have selected your authentication method or file location for a file deployment, you must choose the installation folder. On the Select Installation Folder screen you can use the Browse button next to the text box labeled Folder to select the folder to which you want to deploy the SSIS packages and any associated dependencies. After you have selected the installation folder the installer will confirm that the wizard has enough information to install the packages from your project.
At this point the wizard will gather all the information required to deploy the packages.
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The Configure Packages screen allows you to edit the configurations that you specified during the design of the SSIS packages. The deployment wizard then validates the packages and finally provides a summary of the deployment.
The summary includes information such as the packages that will be deployed, the deployment location, the configuration file information, a list of dependencies, and a log file name. At this point you click Finish and the packages are deployed. You will set the c property to true so that it creates the deployment wizard when you build the project.
Step-by-Step 1. Open the SSIS project that contains the packages you want to deploy. You could use the project from the Lesson 9 Try It section. Right-click the project in the Solution Explorer and select Properties or click Project in the toolbar and then select Properties.
Click Deployment Utility from the Configuration Properties. Then set the DeploymentOutputPath to the file location where you want to create the deployment wizard. Specify the location where you want to create the deployment utility in the DeploymentOutputPath. Click Build in the tool bar and select Build Solution. Copy the Deployment folder to the target server. Run the Package Deployment Wizard. Click Next on the Welcome screen. Enter a SQL Server name.
Choose Windows Authentication. At this point the wizard will confirm the installation of the packages. Click Next on the Package Validation screen. Once the wizard has enough information to install the package, you will have the Configure Packages screen expand the Property item and all configurable items will appear.
Most organizations that we ask have a report backlog of 50 or more reports and a report writer who is constantly battling to catch up. SSAS has two major functions: As you may remember, a data warehouse is made up of fact and dimension tables. The fact tables hold measures, which are the numeric columns that you want to put on the report. SSAS adopts many of the same items but has changed their names. For example, measure groups in SSAS are collections of measures within a single fact table.
The fi rst step, once the project is created, is to create a data source. The data source is a connection to your data warehouse. Second, create a data source view to act as a layer between your data source and your data warehouse and to protect you from changes. Third, run the Cube Wizard to create the fi rst cube and dimensions. Finally, modify the cube and dimensions to scale better and be properly formatted. Those users can be slicing and dicing data from one of many clients like Excel, SharePoint, a third-party application, or a custom application.
Those same users can read data out of the cube by using Reporting Services for more standard reporting.
NET language. Once the database is created, you can create any number of cubes, which are containers for one or more measure groups or dimensions, inside the database. The data in the cube is refreshed periodically from the data warehouse through a processing step.
During the processing step a series of queries run against the data warehouse to retrieve all the distinct values of each column in the dimensions and load the measure groups.
It is a collection of measures, which are numeric items you can place in the data area of a report.
In other words, if someone were to drag over the Sales Amount measure onto the report, should the number be summed, averaged, or something else, like merely counted? Each number like this will have its own logic for its aggregation and can also be formatted differently. When you process a dimension, queries against the underlying dimension table in the data warehouse are run to retrieve all the distinct values on an attribute-by-attribute basis. These distinct values are called members.
For example, the Gender attribute might have members like Male and Female. The user is evaluating the Sales Amount by these various attributes. If you feel your users are consistently going to view data a given way in a dimension, you can create a user hierarchy. These hierarchies give the user the means to find the data quickly.
While a user could still drag over Year, Quarter, and Month individually, creating this hierarchy allows the user to drag over a single element that contains all those attributes. It also helps with security and the user experience. This, and every other dimension property, is set in the Dimension Editor. In MDX, you refer to intersection points between two coordinates called tuples. The first two axes are columns and rows. Children, [Measures].
Children, [Product]. Lesson 17 will be detailed enough to make you functional in MDX but not an expert. You can also select Import Analysis Services Project to reverse-engineer a project from an Analysis Services database already in production. Deploying this to the SSAS instance will send the schema to the server and then automatically process the data by default. You can adjust to where the cube is deployed once the project is created by going to Solution Explorer, right-clicking the project, and selecting Properties.
The Cube Editor is where you can modify the measure groups and how those measure groups relate to the dimensions. This database is not installed by default, but by the deployment of a project. The project file will be located in the C: You may need to adjust the location to which you deploy this database by right-clicking your project in the Solution Explorer and selecting Properties.
Open Adventure Works DW Right-click the project name and select Properties. The deployment process should take a few minutes the first time.
There are two reasons the project would not deploy. The first is that you may not have adequate access to the server to create a new SSAS database. To resolve that, have the sysadmin of that instance give you access by right-clicking the instance in Management Studio, selecting Properties, and adding your account to the Security tab. To fi x that, ensure that the service account has access to read data out. Please select Lesson 11 on the DVD to view the video that accompanies this lesson.
Creating this connection to your source data will be the fi rst step you take when designing any Analysis Services project. The Data Source Wizard will open, which walks you through the steps of configuring your source. Figure shows the Connection Manager dialog box. If you do choose the default data provider, you will identify the server on which your data source is stored, as well as the database name. Optionally, you can change the authentication used to connect to the source from Windows to SQL Server Authentication.
Verify that your authentication can access the data source by clicking Test Connection. On the next screen you see after configuring the connection, you will define the impersonation information. This is the information Analysis Services will use to connect to the data source throughout development, most noticeably during processing time. After deciding which option is best for you, click Next; you will then finish by naming the data source.
A DSV holds all the metadata from the available data sources in the project. It is a useful tool for filtering just the tables, views, or queries that are needed for your development. When tables are brought into a DSV, any relationships that have been established on the relational database will carry over and will be visible in the DSV. It is designed to cache the source metadata so that it does not have to hold a continuous connection to the data source.
You will be asked to select a data source. If you did not previously create a data source as we discussed earlier in this lesson, you can do it in the Select a Data Source dialog box. Once you select the data source, click Next and you will be asked to define which tables or views you would like added to the DSV.
Click Next, and then give the data source view a name before clicking Finish. You can design relationships manually in the DSV, if they do not already exist in the relational database, by simply clicking and dragging the foreign key column from the source table to the primary key column in the destination table.
This will open a Create Named Query editor where you can change the table or view into a query. This is commonly used for designing a testing or development cube to bring back a subset of the production data. You can also add a new named query if you right-click the diagram design surface and select New Named Query. To add a named calculation, right-click the table name in the diagram and select New Named Calculation.
Anytime you create a new column with a named calculation, you need to verify that the expression did what you anticipated. To double-check that the expression worked, right-click the table and select Explore Data. You will find the new column if you scroll all the way to the right. Use this Explore Data feature not only to see a preview of your data but also to profile your data.
This is great to use as a data profiling tool for some quick analysis of your data before you begin developing. Keeping this in mind, it is always a best practice to give friendly names to both tables and columns so your users will better understand the data as they try to browse through it in a tool like Excel. Generally speaking, end users have no idea what fact and dimension tables are in a data warehouse, so at the very least, rename your tables to remove any prefi x you may have on the relational database such as in the names DimCustomer or FactTransaction.
To give a table a friendly name, select the table name in the Tables window on the left side of the designer or in the diagram and open the Properties menu F4. In the Properties menu, change the FriendlyName property to anything that will make the most sense to your end users.
Now that you have learned about the different features of the data source and data source view, try designing your own in the next section. Also, once that connection is made you will create a data source view and make design changes to the resulting cube to make it friendlier to the end user.
You will first create a data source to connect to the previously mentioned database, and then design a data source view with FactInternetSales and all the tables it shares a relationship with. Ensure that table names are changed to help end users understand the data. Click Next to bypass the welcome screen and then click New to define a new connection to the source data.
Before you click OK, click Test Connection to make sure you have the permissions to the database with a Windows account.
You will return to the wizard, where you can click Next to continue. Complete the wizard by assigning the data source a name. Click Next to bypass the welcome screen and then select AdventureWorksDWR2 as the relational data source before clicking Next again. With the FactInternetSales table still selected, but now in the new pane, click Add Related Tables to add all tables that have an existing database relationship to FactIntenetSales. The last step in the wizard is to assign the data source view a name.
Rename each table, removing the prefix Fact or Dim, by selecting each table either in the Diagram window or the Table pane and opening the Properties menu F4. Next, create a new named calculation on the table now called just Customer by right-clicking the table name and selecting New Named Calculation. Ensure the named calculation is created correctly by right-clicking the table name Customer and selecting Explore Data. Scroll all the way to the right to see the new column values. Data profiling will help you understand your data a little better.
Close the Explore Customer Table window when you are done. Create a second named calculation on the Promotion table with a column name of MinimumRequired see Figure Use the following expression to evaluate this column before clicking OK: You are off to a great start.
The next step is to use the data source and DSV that you have created in this lesson to develop a cube. Please select Lesson 12 on the DVD to view the video that accompanies this lesson.
You have likely designed a data warehouse and built database relationships, designed an ETL process in SSIS to load that database, and have just begun in SSAS with the data source and data source view.
Now you are ready to create a cube, which is the subject of this lesson. A cube is a set of measures facts and dimensions that are related in a way that helps you analyze your data. Any small problems with the ETL and even bad relationships built in the database will be exposed while you design and process the cube.
As you process the new cube you will soon discover any design flaws from previous steps in errors that appear during processing. Creating the initial cube using the Cube Wizard is actually a very quick process.
Designing the data source, data source view, and initial cube represent about five percent of the project. The other 95 percent is building out what the wizard creates so it can be more useful to your end users. Doing this includes, but is not limited to, correcting formatting, adding dimension hierarchies, and creating MDX calculations.
The Cube Wizard automates the process of creating the cube and can automatically create dimensions that are related for you as well. On the Select Creation Method screen you have three options: It will also determine which columns in a measure group are likely measures.
Selecting this option will have you select a data source view and then create the empty cube shell. If you select this option, the wizard assumes you have not previously created a relational database on the backend and will walk you through designing it based on a template you can select from the Template drop-down box.
You can find these templates in C: You can either check off the tables yourself individually or click the Suggest button, in which case Analysis Services will try to determine which are measure groups based on the relationships. It does not always pick correctly, so, if you hit the Suggest button, make sure to verify that it chose correctly.
You learned in Lesson 11 that, generally, fact tables are measure groups because they store all the measures, so, if you are designing based off a traditional data warehouse, you will select your fact tables here.
You will find listed here only the columns that are not primary or foreign keys in the table. Analysis Services assumes correctly that none of those columns will serve as a measure.
Here you can uncheck any columns that are not truly measures, as well as rename the measure group and measures before you click Next. Notice that the wizard has automatically added one measure on the bottom that does not exist in the physical table. This is a count of how many rows are found in the table, which is automatically added with every measure group.
If there are any tables listed from which you do not wish dimensions to be created, simply uncheck them. However, often you may find the need to create a dimension from a fact table if you desire to slice your data by any column found in the fact table.
A fact table that is used as a dimension is known as a degenerate dimension. Assign the cube a name and click Finish. This will automatically add the cube and any dimensions you assigned in the wizard to the Solution Explorer.
Now that you have learned what each step does in the Cube Wizard, try designing your own in the next section. As you go through the wizard you will categorize which tables from the data source view are measure groups and which are dimensions. Also, once you select the Measure Group table you will identify which columns will be the measures that will be used throughout the cube. In that lesson, you created a data source and data source view that will be the basis for the cube you need to create.
The new cube design will use measures from the InternetSales table, and the tables that are related will serve as dimensions. This will be the main table used to track sales. Leave all the measures checked by default except Revision Number; then click Next, as shown in Figure All the remaining measures that are checked will be used to show users the tracking of individual and rolled-up sales. Figure shows the Select New Dimensions screen.
Leave all tables checked except InternetSales, and then click Next. Each of the selected tables will have a dimension created for it. After that option is selected click Next. The last step in the wizard is to name the cube. Once the cube processing finishes successfully, move to the Browser tab. The Internet Sales number is unformatted, and the promotion key does not tell you very much about what promotion was used. These will be fi xed in the following lessons.
You have created your fi rst cube. Remember that what you have completed in Lessons 12 and 13 represent about five percent of the actual cube development. In the next lessons you will learn how to correct formatting, add dimension hierarchies, create MDX calculations, and perform many other tasks that represent the other 95 percent of the project.
Please select Lesson 13 on the DVD to view the video that accompanies this lesson. All other attributes will need to be added manually. Nothing will fi nd data integrity issues in your dimensions better than an SSAS project. As you deploy your project to the instance, SSAS runs a number of queries to determine the distinct records and the uniqueness of each key.
Any problems with this process or with foreign key violations will cause the cube not to be able to process and require you to go back to SSIS to correct those issues. If you skipped that lesson, you can download Lesson13Complete. You can rename each attribute by simply rightclicking it and choosing Rename. User hierarchies make it much easier to navigate through your hierarchies.
Instead, you can create a simple user hierarchy called Calendar Drill-Down that contains all the attributes already preconfigured. In this figure you can see that in the Hierarchies pane there is one hierarchy that has four levels.
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You can create multiple hierarchies by dragging attributes over into the white space. You can also choose to rename a hierarchy by right-clicking it. Attribute Relationships Attribute relationships allow you to create relationships between your different dimension attributes, much like foreign key relationships in SQL Server. Attribute relationships help in a number of areas outside of what you may expect. They help you in performance-tuning the processing of your cube and help your queries return results more efficiently.
They also help in security. For example, imagine if a manager could see only the Europe customers. Creating attribute relationships implies a relationship between the United States attribute and any states and cities that attribute owns. The last thing they help with is member properties. Then drag the Month attribute onto the Quarter attribute and Quarter onto Year. You may ultimately have different paths to the data. For example, you could get to a day through a week also, which is allowed.
If you make a mistake, delete the arrow and try again. In SSAS, the error messages are about as useful as a screen door on a submarine. Keep in mind that SSAS processes a cube by running select distinct queries against each of the column keys. What makes a unique CalendarQuarter? A unique quarter should be the combination of a quarter and a year. To create a key with multiple columns, go back to the Dimension Structure tab and select the Quarter attribute.
Select the CalendarYear attribute and click the single right-facing arrow button to make a concatenated key. To classify a dimension, click the Date dimension and select Time from the Type dropdown box in the Properties window.
You can then do this for each attribute. You will also want to create the attribute relationships to optimize the dimension. Last, hide the Sales Territory Key attribute to prevent users from dragging it over. Step-by-Step Double-click the Sales Territory. Drag the same attributes into the Hierarchies pane from the Attributes pane to create a hierarchy in the following order: In the Properties pane change the AttributeHierarchyVisible flag to false.
This gives you the option of hiding the level if the member matches the parent. For example, France has no regions and the member would just be the word France over again. Instead of being displayed twice, the name could be skipped the second time.
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Once you deploy the cube, you have successfully edited the cube and can repeat these steps against other dimensions. Please select Lesson 14 on the DVD to view the video that accompanies this lesson. The examples in this lesson start where we left off in Lesson Yellow items are fact tables and blue items are dimensions.
Green tables represent dimensions that are also fact tables. On the bottom left you can fi nd a list of the dimensions and their attributes. Finally, on the top left you can fi nd the measure groups and each of the measures. These appear to the end user as three different dimensions with the same attributes, which they can drag over when querying. Each of these is called a role-playing dimension.
Notice that in the Data Source View on the right there are three foreign keys going into the Date dimension table. Each foreign key creates a new roleplaying dimension. In other words, if you add a hierarchy to the date. Then you can set the FormatString property to , 0. In our case, we want only whole numbers, so we can change the FormatString property to , 0;- , 0.
Percent and currency formats are other common measure formats. Many measure groups have dozens of measures or more inside them. Then you can multi-select the measures and go to the Properties pane. They may spend too much time searching for a given measure while browsing for data.
One usability feature in SSAS is to compartmentalize your measures into folders. To do this, simply select the measure and type in the DisplayFolder property the name of the folder into which you wish to place the measure.
The most important measure property is the AggregateFunction property. This defines how SSAS will roll the data up. The default method for rolling up is to sum the data, but averaging and counting are also common.
To average the data you must have a defined time or date dimension. To see how to specify a type in a dimension, review Lesson 14 on using the Dimension Editor. Another common problem that can be solved easily in SSAS with the AggregationFunction property is that of semiadditive measures.
The default answer from SSAS would be , but that would be incorrect. Inventory examples like this are also known as snapshot fact tables, in which you take a snapshot of inventory, a portfolio, or an account for some period of time. Some companies would choose to say that there was an average inventory of 42 during Q1. Some others would show the first number, 15, for Q1, while still others would show for Q1.
If you wish to show the last number, select LastNonEmpty. This measure counts how many rows you have in a given slice of data and can come in handy later for creating calculations or MDX. For example, it can easily be used to determine how many customer orders you had in a given quarter, since the Order Quantity column contains the number of products in a given sale.
This tab shows you the relationships between your measure groups and your dimensions. After you create a calculation here, the formula will be stored on the server and available to all applications and clients. You can also pre-filter your data with named sets. If the Profit Margin KPI falls below a percentage, like 10 percent, an indicator like a stoplight will turn red. The advantage in putting them in SSAS on the server side is that no matter what client the user uses to view the data, he will see the same metric as everyone else.
Actions can make it much easier to QA your data or make the data your users are seeing actionable. The Partitions tab allows you to compartmentalize your data into separate files. As your data grows past 20 million rows in a given measure group, it should be split into multiple partitions to improve your query performance and scalability.
The Aggregations tab gives you control over how the data will be pre-aggregated. Pre-aggregating the data speeds up your queries and improves your end-user experience. This allows you to create pareddown views of your dimensions, attributes, or any other measures for different roles in your company.
Users will see perspectives in most cube clients, or they can be set in the connection string. In this tab you can translate your measures and measure group names to nearly any language.
If you are just starting to get a handle on Microsoft BI tools, this course provides you with the just the right amount of information to perform basic business analysis and reporting. With this crucial resource, you will explore how this newest release serves as a powerful tool for performing extraction, transformation, and load operations ETL.
A team of SQL Server experts deciphers this complex topic and provides detailed coverage of the new features of the product release. The authors present you with a new set of SISS best practices, based on years of real-world experience and it includes case studies and tutorial examples to illustrate advanced concepts and techniques. Please note that we have removed the version number from the database names. If you already have a versioned database like the AdventureWorksR2 database though, this will work for the purposes of the demo.
Threats Fundamentals. Learn Microsoft Flow. Please select Lesson 2 on the DVD to view the video that accompanies this lesson. Numeric data is more common in financial or inventory situations. You need fact tables because they allow you to link the denormalized versions of the dimension tables and provide a largely, if not completely, numeric table for Analysis Services to consume and aggregate.
In other words, the fact table is the part of the model that holds the dollars or count type of data that you would want to see rolled up by year, grouped by category, or so forth. Since many OLAP tools, like Analysis Services, look for a star schema model and are optimized to work with it, the fact table is a critical piece of the puzzle.
The process of designing your fact table will take several steps: 1. Decide on the data you want to analyze. Will this be sales data, inventory data, or fi nancial data? Each type comes with its own design specifics.
This means that you need to decide on the lowest level of analysis you want to perform. For example, each row may represent a line item on a receipt, a total amount for a receipt, or the status of a particular product in inventory for a particular day.
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Decide how you will load the fact table more on this in Lesson 9. Transactions are loaded at intervals to show what is in the OLTP system. An inventory or snapshot fact table will load all the rows of inventory or snapshot-style data for the day, always allowing the user to see the current status and information based on the date the information was loaded.
Fact tables are often designed to be index light, meaning that indexes should be placed only to support reporting and cube processing that is happening directly on that table. It is a good idea to remember that your fact tables will often be much larger in row count and data volume than your dimensions. This means you can apply several strategies to manage the tables and improve your performance and scalability.
Implementing a sliding-window partitioning scheme, where you roll off old partitions and roll on new ones periodically, can drive IO and query times down and access speeds up since you will be accessing only the specific areas of data on the disk that are needed for your query.
Processing speeds for data loading and cube processing will also be faster since the new versions of SQL Server and R2 allow for increased parallelization of queries across partitions. The details of these are out of the scope of this lesson, but you should investigate using these in your data warehouse, because of the significant performance and disk space improvements you will see.
Compression will deliver a decreased amount of IO for queries and a lower total amount of IO will be needed to satisfy them, resulting in faster, more nimble tables, even though some fact tables will have millions of rows. For this minimal overhead, however, Analysis Services will optimize its processing based on the fact that it can assume these keys are keeping the data valid and disable some of the internal checks built into the processing algorithm.
This will allow Analysis Services to consume the data from your fact and dimension tables much faster than if it did not have those keys. To complete the lesson you need to build the table, and include any valuable cost, sales, and count fact columns, along with the key columns for the important dimensions.
Next are a couple of hints to get you started. Make sure you identify the dimensions that are important to your analysis and then include references to those surrogate key columns in your fact table. Check out the example in Figure Yours should look similar. It consists of a myriad of diverse components, each of which can perform a multitude of operations. SSIS is a tool that can be used to construct high-performance workflow processes, which include, but are not limited to, the extraction, transformation, and loading of a data warehouse ETL.
In this section we demystify some of the key components of SSIS. We focus primarily on those that are essential in the development of most SSIS packages. If you want to improve the way the application starts you can add a startup parameter to the application shortcut.
Close and reopen BIDS. You should notice that the splash screen that appeared when you initially opened BIDS no longer appears. Select Integration Services Project from the Template pane. Before proceeding we should provide a brief explanation of solutions and projects.
A solution is a container of projects. One solution can include several Integration Services projects, but a solution can also contain other types of projects.When organizations cannot agree or get together on their data strategy, you need to bring them together for the good of the organization.
What Will You Learn? These capabilities allow everyone to speak the same language when it comes to company metrics and to the way the data should be measured across the enterprise or department. Then you can use that variable as the value for your table or query.
To use these paths, simply drag one from an item onto the next item you want to execute. Either way, if you get stuck or want to see how one of us do the solution, watch the video on the DVD to receive further instruction. Please note that we have removed the version number from the database names. There will be more on how to do this shortly.
You should set the DiscountPct column as a Type 2 dimension column. The five preceding steps are typical of most data flows for building a data warehouse ETL process.
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