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«October 2015»

Data Warehouse from the Ground Up at SQL Saturday Orlando, FL on Oct. 10th

SQL Saturday #442SQL Saturday #442 is upon us and yours truly will be presenting in Orlando, Florida on October 10th alongside Mitchell Pearson (b|t). The session is scheduled at 10:35 AM and will last until 11:35 AM. I’m very excited to be presenting at SQL Saturday Orlando this year as it’ll be my first presenting this session in person and my first time speaking at SQL Saturday Orlando! If you haven’t registered yet for this event, you need to do that. This event will be top notch!

My session is called Designing a Data Warehouse from the Ground Up. What if you could approach any business process in your organization and quickly design an effective and optimal dimensional model using a standardized step-by-step method? In this session I’ll discuss the steps required to design a unified dimensional model that is optimized for reporting and follows widely accepted best practices. We’ll also discuss how the design of our dimensional model affects a SQL Server Analysis Services solution and how the choices we make during the data warehouse design phase can make or break our SSAS cubes. You may remember that I did this session a while back for Pragmatic Works via webinar. I’ll be doing the same session at SQL Saturday Orlando but on-prem! ;)

So get signed up for this event now! It’s only 11 days away!

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Create Date Dimension with Fiscal and Time

Here are three scripts that create and Date and Time Dimension and can add the fiscal columns too. First run the Dim Date script first to create the DimDate table. Make sure you change the start date and end date on the script to your preference. Then run the add Fiscal Dates scripts to add the fiscal columns. Make sure you alter the Fiscal script to set the date offset amount. The comments in the script will help you with this.

This zip file contains three SQL scripts.

Create Dim Date

Create Dim Time

Add Fiscal Dates

These will create a Date Dimension table and allow you to run the add fiscal script to add the fiscal columns if you desire. The Create Dim Time will create a time dimension with every second of the day for those that need actual time analysis of your data.

Make sure you set the start date and end date in the create dim date script. Set the dateoffset in the fiscal script.

Download the script here:


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Getting Started with Data Quality Services (DQS) 2012

  • 29 March 2012
  • Author: cprice1979
  • Number of views: 13695

Data Quality Services is a new and powerful feature that is available in SQL Server 2012. Called a knowledge-driven data quality product, DQS allows you to build knowledge bases that handle the traditional data quality tasks such as profiling, correction, enrichment, standardization and de-duplication.  In this blog series we will dive into DQS and explore how its numerous features and capabilities can improve and enrich your critical and valuable business data. 


DQS Blog Series Index

Part 1: Getting Started with Data Quality Services (DQS) 2012

Part 2: Building Out a Knowledge Base

Part 3: Knowledge Discovery in DQS

Part 4: Data Cleansing in DQS

Part 5 : Building a Matching Policy in DQS

Part 6: Matching Projects in DQS

Part 7: Activity Monitoring, Configuration & Security in DQS


Installing the DQS Server and Data Quality Client

Data Quality Services consist of two components: DQS Server and the Data Quality Client. Both of these components are install by the Data Quality Server Installer. 

To install the DQS server and client, select 'Data Quality Server Installer' shortcut from the the Microsoft SQL Server 2012 RC0/Data Quality Services folder on the All Programs menu of the Start button. 


Once the installer starts, you will be prompted to enter a password for the database master key. This key will be used to encrypt the contents of the DQS databases. 


 The process to install the DQS components may take several minutes. During this process three databases and an out of the box knowledge base is created. The three databases that are created are: 

  • DQS_MAIN - This database contains all the DQS stored procedures, the DQS engine and published knowledge bases
  • DQS_PROJECTS - Contains the data associated with data quality projects created in the Data Quality Client
  • DQS_STAGING_DATA - As the name implies, this is a staging area where you can both copy data to perform DQS operations on it as well as export processed data from. 


 The installation process also creates several DQS server logins  (##MS_dqs_db_owner_login##  and ##MS_dqs_service_login##) and DQS database roles (dqs_administrator, dqs_kb_editor and dqs_kb_operator). To handle DQS initialization, a stored procedure is created in master database. It should also be noted that is the installer can find a Master Data Services database instance on the same service it create a user and map it to the MDS login and then grant administrator access to the DQS_MAIN database. 


Once the installer finishes, you will be prompted to press any key to exit and the installation is complete. 

Miscellaneous Notes:

  • The Microsoft.Net Framework 4.0 is required to run the Data Quality Client.
  • To login to the Data Quality Client a user must be in one of the DQS roles. If a user is in the sysadmin server role, it is not necessary to add them to the DQS roles.
  • If you are running the client on a separate computer, TCP/IP must be enabled on the instance hosting the DQS server.

First Look at the Data Quality Client

To begin working with DQS open the Data Quality Client from All Programs menu on the Start button. When the application launches you will be prompted to enter the server name of instance which host your DQS server. Before you click 'Connect', note that you have an option to 'Encrypt connection' which will use a SSL connection for the communications between the client and server. 


Once you are connected you will notice, three distinct areas: Knowledge Base Management, Data Quality Projects and Administration. We will dive deeper into these areas in the next blog post but I just want to cover some of the high level concepts that are important in the DQS world. 


  • Knowledge Base - collection of data domains
  • Domain - contain domain values and status, domain rules, term-based relations, and reference data. Domains can either be single or composite
  • Knowledge Discovery - analyzes organizational data to build knowledge that can be used in cleansing, matching and profiling
  • Cleansing - Process of using the a knowledge base to propose data corrections
  • Matching Policy - Rules used to perform data de-duplication. These rules can be fine tuned by matching results and profiling that creates additional matching policies.
  • Reference Data - Data that can be used to validate and enrich your data. Reference data providers are available in the Azure Marketplace Data Market or you have the option of connecting directly to your provider.

In the next blog post we put DQS to use by building out a knowledge base with domains and business rules, run through the data discovery process and then build out a matching policy. 

Till next time!! 


Categories: Analysis Services
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