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«November 2015»

DirectQuery in Power BI Desktop

In the latest Power BI Desktop a new Preview features was released that now allows you to connect using DirectQuery to either SQL Server or Azure SQL Databases.  DirectQuery is a really neat feature that allows you to point to the live version of the data source rather than importing the data into a data model in Power BI Desktop. 

Normally when you want to get an updated dataset in the Power BI Desktop you would have to manually click the refresh button (this can be automated in the Power BI Service), which would initiate a full reimport of your data.  This refresh could take a variable amount of time depending on how much data your have.  For instance, if you’re refreshing a very large table you may be waiting quite a while to see the newly added data. 

With DirectQuery data imports are not required because you’re always looking at a live version of the data.  Let me show you how it works!

Turning on the DirectQuery Preview

Now, because DirectQuery is still in Preview you must first activate the feature by navigating to File->Options and settings->Options->Preview Features then check DirectQuery for SQL Server and Azure SQL Database


Once you click OK you may be prompted to restart the Power BI Desktop to utilize the feature.

Using DirectQuery in Power BI Desktop

Next make a connection either to an On-Premises SQL Server or Azure SQL database.

Go to the Home ribbon and select Get Data then SQL Server.


Provide your Server and Database names then click OK. ***Do not use a SQL statement.  It is not currently supported with DirectQuery***


From the Navigator pane choose the table(s) you would like to use.  I’m just going to pick the DimProduct table for this example and then click Load.  You could select Edit and that would launch the Query Editor where you could manipulate the extract.  This would allow you to add any business rules needed to the data before visualizing it.


Next you will be prompted to select what you want to connect to the data. Again, Import means the data

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The Big Data Blog Series

Over the last few years I’ve been speaking a lot on the subject of Big Data. I started by giving an intermediate session called “Show Me Whatcha’ Workin’ With”. This session was designed for people who had attended a one hour introductory session that showed you how to load data, to look at possible applications … Continue reading The Big Data Blog Series
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Getting Started with Data Quality Services (DQS) 2012

  • 29 March 2012
  • Author: cprice1979
  • Number of views: 13971

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!! 


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