1. Introducing SQL Server 2012 Improvements

2. MODULE OUTLINE
Introduction
New Improvements and Features

3. INTRODUCTION

4. INTRODUCTION
SQL SERVERS NEW STORAGE MODEL

6. BUSINESS intelligence highlights

7. The Vision
IMPROVED PRODUCTIVITY FOR DEVELOPERS (CONTINUED)

8. Introducing SQL Server 2012 Integration Services Improvements
SQL11UPD05-DECK-01

9. Complete, Current, Consistent and Clean Data
INTRODUCTION
SQL SERVER ENTERPRISE INFORMATION MANAGEMENT
Integration Services
Master Data Services
Data Quality Services
Primarily designed to implement ETL processes
Provides a robust, flexible, fast, scalable and extensible architecture
Master data management
Manages reliable, centralized data
Broadens its reach with a new Excel Add-in that can leverage Data Quality Services
Knowledge-driven data cleansing
Corrects, de-duplicates and standardizes data
Integrates with Integration Services
Complete, Current, Consistent and Clean Data

10. New Improvements and Features
IMPROVED USABILITY FOR NEW DEVELOPERS
“Getting Started” window with links to samples and videos
New SSIS Toolbox:
Exposes a description and links to samples for a selected component
Allows categorizing, including Favorites
Data flow connection assistants to facilitate the configuration of sources and destinations

11. New Improvements and Features
IMPROVED PRODUCTIVITY FOR DEVELOPERS
Undo/Redo
Data flow Script component debugging
SQL Server Data Tools (SSDT) designer
Updated look and feel
New VSTA scripting environment with support for .NET 4
Auto-save and recovery
Improved performance when opening packages
Project connection managers
Icon marker to indicate when expressions are used

12. New Improvements and Features
IMPROVED DEPLOYMENT, CONFIGURATION AND MANAGEMENT
SSIS catalog:
User database (SSISDB) hosted on a SQL relational instance with a collection of views and stored procedures that provide a T-SQL API
New project model for bundling project resources together to simplify deployment (.ispac)
New parameter model to simplify configuration management
Similar to parameters in programming functions
Read-only variable in a special namespace

13. New Improvements and Features
IMPROVED DEPLOYMENT, CONFIGURATION AND MANAGEMENT (CONTINUED)
Available with the SSIS catalog:
Connection manager properties are automatically exposed on the server
Environments can be created to run packages with different settings
Automatic capture of package execution logs
Data tap functionality, to dynamically capture data as it flows through the data flow, and without modifying the package
Server can be managed using PowerShell

14. New Improvements and Features
IMPROVED DEPLOYMENT, CONFIGURATION AND MANAGEMENT (CONTINUED)
Available with the SSIS catalog (continued):
Built-in reports:
Integration Services Dashboard – view all packages that have run on the server in the past 24 hours
Performance Reports – view a package’s performance over time, at package and component level
Error Message Report – details failed package executions and related error messages
Support for custom reports

15. DEVELOPER OPPORTUNITIES
Manage the SSIS catalog by using the public T-SQL API, or the .NET interface:
Develop management tools and solutions
Develop custom monitoring and dashboard reports
Embed functionality into solutions (e.g. package execution)
Automate project deployment as part of an installer package
Develop dynamic packages or SSIS extensions by using the SSIS object model:
Custom tasks
Custom data flow components (Transformations, Sources, Destinations)
Data source connectors
ForEach enumerators

16. Introducing SQL Server 2012 Master Data Services Improvements
SQL11UPD05-DECK-02

17. Complete, Current, Consistent and Clean Data
INTRODUCTION
SQL SERVER ENTERPRISE INFORMATION MANAGEMENT
Integration Services
Master Data Services
Data Quality Services
Primarily designed to implement ETL processes
Provides a robust, flexible, fast, scalable and extensible architecture
Master data management
Manages reliable, centralized data
Broadens its reach with a new Excel Add-in that can leverage Data Quality Services
Knowledge-driven data cleansing
Corrects, de-duplicates and standardizes data
Integrates with Integration Services
Complete, Current, Consistent and Clean Data

18. NEW IMPROVEMENTS AND FEATURES
IMPROVED MASTER DATA MANAGER

19. NEW IMPROVEMENTS AND FEATURES
IMPROVED MASTER DATA MANAGER (CONTINUED)

20. NEW IMPROVEMENTS AND FEATURES
NEW MICROSOFT EXCEL ADD-IN (CONTINUED)

21. DEVELOPER OPPORTUNITIES
For the Systems Integrator:
Leverage MDS to connect disparate information systems that share common master data
For the ISV:
By integrating with MDS, customers who deploy your line-of-business solution can incorporate it into their own enterprise-wide master data management solution
This ensures that your solution is not an island and stays consistent and compliant with master data that is shared with other applications running in the customers environment
Anything that MDS can do can be embedded into, or automated by, your solutions by using the WCF API. For example:
Create, read, update, and delete metadata
Create, read, update, and delete entity members

22. Introducing SQL Server 2012 Data Quality Services
SQL11UPD05-DECK-03

23. Complete, Current, Consistent and Clean Data
INTRODUCTION
SQL SERVER ENTERPRISE INFORMATION MANAGEMENT
Integration Services
Master Data Services
Data Quality Services
Primarily designed to implement ETL processes
Provides a robust, flexible, fast, scalable and extensible architecture
Master data management
Manages reliable, centralized data
Broadens its reach with a new Excel Add-in that can leverage Data Quality Services
Knowledge-driven data cleansing
Corrects, de-duplicates and standardizes data
Integrates with Integration Services
Complete, Current, Consistent and Clean Data

24. INTRODUCTION Knowledge Management and Reference Data
BUSINESS SCENARIOS
Knowledge Management and Reference Data
Create and manage DQS Knowledge Bases
Discover knowledge from the organization‘s data
Explore and integrate with 3rd party reference data
Cleansing and Matching
Correct, de-duplicate and standardize data
Administration
Monitor and control data quality processes

25. Discover / Explore Data / Connect
INTRODUCTION
DQS PROCESS
Cloud Services
Knowledge
Management
Build
Reference
Data
Discover / Explore Data / Connect
Enterprise
Data
Knowledge
Manage
Integrated
Profiling
Notifications
Progress
Status
Use
Match & De-duplicate
Correct
& Standardize
Data Quality Projects

26. DQS COMPONENTS
DATA QUALITY CLIENT

27. Knowledge BaseS Rationale: To cleanse data you need knowledge about it
The Knowledge Base is a data repository of knowledge that enables professionals to understand their data and maintain its integrity
Knowledge in a Knowledge Base is maintained in Domains, each of which is specific to a data field

28. KNOWLEDGE BASES Domains capture the semantics of the data
(CONTINUED)
Domains capture the semantics of the data
Domains can use online reference data
Online DataMarket Reference Data Service
Direct Online 3rd Party Reference Data Services
Processes include:
Domain Management – to define domains
Knowledge Discovery – to learn domain values
Matching Policy – to identify potential duplicates and non-matches

29. Represent the data type
KNOWLEDGE BASES
(CONTINUED)
Composite Domains
Domains
Represent the data type
Values
Rules & Relations
Reference Data
Knowledge Base
Matching Policy
Domains

30. DATA QUALITY PROJECT
A Data Quality Project is a means of using a Knowledge Base to improve the quality of source data by performing data cleansing and data matching activities
Created and managed in the Data Quality Client
Results can be exported to a SQL Server table or CSV file
Two types:
Cleansing Activity – processed data is categorized as new, invalid, corrected, and correct
Matching Activity – used to prevent data duplication by identifying exact and approximate matches

31. DQS CLEANSING TRANSFORM
Implements data cleansing in an SSIS 2012 data flow
Configuration involves:
Defining a connection to a Data Quality Server
Selecting a knowledge base
Mapping input columns to domains
Selecting advanced statistical columns
The output includes the original data and corrected data, together with status

32. Introducing SQL Server 2012 Analysis Services Improvements
SQL11UPD06-DECK-01

33. Tabular model development is explored in SQL11UPD06-DECK-02
introduction
SQL Server 2012 Analysis Services (SSAS) is the fifth release of the product
New features and enhancements in this release are based on:
A vision to expand the reach to a broader user base
Embracing the tabular data model
Bringing together tabular and multidimensional models under a single unified platform – the BI Semantic Model
Note: There are no improvements to the data mining component
Tabular model development is explored in SQL11UPD06-DECK-02
This presentation introduces what is new for Analysis Services in SQL Server Take care not to cover content, specifically about tabular model development and DAX, that will be delivered in the next presentation.

34. BI SEMANTIC MODEL One Model for All End User Experiences Team BI
Client Tools Analytics, Reports, Scorecards,
Dashboards, Custom Apps
Data Sources
Databases, LOB Applications, OData Feeds,
Spreadsheets, Text Files
BI Semantic Model
Data Model
Business Logic
and Queries
Data Access
Team BI
PowerPivot for SharePoint
Personal BI
PowerPivot for Excel
Corporate BI
Analysis Services
One Model for All End User Experiences
Use this slide to introduce the BI Semantic Model as one model for all end user experiences. Explain briefly at this stage that it works for all existing Microsoft OLAP clients and tools, but do not go into the details at this stage. It is sufficient to introduce the BI Semantic Model and describe the three layers of Data Model, Business Logic and Queries and Data Access. Respectively, these are the model as exposed to the end user, the logic in the form of calculations, and data access to different types of data stores either cached or pass through.
The BI Semantic Model supports Personal BI with PowerPivot for Excel, Team BI with PowerPivot for SharePoint and Corporate BI with Analysis Services.

35. Third-Party
Applications
Reporting
Services
Excel
PowerPivot
SharePoint
Insights
Power View
DAX Query
MDX Query
BI Semantic Model
Project Type
PowerPivot Workbook
Tabular Project
Multidimensional Project
Excel 2010
DAX
In-Memory N/A
SharePoint Library / Analysis Services PowerPivot
Tabular
SQL Server Data Tools
DAX
In-Memory DirectQuery
Analysis Services Tabular
Tabular
SQL Server Data Tools
MDX
MOLAP ROLAP
Analysis Services Multidimensional
Multidimensional
Design Type
Development Tool
Business Logic
Data Access – Cache Passthrough
Deployment
A PowerPivot workbook can be restored to a Tabular instance, or imported to create a Tabular Project
LOB Applications
Files
OData Feeds
Cloud Services
Relational Databases
Deployed BI Semantic Model
This comprehensive animated slide has been designed to build up an understanding of the three project types that can be used to develop a BI Semantic Model.
[2] Introduce the three project types: PowerPivot Workbook (existing and enhanced), Tabular Project (new) and Multidimensional Project (renamed from the Unified Dimensional Model).
[3] Each project type is described in terms of Design Type, Development Tool, Business Logic, Data Access and Deployment.
[4] PowerPivot Workbook: Targeted at business analysts, these models are created using PowerPivot for Excel and business logic is implemented with DAX. There is only support for cached data with the In-Memory storage mode. Deployment is optional, and is achieved with SharePoint Server 2010 with the PowerPivot Add-in for SharePoint.
[5] Tabular Project: Targeted at IT Professionals, the development tool is SQL Server Data Tools. These models can be configured with DirectQuery which is a pass through mode to the underlying data source. Models (projects) must be deployed to an Tabular mode instance of Analysis Services.
[6] PowerPivot Workbooks can be restored to a Tabular mode instance of Analysis Services or imported into a Tabular Project.
[7] Multidimensional Project: Also targeted at IT Professionals, this model type is what was previously known as the Unified Dimensional Model (UDM). The only major changes in this release are the SQL Server Data Tools designers and the change of name.
[8] All existing OLAP clients, third party applications and the new report authoring tool named Power View can be used to query any type of BI Semantic Model.
[9] Existing clients and third party applications continue to use MDX.
[10] Power View uses a new query language named DAX Query. This new query language is supported only by tabular models in this release.
[11] Data can be integrated into models from a variety of data sources.
[12] The Multidimensional Project only supports producing models based on the relational databases.
[13] The tabular models support relational databases, files, OData feeds, and other deployed BI Semantic Models.

36. Flexibility Richness Scalability
BI SEMANTIC MODEL
DELIVERABLES
Flexibility
Richness
Scalability
Tabular and multidimensional modeling experiences
DAX and MDX for business logic and queries
Cached and passthrough storage modes
Choice of end-user BI tools
Rich data modeling capabilities
Sophisticated business logic using DAX and MDX
Fine-grained security – row and cell level
Enterprise capabilities – multi-language and perspectives
In-Memory for high performance, MOLAP for mission critical scale
DirectQuery and ROLAP for passthrough access to data sources
State-of-the-art compression algorithms
Scales to the largest of enterprise servers
Use this slide to describe the BI Semantic Model deliverables in terms of flexibility, richness and scalability. This slide should be used to reinforce the material presented in the previous slide.

37. CHOOSING THE RIGHT DEVELOPMENT APPROACH
The model developer needs to choose the right development approach
Use this slide to describe the project templates now available to the developer in SQL Server Data Tools.

38. CHOOSING THE RIGHT DEVELOPMENT APPROACH
DATA MODEL
Tabular
Multidimensional
Familiar model, easier to build, faster time to solution
Some advanced concepts are not available natively in the model and may need calculations to simulate these
Easy to wrap a model over a raw database or data warehouse for analytics and reporting
Sophisticated model involving a higher learning curve
Advanced concepts are baked in to the model and optimized (parent-child, many-to-many, attribute relationships, key vs. name, etc.)
Ideally suited for OLAP type applications (e.g. planning, budgeting, forecasting) that need the power of the multidimensional model
Data model considerations are detailed on this slide for both types of model development.

39. CHOOSING THE RIGHT DEVELOPMENT APPROACH
BUSINESS LOGIC
DAX
MDX
Based on Excel formulas and relational concepts – easy to get started
Complex solutions require steeper learning curve – row/filter context, CALCULATE, etc.
Calculated columns enable new scenarios, however no named sets or calculated members (other than measures)
Based on an understanding of multidimensional concepts – involves a higher initial learning curve
Complex solutions require steeper learning curve
Ideally suited for applications that need the power of multidimensional calculations involving scopes, assignments, and calculated members
Business logic considerations are detailed on this slide for both types of model development.

40. CHOOSING THE RIGHT DEVELOPMENT APPROACH
DATA ACCESS AND STORAGE
In-Memory
In-memory column store with typical 10x compression
Brute force memory scans is high performance by default, and no tuning is required
Basic paging support and data volume mostly limited to physical memory
MOLAP
Disk based store with typical 3x compression
Disk scans with in-memory subcube caching, and aggregation tuning required
Extensive paging support and data volumes can scale to multiple terabytes
DirectQuery
Passes through DAX queries and calculations to fully exploit backend database capabilities
No support for MDX queries and no support for data sources other than SQL Server
ROLAP
Passes through fact table requests and is not recommended for large dimension tables
Supports most relational data sources though no support for aggregations except with SQL Server indexed views
Data access and storage considerations are detailed on this slide for both types of model development.

41. Exploring SQL Server 2012 Power View
SQL11UPD06-DECK-04

42. INTRODUCTION
Power View is an interactive data exploration, visualization, and presentation experience
Highly visual design experience
Rich meta-driven interactivity
Presentation-ready at all times
Provides intuitive ad-hoc reporting for business users such as data analysts, business decision makers, and information workers
Ordinarily, a Power View report needs to be based on a tabular BI Semantic Model that has been optimized for the report authoring tool
Use this slide to describe in broad terms what Power View has been designed to achieve, and that it must be based on a tabular BI Semantic Model (probably optimized for Power View).

43. POWER VIEW REPORTING AUDIENCE AND AUTHORING TOOLS
To make it very clear where this new report authoring tools fits, spend good time ensuring the audience appreciate that it targets end users for data exploration and visualization. Importantly it has not been designed to compete with the Report Designer or Report Builder authoring tools.

44. POWER VIEW EXAMPLE REPORT
This slide provides a preview of the visually impressive layout of a Power View report. It is in fact the report created in the hands-on lab. The amazing part about this report is the ease and speed at which it can be produced, and the supported interactivity.

45. Tabular BI Semantic Model Optimization
Ordinarily, the tabular BI Semantic Model needs to be optimized for the Power View experience
This is required to exploit the unique capabilities of the report authoring tool by supplying hints and directives
Note: Optimizing a model for Power View may de-optimize it for OLAP clients
As described in this slide, the tabular BI Semantic Model will need to be optimized for use by Power View.

46. Tabular BI Semantic Model Optimization
(CONTINUED)
The following model resources are not available in the Power View Field List:
Hidden tables, columns and measures
Hierarchies
Implicit measures (defined in the PowerPivot Field List)
Key Performance Indicators (KPIs)
Only the default perspective can be used
This slide details what will not be shown in the Power View Field List. Describe that an implicit measure is one created in the PowerPivot Field List in Excel. Emphasize that it is not a recommended way to create measures – use the explicit approach instead. This will be covered in more detail later in this presentation.

47. Tabular BI Semantic Model Optimization
(CONTINUED)
The model can be optimized by:
Providing friendly names for tables, columns and measures
Hiding unnecessary tables, columns and measures
Setting appropriate formats for columns and measures
Providing descriptions for tables, columns and measures
These are surfaced as tooltips in the Field List
Adding columns that contain images (binary data)
Images can also be referenced by their URL
There may not be the need to define measures
This slide provides details about how a model can be optimized. The hands-on lab will provide deeper discussion on these optimizations and reporting properties.