Presentation at Microsoft Architect Council in November 2007 - by Steve Muise, Neudesic

1. Steve Muise
Technology Strategist – SQL/BI
Neudesic LLC
Steve.muise@Neudesic.com
949.315.5888

2. Your Data Any Place, Any Time
Services
Integration
Reporting
Analysis Synch
FILE
RDBMS OLAP
Query Search
XML
Mobile and
Cloud
Desktop Server

3. Relational Data Documents & Multimedia
Applications
XML Spatial

4. Application Tier
BI Analysis Compliance
Application Integration Reporting
Increasing complexity
Data Model Tier
Object Mapping
Search & Indexing
Rich Query Capability
Caching & Synch
Storage Tier
Large Data Sets Reliability and Scale
Transactions & Security Referential Integrity
Relational Documents & XML
Data Spatial
Multimedia
Growth of the types of data to be hosted

6. SQL Server 2008
SQL Server 2005
• XML Data Type and • XML Upgrades
Functions
XML
• Remote BLOB Store API
Documents & • FILESTREAM
• Full Text Indexing
• Integrated FTS
Multimedia
• Fully supported Geometry
and Geography data types
Spatial and Functions
• Large UDTs
• Flexible Columns
• Wide Tables
• User Defined Types
Relational
• Filtered Indices
Data
• HierarchyID

7. • Storage Attribute on VARBINARY(MAX)
• Unstructured data stored directly in the
Store BLOBs in file system (requires NTFS)
DB + File System • Dual Programming Model
– TSQL (Same as SQL BLOB)
Application
– Win32 Streaming APIs with T-SQL
BLOB
transactional semantics
• Data Consistency
• Integrated Manageability
– Back Up / Restore
DB
– Administration
• Size limit is the file system volume size
• SQL Server Security Stack

8. • Full-Text Indexes fully integrated into SQL
Server
• Make mixed queries perform and scale
SELECT * FROM RealEstate
WHERE CONTAINS(Type, ’”New Home”’)
AND ZipCode = ‘92648’

9. Geometry Geography
• Geometry can store instances of • Very similar interface to
various types geometry
– Points, Line strings, Polygons – Some methods have different
semantics
– Collections of the above
• Most data commonly available
• Methods for computing
user data is geodetic
– Spatial relationships:
intersects, disjoint, etc.
– Spatial constructions:
intersection, union, etc.
– Metric functions: distance, area
– Planar is conceptually simpler, but
more specialized
Spatial Indexing supported for both data types

10. • Storage and retrieval of spatial data using
standard SQL syntax
– New Spatial Data Types (geometry, geography)
– New Spatial Methods (intersects, buffer, etc.)
– New Spatial Indexes
• Offers full set of Open Geospatial Consortium
components (OGC/SQL MM, ISO 19125)
• Query speed (spatial index)
• Integration with Virtual Earth

11. Spatial Data

12. • Improved XML Schema Validation
– Full support for storing & validating Office 12 Document formats
– Support for lax validation
– Full xs:dateTime support
• Support for no timezone values
• timezone preservation
– Support for lists and union types
• Added support for let-clause in XQuery
• Added support insert sql:variable(“@xml”) into /a/b

13. • New data type: HierarchyId
• Rich built-in methods for manipulating
hierarchies
– Simplifies storage and querying of hierarchical data
• Supports depth-first and breadth-first indexes

14. HierarchyID

15. • Scenarios
– Semi-structured Data: Property Set Storage
• Databases with heterogeneous data
• Distinct properties associated with subsets of data
• Large Number of sparsely populated properties
• Examples
– Product Catalogs
• Distinct Product specific properties
– Document Management Systems
• Document specific/user-defined properties
– GPS/Mapping Systems
• Location/Business specific Properties

16. pk c1 sc1 sc2 sc3 sc4 sc5 sc6 sc7 sc8 sc9
1 A 1 9
2 B 2 4
3 C 6 7
4 D 1 5
5 E 4 8
6 F 3 9
7 G 5 7
8 H 2 8
9 I 3 6

17. NULL values: 0 bytes. Non-NULL: +(2-4)b.
Slightly slower to access
pk c1 sc1 sc2 sc3 sc4 sc5 sc6 sc7 sc8 sc9
1 A (sc1, sc9) (1,9)
2 B (sc2, sc4) (2,4)
3 C (sc6, sc7) (6, 7)
4 D (sc1, sc5) (1,5)
5 E (sc4, sc8) (4, 8)
6 F (sc3, sc9) (3,9)
7 G (sc5, sc7) (5, 7)
8 H (sc2, sc8) (2, 8)
9 I (sc3, sc6) (3, 6)

18. • Column Set
– A logical grouping for all sparse columns in a table
Create Table Products(Id int, Type nvarchar(16),
ProductProperties XML COLUMN_SET
FOR ALL_SPARSE_COLUMNS);
– Updateable, computed XML column
– Select * returns all non-sparse-columns, sparse column set
– Allows generic retrieval/update of sparse columns as a set
• Limits are maintained throughout the system

19. pk c1 sc1 sc2 sc3 sc4 sc5 sc6 sc7 sc8 sc9
1 A 1 9
2 B 2 4
3 C 6 7
4 D 1 5
5 E 4 8
6 F 3 9
7 G 5 7
8 H 2 8
9 I 3 6
• Create index i1 on T(sc1) where C1=A or C1=D;
select sc1 from T where c1=A and sc1>5
• Create index i2 on T(sc7) where sc7 IS NOT NULL;
select sc1 from T where sc7=5

20. Data Any Place, Any Time
FILESTREAMS - Gives data consistency and integrated manageability
REMOTE STORE BLOB API – simple API’s for fetch, create,
enumerate, Garbage collection, delete
Returns reference that is stored in the db
Full-Text Indexing - Mixed query performance
Spatial Data – Integration with Virtual Earth
Relational Data
- HierarchyID - store arbitrary hierarchies of data and efficiently query them. New UDT to
implement hierarchies.
- Large UDTs - no more 8k limit
- Sparse columns - optimized storage for sparsely populated columns.
- Wide tables - support for hundreds of thousands of sparse columns.
- Filtered indexes - define indexes over subsets of data. Index on rows in a table with a
particular value.

22. Scalability and Performance
SQL 2008 Key Benefits
Effective Memory Balanced Workload
Efficient Data storage Optimal Concurrency
Utilization Performance
• Store relational and • Automatic, dynamic • Use Resource Governor • Proactive lock
non-relational data memory management to: management through
types efficiently the LOCK_ESCALATION
• Reduce disk I/O by • Differentiate
table option
• Data and backup maintaining buffer workloads
compression reduce pool • Isolation levels to suit
• Allocate hardware
storage requirements all concurrency
• Release memory to resources by
and improve I/O scenarios
system as required workload
performance • Granular locking to
• Support for large • Achieve predictable
• Sparse columns reduce maximize concurrent
memory allocation performance
NULL overhead access:
• 64 GB on Windows
• Row
Server Datacenter
Edition (using AWE) • Partition
• Up to 8 TB on 64-bit • Table
hardware • Database

23. High Availability
Key Benefits
Database Mirroring Failover Clustering Replication Online Operations
• Reliable data • Full server • Enterprise-wide • Online index
availability protection data availability management and
restore operations
• Automatic • Integration with • Easy to configure
failover with Windows Server and manage • Hot-add memory
transparent 2008 and CPUs
• Configuration
client • No “1drive wizard
redirection letter per • Visual
• Suspect page instance! management
recovery from limitation interface
mirror • Supports up to • Add nodes
• Optimal 16 nodes without stopping
performance • Cluster validation services
• Synchronous or to help you
asynchronous choose the right
mode hardware
• Log stream
compression

24. Security
Key Benefits
Authentication and Reduced Surface
Full Event Auditing Native Encryption
Authorization Area
• Enforce password • Secure by default • Audit all actions • Granular
policies for all across the encryption of data
• Minimize attack
users enterprise
exposure • Transparent
• Comprehensive, • Consolidate audit cryptography – no
• Enable only the
yet easy to reporting custom client
services and
manage development
features you
hierarchical required
require
security model • Consolidated key
management that
supports 3rd party
providers and
hardware security
modules

25. Manageability
Key Benefits
Centralized Automated Policy-Based Consolidated
Administration Operations Management Monitoring
• SQL Server • Scheduled • Declarative • Performance
Management database Management Studio
Studio – a single maintenance Framework (DMF) • Monitor, tune,
administration operations • Define policies and
interface for all • Automated alert for servers, troubleshoot
services and management databases, SQL Server
instances tables, etc. instances
throughout the
• Check and
enterprise
enforce policies:
• Collect
• Proactively
performance
• After changes
data from
• On a
multiple
scheduled
sources
basis
• Store
• Using ad-hoc
performance
checks
data centrally

26. CTP 5
• Encryption with application transparency
– Expands the SQL Server 2005 encryption offering
• Database level scope
– Introduces Database Encryption Key (DEK)
• Data at rest protection
– Backups are encrypted (by default?)
CREATE DATABASE ENCRYPTION KEY
WITH ALGORITHM = AES_128
ENCRYPTION BY SERVER CERTIFICATE ent_cert
ALTER DATABASE <database_name>
SET ENCRYPTION {ON | OFF}

27. CTP 5
• Smooth Upgrade
• Disallow plan changes for critical plans
• Troubleshooting queries (cardinality tuner)
• Plan fixing for ISV applications
• Stability between test and production systems

28. CTP 4
• High performance lightweight tracing infrastructure
– Easier to instrument
– Suitable for production environments
– Reduced troubleshooting time
• Integrated with ETW (Event Tracing for Windows)
– Enables activity correlation at unprecedented levels
Provides the foundation for other high performance tracing
subsystems in SQL Server (e.g. Audit)
Lays the foundation for key diagnostic and supportability
enhancements for troubleshooting SQL Server

29. CTP 6
• From building blocks in SQL Server 2005 to out-of-the-
box solution in SQL Server 2008
• AUDIT is a first Class Server Object
• Granular audit actions on database objects and/or users
• Multiple outputs (File, Windows Application Log, Security Event
Log)

30. CTP 6
• High performance
– Based on Extended Events
• Built in tools for consolidation of Audit Logs
across enterprise
– Function provided to help pull in logs to a table
• Full Analysis Services and Reporting Services
support on consolidated data

31. CTP 6
CREATE AUDIT HIPAA_Audit
TO FILE
(
FILENAME=’PRO1AudHIP_ADT.aud’,
Target
MAX_SIZE=100 MB,
RESERVE_DISK_SPACE
)
WITH (SHUTDOWN_ON_FAILURE = ON);
CREATE AUDIT SPECIFICATION
CREATE AUDIT SPECIFICATION
AuditAC
SvrAC
ON DATABASE
Server Database
ON SERVER
TO HIPAA_Audit
TO HIPAA_Audit
Audit Audit
ADD SELECT ON
ADD FAILED_LOGIN_GROUP;
table::Customers(payment);

32.  Declarative Management Framework
 Import/Export Surface Area Configuration
 Enforce Naming Convention
 Scalability/Security Policies
 Configuration Server
 Single Server configured to manage a group of servers
 Mass Query across all servers simultaneously
 Enforce DMF Policies
 Data Collector
 Collect data from multiple servers to a single MDW
 Collects from multiple sources (unlike Trace)
 Uses SSIS and Agent to move data
 Out of the box: T-SQL, Trace, Perf Counters

33. SQL Server
• Single resource pool Backup
• Database engine OLTP Activity
doesn’t differentiate Admin Tasks
workloads Executive Reports
Ad-hoc Reports
• Best effort resource
sharing Workloads
Memory, CPU, Threads, …
Resources

34. SQL Server
Executive
• Ability to differentiate Backup
OLTP Reports
workloads Activity
– e.g. app_name, Admin Tasks Ad-hoc
Reports
login
• Per-request limits OLTP Workload
Admin Workload Report Workload
– Max memory %
– Max CPU time
– Grant timeout Memory, CPU, Threads, …
– Max Requests
• Resource monitoring
Resources

35. Database
Engine
SQL Server
Putting it all together
Executive
• Workloads are mapped to Backup
OLTP Reports
Resource Pools (n : 1) Activity
• Online changes of Admin Tasks Ad-hoc
groups/pools
Reports
• SQL Server 2005 = default High
group + default pool OLTP Workload
Admin Workload Report Workload
Main Benefit
• Prevent run-away queries
Min Memory 10%
Max CPU 90%
Max Memory 20%
Max CPU 20%
Application Pool
Admin Pool

36. • Mirroring is now V2 – no fear
• Replication just got way easier, don’t get carried away though
• Locking down consistently across the enterprise is now easy
• Managing resources on the server just got way easier
• xEvents and Audits will be terribly useful, take the time to
figure out how to use them
• Service Broker (arguably one of the greatest things in SQL 05) is
getting easier to setup and manage

38. Connect To Your Data From Any
Device
SQL Server Change Tracking  Access your data from anywhere
 Store your data locally while
disconnected from server
Synchronized Programming
Model
 Synchronize Incremental changes
between client and server
Visual Studio Support  Detect conflicts during
synchronization including deletes
 Add disconnected scenarios without
SQL Server Conflict
Detection re-writing existing applications

39. T-SQL “Delighters”
• T-SQL Enhancements
DECLARE @t int = 5;
INSERT dbo.myT
VALUES (‘WA’, @t), (‘FL’, @t+1);
UPDATE dbo.myT
SET instances+=1;
• Continued investment and innovation in T-SQL

40. INSERT over DML
• Ability to have INSERT statement consume
results of DML
– Enhancement over OUTPUT INTO <table> clause
• DML OUTPUT can be filtered with a WHERE
clause
– Data accessing predicates not allowed
(sub-queries, data accessing UDFs and full-text)
• Why?
– History tracking of slowly changing dimensions
– Dumping DML data stream to a secondary table for
post-processing

41. INSERT over DML
INSERT INTO Books (ISBN, Price, Shelf, EndValidDate)
SELECT ISBN, Price, Shelf, GetDate() FROM
( MERGE Books T
USING WeeklyChanges AS S
ON T.ISBN = S.ISBN AND T.EndValidDate IS NULL
WHEN MATCHED AND
(T.Price <> S.Price OR T.Shelf <> S.Shelf)
THEN
UPDATE SET Price = S.Price, Shelf = S.Shelf
WHEN NOT MATCHED THEN
INSERT VALUES(S.ISBN, S.Price, S.Shelf, NULL)
OUTPUT $action, S.ISBN, Deleted.Price,
Deleted.Shelf
) Changes(Action, ISBN, Price, Shelf)
WHERE Action = 'UPDATE’;

42. MERGE statement
• New DML statement that combines multiple
DML operations
– Building block for more efficient ETL
– SQL-2006 compliant implementation
Source Target
XXXXX XXX XXX
XXXXX X XX XXX
If source matches target,
XXXX XXX
XXXX XXX
UPDATE
XXXXXXXXXX If no match, XXX XXX
X XXX XXXX XX INSERT
XX XXXX
If source not matched,
XXXXX XXX XX
DELETE

43. MERGE statement
MERGE Stock S
USING Trades T
ON S.Stock = T.Stock
WHEN MATCHED AND (Qty + Delta = 0) THEN
DELETE -- delete stock if Qty reaches 0
WHEN MATCHED THEN
-- delete takes precedence on update
UPDATE SET Qty += Delta
WHEN NOT MATCHED THEN
INSERT VALUES (Stock, Delta)
OUTPUT $action, T.Stock, inserted.Delta;

44. Date & Time Enhancements
• 4 new data types
• SQL Standard compatible
• 3 Byte Fixed Storage Size
• Date only
Date • From 0001-01-01 to 9999-01-01 in Gregorian
calendar
• Time only
Time (n) • Optional user specifiable fractional precisions
up to 100 nanoseconds
• Time-zone aware/preserved UTC datetime
DateTimeOffset(n) • Optional user specifiable fractional precisions
up to 100 nanoseconds
• Large date range
• Optional user specifiable fractional precisions up to
DateTime2 (n) 100 nanoseconds (Default)
• Time-zone NOT aware

45. Date & Time Data Types
CREATE TABLE t1 (c1 DATE, c2 TIME(3),
c3 DATETIME2(7) NOT NULL DEFAULT GETDATE(),
c4 DATETIMEOFFSET CHECK
(c4<CAST(GETDATE() AS DATETIMEOFFSET(0)))
);
INSERT INTO t1 VALUES ('0001-01-01', '23:59:59',
'0001-12-21 23:59:59.1234567',
'0001-10-21 23:59:59.1234567 -07:00');
INSERT INTO t1 VALUES ('9999-12-31', '23:59:59',
'9999-12-31 23:59:59.1234567',
'1111-10-21 23:59:59.1234567 -07:00');
SELECT c4,
DATEPART(TZOFFSET, c4),
DATEPART(ISO_WEEK, c4),
DATEPART(MICROSECOND, c4) FROM t1;

46. Table Types
• User-defined Table Types
– A new user defined type
– Aligned with inlined table definition for table variables
– Can be used for declaring table variables
– Can define indexes and constraints
– New Catalog view for table types Sys.table_types
• Benefits
– Usability, Type Matching, Precise Typing
• In 2000/5 we had UDF that returned TVF
– These were populated with hardcoded queries
– Couldn’t SELECT … INTO

47. Table-Valued Parameters (TVP)
• Parameters of type “Table Type”
• Input parameters on SPs/Functions
• New “ReadOnly” keyword is needed.
• TVPs are scoped within the SP/function body
• Optimized to scale and perform better for large
data
• Behaves like BCP inside server

48. Table Valued Parameters
And Merge

49. Grouping Sets
• Extension to the GROUP BY clause
• Lets you define multiple groupings in the same
query
• Produces a single result set that is equivalent to
a UNION ALL of differently grouped rows.
Makes aggregation querying and reporting easier
and faster

50. Grouping Sets

51. Top Take-Aways
• Moving to a paradigm shift from “application
centric” to “data centric”
• LINQ is coming – the app dev guys are going to want
to use, better learn it
• Separate Date and Time data types will reduce the
duct tape solutions
• INSERT over DML – track changes for compliance or
for warehousing (replace those triggers!)
• Table Type/TVP – get rid of temp tables
• Grouping Sets – stop building aggregation tables

53. Pervasive
Insight
Enterprise Data Warehouse
• Scale and Manage large number of
Partitioned Table Parallelism users and data
– Improve Query performance on large tables
– Optimize Queries for data warehousing
Star Join scenarios
– Increase I/O performance with efficient and
cost effective data storage
Data Compression – Manage concurrent workloads of ad-hoc
queries, reporting and analysis
• Integrate growing volumes of data
Persistent Lookups
– Optimize ETL performance by identifying
data in your largest tables
– Reduce the data load volumes by capturing
Change Data Capture (CDC)
operational changes in data
– Simplify the insert and update data processing
– Profile your information to identify dirty data
Data Profiling

54. Pervasive
Reach All Your Users With Insight
Scalable BI Platform
• Deliver insights throughout your
Scalable Report Engine
organization
– Deliver reports of any size at enterprise scale
Scale out Analysis
– Scale out through read-only Analysis Services
storage
Subspace Computations
– Enhance analytical capabilities with more
complex computations and aggregations
• Deploy and manage your BI infrastructure
New Cube Design Tools
– Streamline development of the analysis
infrastructure with new cube design tools
Best Practice Design Alerts
– Optimize cube design with real time best
practice alerts
Scalable Backup Tools – Backup cubes with enhanced scalability
– Deploy Reporting Services without IIS
dependency
IIS Agnostic Report Deployment

55. Pervasive
Empower Every User With Insight
Actionable Insights
• Deliver information via Microsoft Office
New Word Rendering
– Render reports to Microsoft Word
– Enjoy improved rendering to Microsoft Excel
Improved Excel Rendering
– Bring data mining to new, much broader
Data Mining Add-Ins for Excel audience
• Enable users to create powerful reports
Report Builder Enhancements
– Build powerful ad-hoc reports
– Create reports with any structure using Tablix
More Flexible Report Layout
– Add rich text regions your reports
– Embed powerful graphical data visualizations
Rich-Text Support
into reports (Dundas)
Enhanced Data Visualization • Empower users with enhanced analysis
– Empower users with enhanced write back
MOLAP Enabled Write Back
scenarios
– Accelerate end user prediction capabilities
Data Mining Engine Improvements
through enhanced Data Mining structures

56. Change Tracking
• Tracks the PK of a changed record
• Doesn’t track changed values
• Tracks RowVersion, including Deleted rows
• Tracks at Transaction Commit
– insure accuracy
• Used in Applications with multiple clients
• “Retention Period” defines archive depth
• Fast, Lightweight, Accurate way to determine if a
record has been changed since loaded into app.

57. Pervasive
Insight
Change Tracking

58. Pervasive
Insight
Change Data Capture (CDC)
• Mechanism to easily track changes on a table
– Changes captured from the log asynchronously
– Information on what changed at the source
• Feature only available in Enterprise and Developer Edition
• Table-Valued Functions (TVF) to query change data
– Easily consumable from Integration Services
XXXXX XXX XXX
Capture
XXXXX XXX XXX XXXX XXX XXXXXXXXXX X XXX XXXX XX
XXXX XXX
Process
Transaction
XXXXXXXXXX
X XXX XXXX XX
XX XXXX
Log
XXXXX XXX XX
Source
Table XXX
XXX
XXXXXX
XXX
CDC CDC
Functions Table

59. Pervasive
Insight
Change Data Capture (CDC)
• Mechanism to easily track changes on a table
– Changes captured from the log asynchronously
– Information on what changed at the source
• Feature only available in Enterprise and Developer Edition
• Table-Valued Functions (TVF) to query change data
– Easily consumable from Integration Services
EXEC sys.sp_cdc_enable_db_change_data_capture
EXEC sys.sp_cdc_enable_table_change_data_capture
EXEC cdc.fn_cdc_get_all_changes_<instance>
EXEC cdc.fn_cdc_get_net_changes_<instance>

60. Pervasive
Insight
Integration Services
New Threading Architecture
New Tasks/Components/Enumerators
Enhanced Lookup

61. Pervasive
Insight
Reporting Services
• Light Version For Novice Users
Stand-Alone Report Designer • Non-Visual Studio Windows Based Tool
On Demand Processing Model • Memory Footprint of large reports much smaller
• No longer dependent to IIS
• Uses HTTP.sys
Self Hosting
• Uses SQL Server’s networking stack
Manage Server from Management Studio
• Reports are no longer managed in browser
Unified Pagination Across Output Formats • Same number of pages should render with Excel,
PDF, Word, etc…
New Visualization
• Charts and Gauges (Dundas acquistion)
Tablix • Table + Matrix

62. SSRS „08 Performance
Response Time (Large Table with Constant Row Visibility)
900
800 RS2005 SP1
Response Time [ms]
700
RS2008 CTP6
600
500
400
300
200
100
0
0 50 100 150 200
Page Number
Response Time (Table With Grouping)
1200
RS2005 SP1
1000
RS 2008 CTP6
Respnse Time [ms]
800
600
400
200
0
0 50 100 150 200 250 300 350 400 450 500
Page Number

63. Pervasive
Insight
Tablix = Table + Matrix
Table
Matrix
Customer Growth
Retail 2001 2002 Total
Acme 19% Retail Acme 1,115 1,331 2,446
Nadir, Inc. 322% Nadir, Inc. 152 642 794
Wholesale Wholesale ABC Corp. 11,156 13,312 24,468
ABC Corp. 19% XYZ, Ltd. 1,523 6,421 7,944
Grand Total 13,946 21,706 35,653
XYZ, Ltd. 322%
Grand Total 56%

64. Pervasive
Insight
Hierarchal Rows with
Dynamic Headers
Matrix Tablix
2005 2006
2005 2006
Washington Seattle 50 60 Washington 80 100
Spokane 30 40 Seattle 50 60
Total 80 100 Spokane 30 40
Oregon Portland 40 50 Oregon 60 80
Eugene 20 30 Portland 40 50
Total 60 80 Eugene 20 30

65. Pervasive
Insight
Mixing Dynamic and
Static Columns
2005 2006 Pop Area
WA Seattle 50 60 WA Seattle 20 30
Matrix Spokane 30 40 Spokane 10 20
OR Portland 40 50 OR Portland 10 10
Eugene 20 30 Eugene 25 5
2005 2006 Pop Area
WA Seattle 50 60 20 30
Tablix Spokane 30 40 10 20
OR Portland 40 50 10 10
Eugene 20 30 25 5

66. Pervasive
Insight
Parallel Dynamic Groups
2005 2006 Table Chair
WA Seattle 50 60 WA Seattle 20 30
Matrix Spokane 30 40 Spokane 10 20
OR Portland 40 50 OR Portland 10 10
Eugene 20 30 Eugene 25 5
Year Product
2005 2006 Pop Area
Tablix WA Seattle 50 60 20 30
Spokane 30 40 10 20
OR Portland 40 50 10 10
Eugene 20 30 25 5

67. Chart Examples

68. Chart Examples

69. Gauge Examples

70. Pervasive
Analysis Services - Best
Insight
Practice Design Alerts
• Over 40 best practices integrated into real time
designer checks
• Blue Squiggly lines serving as build time
warnings
• Enable/Disable Alert Checks

71. Pervasive
Insight
Dimension Design
• Attribute Relationship Designer
– New Designer for viewing and editing attribute
relationships
– Multiple built-in validations for support of ideal dimension
design
• Dimension Wizard
• Dimension Editor

72. Pervasive
Insight
Dimension Design

73. SQL 2008 Summary
• Performance Enhancements
• Across the board – all things faster
• Make it easier to stay up
• Security Enhancements
• Encryption made easy
• Administration Enhancements
• Manage from single point
• Enforce rules/best practices
• Development
• Make hard tasks easier
• Business Intelligence
• Scale Improvements
• Design Improvements
• Sexy is easy now