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SQL201 - Microsoft SQL Server 2008 R2 Mark Souza Director Microsoft SQL Server.

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Presentation on theme: "SQL201 - Microsoft SQL Server 2008 R2 Mark Souza Director Microsoft SQL Server."— Presentation transcript:

1 SQL201 - Microsoft SQL Server 2008 R2 Mark Souza Director Microsoft SQL Server

2 SQL Server 2008 – Strong Release

3 The SQL Server 2008 R2 Journey The origins of Kilimanjaro Self-service Business Intelligence Application & Multi-server Management Scaling for the next generation enterprise High End Scale out Data Warehouses CEP – Complex Event Processing Reaching the summit

4 Project “Gemini” Excel Add-in Report Builder 3.0 StreamInsight, Complex Event Processing Master Data Services SharePoint Publishing Application & Multi-Server Management Project “Gemini” SharePoint Management Console StreamInsight.Net Extensions Enterprise-level security, scalability Supports up to 256 Logical Processors SQL Server System Preparation Enhanced Data Compression Solid Foundation for Enterprise Workloads Hyper-V™ Live Migration Support for largest Windows Server hardware MPP support for 100+ terabyte data warehouses Appliance-like data warehouse on industry standard hardware Project “Madison” Better Together with Windows Server

5 The SQL Server 2008 R2 Journey The origins of Kilimanjaro Self-service Business Intelligence Application & Multi-server Management Scaling for the next generation enterprise High End Scale out Data Warehouses CEP – Complex Event Processing Reaching the summit

6 What's in a name… Gemini - Gemini (pronounced /ˈgɛmɪnaɪ/, Latin: twins, symbol ♊ ) is one of the constellations of the zodiac known as "the twins"/ˈgɛmɪnaɪ/Latin twinsconstellationszodiac

7 The corporate Twins: IT Pro/End User A widening gap between end user and IT needs End Users : – Access to corporate data – Mix in their own data – Aggregate, augment data – Organize, present solutions – Share insights with others IT Professionals: – Know data is secure – Know data is consistent – Keep systems running – Keep the cost down – Track data access & usage There need not be an end-user versus IT conflict or gap in meeting user needs The gap is caused by lack of enabling technology, heavy “app lifecycle” costs I’m not exactly sure what I need but I know I need it now… If I help this time I’m stuck maintaining it forever…

8 The Challenges New formal BI solutions need time and resources Diverse users have diverse data needs Ad-hoc requests stress I.T. capacity Data warehouses do not cover all data or all users Power users bypass I.T. with unsanctioned sources BottleneckChaos

9 Gemini: Uniting the Twins Re-draws the line between I.T. and end-user roles Empowered to create without IT dependence Managing compliance and resources without user obstruction resources without user obstruction I.T. Provision Administer Secure Track data Users Directly model Analyze Personalize Share data

10 Excel is key for IW/Users “It has to be Excel” “We don’t get OLAP & dimensional models” “What is data modeling anyway?” “Just make my Excel better" Use Excel as a catch all tool to Collect data Clean, prepare and integrate it Enrich and Analyze Create reports and visualizations Share them with others Easy sharing of insights is critical Each power user publishes data to 10’s-100’s consumers IT needs to know !

11 SNEAK PEAK NICHOLAS DRITSAS PROGRAM MANAGER SQL SERVER PRODUCT TEAM

12 IT manage the "Spreadmarts" Excel is the IW tool of choice, but for IT: Excel is a problem - “unmanageable” Excel is an addiction – users “can’t quit it” Why not make Excel part of the solution? Include Excel as part of a complete BI solution Structured and manageable Give IT insight into its usage Provide IT with the technology to Have insight and management Become a strategic differentiator Without being a bottleneck Enable managed Self-Service

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14 The SQL Server 2008 R2 Journey The origins of Kilimanjaro Self-service Business Intelligence Application & Multi-server Management Scaling for the next generation enterprise High End Scale out Data Warehoues CEP – Complex Event Processing Reaching the summit

15 Trends Challenges: People vs. Hardware Number of database apps Number of DBA’s Hardware computing capacity Underutilized hardware Overburdened Administrators Database apps increasing at a higher rate than DBAs Overburdened DBAs Hardware computing capacity exploding Underutilized hardware

16 Control server sprawl with 1 to many management – setup is fast and easy Introducing a better way TodayTomorrow Single unit of deployment – increase deployment and upgrade efficiency

17 Key Concepts Data-Tier Application Component (DAC) Think of this as the new unit of deployment for T-SQL apps and providing similar benefits of a MSI in a very general sense. There is a definition of all the parts that make up the app along with services such as Install, Uninstall, Upgrade, and eventually Repair. SQL Server Manageability Confidential – Internal Use Only 17 Data-Tier Application Unit (DAU) Think of this as the overall unit of management. Or the deployed instance of a DAC Maps to a plain database in KJ. In SQL 11, a CDB - a more self-contained database (with additional dependent objects). Provides namespace and resource isolation. DAC DAC Deployment Profile Deployment Requirements, Management Policies, Failover Policies Logical Tables, Views, Constraints, SProcs, UDFs Users, Logins Physical Indexes, Partitions FileGroups … Unit of Deployment DAU – (C)DB Schema Tables, Views, Constraints, SProcs, UDFs, Users, Logins Indexes, Partitions, FileGroups DAC Properties & Metadata Deployment Requirements, Management Policies, Failover Policies Unit of Management

18 Key Concepts (continued..) SQL Server Manageability Confidential – Internal Use Only 18 Utility Control Point (UCP) Think of this as the central reasoning point of the utility. From here operations such as policy evaluation, discovery, deployment, impact, and what if analysis can be performed. Connection Virtualization (Medusa) Think of this as DNS for connection strings Decouples application from the physical location of DAU (CDB) Uses Active Directory (KJ). Management Studio DBA SQL02 SQL03 SQL04 SQL01 Managed Instances SQL05 UCP

19 New wizards in SSMS – fast and easy setup Create a Control Point Enroll instances Insights refreshed every 15 minutes Key Benefits Control Optimization Efficiencies Management Studio Database Administrator Microsoft Confidential—Preliminary Information Subject to Change SQL Server Control Point Managed Server Group Gain Visibility and Control

20 At-a-glance views for insights Microsoft Confidential—Preliminary Information Subject to Change Key Benefits Control Optimization Efficiencies Improve Resource Optimization Simple UI for policy adjustments ID consolidation opportunities Quickly drill-down to detailed views

21 Application & Multi-Server Management Creating the UCP Creating the UCP Insights – Health Check Insights – Health Check

22 Single unit of deployment Integration with Visual Studio Streamlined deployments & upgrades Client “Finance” Management Studio Database Administrator Central management Microsoft Confidential—Preliminary Information Subject to Change Data-Tier Developer Key Benefits Control Optimization Efficiencies Managed Server Group Improve Efficiencies

23 Application & Multi-Server Management Creating the DAC Creating the DAC Migrating the DAC Migrating the DAC

24 Application & Multi-server Management Productive database application development and management via Introduction of new Database Application Components (DAC) Application of Policy Based Administration to DACs Intellisense integration with Visual Studio Ability to version, deploy and reverse engineer a DAC Multi-server Management made easier through DAC experiences integrated with Management Studio and Visual Studio Import and Export of database application artifacts Support for reverse engineering a DAC from down-level systems Deployment to one or more target systems Monitoring of multiple instances of a database application on several servers via Management Studio

25 The SQL Server 2008 R2 Journey The origins of Kilimanjaro Self-service Business Intelligence Application & Multi-server Management Scaling for the next generation enterprise High End Scale out Data Warehouses CEP – Complex Event Processing Reaching the summit

26 The Data Warehouse scale journeyScale-upScale-up Massive Scale-out FastTrack Reference Architecture – 10s TB Easier, predictable and cost effecient 10s of TB

27 27 Fast Track DW Appliance-like time to value Flexibility through choice of HW platforms Low TCO through commodity hardware and value pricing. Reduced risk through pre-tested and pre-tuned configurations Provides a clear upgrade path to “Madison” via Hub/Spoke Microsoft Confidential—Preliminary Information Subject to Change 27

28 Scale out Data Warehousing

29 Massively Parallel Processing True MPP, Shared Nothing Architecture Server/CPU’s have their own dedicated resources Secret Sauce is MPP Query Optimizer supporting Parallel operations Lightning-fast Queries, Data Loads And Updates Linear Scalability Lower TCO- Reduced DBA time MPPMPP

30 Control Rack High-Level Madison Architecture 30 Compute Node

31 Date Dim D_ DATE _ SK D_ DATE _ ID D_ DATE D_ MONTH … Date Dim D_ DATE _ SK D_ DATE _ ID D_ DATE D_ MONTH … Item I _ ITEM _ SK I _ ITEM _ ID I _ REC _ START _ DATE I _ ITEM _ DESC … Item I _ ITEM _ SK I _ ITEM _ ID I _ REC _ START _ DATE I _ ITEM _ DESC … Store Sales Ss_sold_date_sk Ss_item_sk Ss_customer_sk Ss_cdemo_sk Ss_store_sk Ss_promo_sk Ss_quantity … Store Sales Ss_sold_date_sk Ss_item_sk Ss_customer_sk Ss_cdemo_sk Ss_store_sk Ss_promo_sk Ss_quantity … Promotion P_ PROMO _ SK P_ PROMO _ ID P_ START _ DATE _ SK P_ END _ DATE _ SK … Promotion P_ PROMO _ SK P_ PROMO _ ID P_ START _ DATE _ SK P_ END _ DATE _ SK … Store S_STORE_SK S_STORE_ID S_REC_START_DATE S_REC_END_DATE S_STORE_NAME … Store S_STORE_SK S_STORE_ID S_REC_START_DATE S_REC_END_DATE S_STORE_NAME … Customer C-C USTOMER _ SK C_ CUSTOMER _ ID C_ CURRENT _ ADDR … Customer C-C USTOMER _ SK C_ CUSTOMER _ ID C_ CURRENT _ ADDR … Customer Demographics C D _ DEMO _ SK C D _ GENDER C D _ MARITAL _ STATUS C D _ EDUCATION … Customer Demographics C D _ DEMO _ SK C D _ GENDER C D _ MARITAL _ STATUS C D _ EDUCATION … Database Tables Madison Appliance Nodes C C I I D D CD S S P P C C I I D D S S P P C C I I D D S S P P C C I I D D S S P P C C I I D D S S P P C C I I D D S S P P SS Large Tables Are Hash Distributed Smaller Tables Are Replicated

32 DBA Work Made Easy Create Database With(AUTOGROW = ON | OFF DISTRIBUTION_SIZE = value_in_GB REPLICATION_SIZE = value_in_GB LOG_SIZE = value_in_GB Create Database With(AUTOGROW = ON | OFF DISTRIBUTION_SIZE = value_in_GB REPLICATION_SIZE = value_in_GB LOG_SIZE = value_in_GB CREATE DATABASE sampledb_288 ON PRIMARY (NAME = N'sampledb_288', FILENAME = N'[DRIVE_LETTER]:\primary\sampledb_288.mdf', SIZE = 3MB, MAXSIZE = UNLIMITED, FILEGROWTH = 10%), FILEGROUP DIST_A (NAME = N'DIST_A_1', FILENAME = N'[DRIVE_LETTER]:\data_01\sampledb_288_DIST_A_1.ndf', SIZE = 625MB, MAXSIZE = UNLIMITED, FILEGROWTH = 4MB), FILEGROUP REPLICATED (NAME = N'REPLICATED_9_1', FILENAME = N'[DRIVE_LETTER]:\data_01\sampledb_288_REPLICATED_9_1.ndf', SIZE = 125MB, MAXSIZE = UNLIMITED, FILEGROWTH = 4MB), LOG ON (NAME = N'sampledb_288_LOG_1', FILENAME = N'[DRIVE_LETTER]:\log_01\sampledb_288_LOG_1.ldf', SIZE = 1000MB, MAXSIZE = UNLIMITED, FILEGROWTH = 10%); ALTER DATABASE sampledb_288 SET AUTO_CREATE_STATISTICS ON; ALTER DATABASE sampledb_288 SET AUTO_UPDATE_STATISTICS ON; ALTER DATABASE sampledb_288 SET RECOVERY SIMPLE; CREATE DATABASE sampledb_288 ON PRIMARY (NAME = N'sampledb_288', FILENAME = N'[DRIVE_LETTER]:\primary\sampledb_288.mdf', SIZE = 3MB, MAXSIZE = UNLIMITED, FILEGROWTH = 10%), FILEGROUP DIST_A (NAME = N'DIST_A_1', FILENAME = N'[DRIVE_LETTER]:\data_01\sampledb_288_DIST_A_1.ndf', SIZE = 625MB, MAXSIZE = UNLIMITED, FILEGROWTH = 4MB), FILEGROUP REPLICATED (NAME = N'REPLICATED_9_1', FILENAME = N'[DRIVE_LETTER]:\data_01\sampledb_288_REPLICATED_9_1.ndf', SIZE = 125MB, MAXSIZE = UNLIMITED, FILEGROWTH = 4MB), LOG ON (NAME = N'sampledb_288_LOG_1', FILENAME = N'[DRIVE_LETTER]:\log_01\sampledb_288_LOG_1.ldf', SIZE = 1000MB, MAXSIZE = UNLIMITED, FILEGROWTH = 10%); ALTER DATABASE sampledb_288 SET AUTO_CREATE_STATISTICS ON; ALTER DATABASE sampledb_288 SET AUTO_UPDATE_STATISTICS ON; ALTER DATABASE sampledb_288 SET RECOVERY SIMPLE; Madison Generates

33 The SQL Server 2008 R2 Journey The origins of Kilimanjaro Self-service Business Intelligence Application & Multi-server Management Scaling for the next generation enterprise High End Scale out Data Warehouses CEP – Complex Event Processing Reaching the summit

34 What Is CEP? Complex Event Processing (CEP) is the continuous and incremental processing of event streams from multiple sources based on declarative query and pattern specifications with near-zero latency. Database ApplicationsEvent-driven Applications Query Paradigm Ad-hoc queries or requests Continuous standing queries LatencySeconds, hours, daysMilliseconds or less Data RateHundreds of events/secTens of thousands of events/sec or more request response Event output stream input stream

35 Microsoft’s CEP Solution Data Sources, Operations, Assets, Feeds, Sensors, Devices Monitor & Record Monitor & Record Operational Data Store & Archive CEP Engine f(x) g(y) CEP Engine f(x) f'(x) g(y) h(x,y) History Deploy Results f'(x) h(x,y) Manage & Benefit Manage & Benefit Mine & Design Mine & Design Input Data Streams Output Data Streams

36 CEP Deployment Alternatives Data Sources Aggregation & Correlation CEP CEP for lightweight processing and filtering CEP for aggregation and correlation of in-flight events CEP for complex analytics including historical data Event processing engines are deployed at multiple places on different scales At the edge – close to the data source In the mid-tier – consolidate related data sources In the data center – historical archive, mining, large scale correlation Devices Sensors Web servers Feeds CEP Complex Analytics & Mining

37 LINQ Query Examples LINQ Example – GROUP&APPLY, WINDOW: from e3 in MyStream3 group e3 by e3.i into SubStreams from s4 in SubStreams from e4 in s4.SlidingWindow(FiveMinutes,ThreeSeconds) select new { pl = new MyNewPayload(e4.i, e4.f)}; LINQ Example – GROUP&APPLY, WINDOW: from e3 in MyStream3 group e3 by e3.i into SubStreams from s4 in SubStreams from e4 in s4.SlidingWindow(FiveMinutes,ThreeSeconds) select new { pl = new MyNewPayload(e4.i, e4.f)}; LINQ Example – JOIN, PROJECT, FILTER: from e1 in MyStream1 join e2 in MyStream2 on e1.ID equals e2.ID where e1.f2 = “foo” select new { e1.f1, e2.f4 }; LINQ Example – JOIN, PROJECT, FILTER: from e1 in MyStream1 join e2 in MyStream2 on e1.ID equals e2.ID where e1.f2 = “foo” select new { e1.f1, e2.f4 }; Join Filter Project Grouping Window

38 Recap: CEP Platform from Microsoft CEP Engine Output Adapters Input Adapters Event Standing Queries Event sources Event targets Event C_IDC_NAMEC_ZIP Event Static reference data CEP Application Development Development experience with.NET, C#, LINQ and Visual Studio 2008 CEP platform from Microsoft to build event- driven applications Event-driven applications are fundamentally different from traditional database applications: queries are continuous, consume and produce streams, and compute results incrementally Flexible adapter SDK with high performance to connect to different event sources and sinks The CEP platform does the heavy lifting for you to deal with temporal characteristics of event stream data

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