SQL Server 2008 R2 Introduction Dejan Sarka Solid Quality Mentors

Agenda

The Data Platform for S+S Database Engine Relational Data Management Replication Full Text Search Integration Services ETL Processing Data Profiling StreamInsi ght* Complex Event Processing Analysis Services Classic OLAP Data Management Data Mining PowerPivot* Self Service Analytics Reporting Services Managed Reporting Self Service Reporting Embedded Reporting Master Data Services* Master Data Management

The Data Platform for S+S SERVICESSERVER Operating System Relational Database CLIENT Developer Tools Programming Model Application Services Systems Management Applications

SQL Server 2008 R2 Editions

Agenda

Compression

SQL 2008 Compression Row compression –Fixed-width data type values stored in variable format Page compression Prefix compression Dictionary compression

Unicode Compression Works on nchar(n) and nvarchar(n) Automatically with row or page compression Savings depends on language –Up to 50% in English, German –Only 15% in Japanese Very low performance penalty

Demo: SQL 2008 & 2008 R2 Compression demo

Data Tier Application (DAC)

Multi-Server Administration

Demo: DAC and UCP demo

Installation SQL Server 2008 –Slipstream installation –Uninstallation of cumulative updates and service packs SQL Server 2008 R2 –Imaging using SysPrep –Side by side installation with SQL Server 2008 –SharePoint integrated SSAS installation

SysPrep Considerations Use for Database Engine, and SSRS –Browser, SQL Server Writer, and Native Client are installed automatically when you prepare an instance of SQL Server Not supported: –Failover cluster installation –Tools –IA64 installations –Repair of a prepared instance –Can prepare on machines with R2 instances only

Agenda

SQL Server DW Offerings Personal and team level –PowerPivot for Excel (client) –PowerPivot for SharePoint (server) Corporate level –SQL Server Standard & Enterprise –Fast Track Data Warehouse –Parallel Data Warehouse

Scale-Up (SMP) Scale-out (MPP) 0 100GB 1TB 10TB 100TB 1PB+ SQL Server Corporate DW

Algorithms Complexity Forever* = about 40 billion billion years!

Data Warehousing Problems

Linearize Joins xy 1 = xy 2 = x 2 y 3 = x 2 per partes ,2 0,04 0,4 0,16 0,6 0,36 0,8 0, ,2 1,441,04 1,4 1,961,16 1,6 2,561,36 1,8 3,241, ,2 4,842,04 2,4 5,762,16 2,6 6,762,36 2,8 7,842,

Trans-Relational Model Not “beyond” relational –Transformation between logical and physical –Invented by Steve Tarin, Required Technologies Inc. (1999) All columns stored in sorted order –All joins become merge joins –Can condense storage –Of course, updates suffer Logically, this is a pure relational model

Column Oriented Storage Row / Col123 NameColorCity 1NutRedLondon 2BoltGreenParis 3ScrewBlueOslo 4ScrewRedLondon 5CamBlueParis 6CogRedLondon Row / Col123 NameColorCity 1Bolt [1:1]Blue [1:2]London [1:3] 2Cam [2:2]Green [3:3]Oslo [4:4] 3Cog [3:3]Red [4:6]Paris [5:6] 4Nut [4:4] 5Screw [5:6] 6

Reconstruction table Row / Col123 NameColorCity 1Bolt [1:1]Blue [1:2]London [1:3] 2Cam [2:2]Green [3:3]Oslo [4:4] 3Cog [3:3]Red [4:6]Paris [5:6] 4Nut [4:4] 5Screw [5:6] 6 Row / Col123 NameColorCity

PowerPivot demo

SQL Server Fast Track Data Warehouse Solution to help customers and partners accelerate their data warehouse deployments

Fast Track DW Components 27 Microsoft NDA-only Software: SQL Server 2008 Enterprise Windows Server 2008 Hardware: Tight specifications for servers, storage and networking ‘Per core’ building block Configuration guidelines: Physical table structures Indexes Compression SQL Server settings Windows Server settings Loading

SMP vs MPP SMPSMP HW advancements increasing ability to scale-up –Scaling is limited –High end SMP very expensive Extremely high concurrency for some workloads Less than 1-2 TB of data SMP will almost always be better Full SQL Server functionality HA must be architected in MPPMPP HW advancements increasing ability to scale-up & scale-out –Scaling to 1 PB+ –Scale out is relatively low cost Relatively high concurrency for complex workloads > 2 TB up to 1 PB Limited SQL Server functionality HA is built in

Sequential I/O Ideal for data warehousing Scalable, predictable performance Large reads & writes Requires 1/3 or fewer drives for same performance Random I/O Ideal for OLTP Not as predictable & scalable for data warehousing Small reads and writes Requires large number of drives Best practices focus on preserving the sequential order of data

Parallel Data Warehouse Architecture Control Node Active/Passive Control Node Active/Passive Landing Zone Configuration & Monitoring Backup Compute Nodes Client Drivers ETL Load Interface Corporate Backup Solution Corporate Network Private Network Spare Node Industry Standard SAN Storage Microsoft Cluster Server Distributed DB

The PDW Logical Data Model Multiple databases per appliance –Each user database maps to one SQL Server db per node Tables –Replicated, Distributed, Replicated + Distributed –Leverage SQL Server compression –Supports Partitioning –Supports secondary indexes Views

StreamInsight Complex Event Processing (CEP)

Reporting Services Data visualization –Maps –Sparklines –Data bars –Indicators Shared components –Datasets –Report data regions Tools –New Report Builder 3.0 –Better Report Manager Other enhancements –Lookup functions –ATOM data service

Reporting Services demo

Types of Data

Master Data

Master Data Management

MDM Approaches

Master Data Services Master Data Management solution An authoritative source for master data Central storage and services –SQL Server database –WCF API Serves as the system of entry and record Stewardship and integration capabilities

Master Data Services demo

Agenda Overview −Editions RDBMS Enhancements −Development −Administration Business Intelligence −Data Warehousing Options −StreamInsight −Reporting Services −Master Data Services

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