Coffing Dw Physical-Table of Contents

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    Coffing Data WarehousingEducation Outline02/17/05

    TERADATA EDUCATIONOUTLINE

    Coffing Data Warehousing has provided quality Teradata education, productsand

    services for over a decade. We offer customized solutions to maximize yourwarehouse.

    Toll Free: 1-877-TERADATBusiness Phone: 1-937-855-4838

    Email: mailto:CDWSales@CoffingDW.comWebsite: http://www.CoffingDW.com

    In addition to the course material listed in this outline, we also offer Teradata classes in TeradataBasics, Implementation, SQL, Database Administration, Design and Utilities.Please contact us so we can customize a course to fit your specific needs.

    2006 Coffing Data Warehousing All rights reserved. Confidential. 1

    http://www.coffingdw.com/http://www.coffingdw.com/
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    Coffing Data WarehousingEducation Outline02/17/05

    PURPOSE

    Coffing Data Warehousing has been providing quality Teradata education for over adecade. We offer customized courses to maximize the effectiveness of each class.

    The purpose of this proposal is to build a lasting relationship with your company. To thisend, we have combined our comprehensive Teradata education services in a uniquepackage that we feel best suits the diverse needs of your company while offering ourhigh quality product at competitive pricing.

    Coffing Data Warehousing is excited to offer you, our preferred partner, an innovativenew way to look at training at the CoffingDW Teradata University (CDW-TU). Thisapproach provides the ability to maximize learning potential. Our goal is to make youremployees the most educated data warehouse experts in the industry.

    CURRICULUM:

    Coffing Data Warehousing will provide an experienced and highly qualified resource todeliver this customized educational seminar on the following topic(s):

    Teradata Education

    Teradata Physical Implementation

    COURSE DESCRIPTION

    COURSEPREREQUISITES There is no prerequisite for this course.

    COURSE This course is designed to be highlyDuration/Format interactive with the audience.

    COURSEAUDIENCE The audience will consist of a mix ofbeginning,intermediate and advanced

    Teradata users.

    OBJECTIVES This course is designed to provide in-depthknowledge of Teradata PhysicalImplementation.

    2006 Coffing Data Warehousing All rights reserved. Confidential. 2

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    Coffing Data WarehousingEducation Outline02/17/05

    Tera-Tom on Teradata Physical Implementationfor V2R6

    Chapter 1 The Rules of Data Warehousing

    Teradata CertificationA Logical View of the Teradata Architecture

    The Parsing Engine (PE)The Access Module Processors (AMPs)The BYNETA Visual for Data Layout

    Teradata Cabinets, Nodes, Vprocs, and Disks

    Chapter 2 Data Distr ibution Explained

    Rows and ColumnsThe Primary IndexThe Two Types of Primary IndexesUnique Primary Index (UPI)

    Turning the Primary Index Value into the Row HashThe Row Hash Value determines the Rows DestinationThe Row is Delivered to the Proper AMP

    The AMP will add a Uniqueness ValueAn Example of an UPI TableAn Example of a NUPI TableHow Teradata Retrieves RowsRow DistributionA Visual for Data Layout

    Teradata accesses data in three waysData Layout Summary

    Chapter 3 V2R5 Parti tion Primary Indexes

    V2R4 ExampleV2R5 PartitioningPartitioning doesnt have to be part of the Primary IndexPartition Elimination can avoid Full Table Scans

    The Bad NEWS about Partitioning on a column that is not part of thePrimary Index

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    Two ways to handle Partitioning on a column that is not part of thePrimary IndexPartitioning with CASE_NPartitioning with RANGE_N

    NO CASE, NO RANGE, or UNKNOWNPartitioning and J oins

    Chapter 4 Teradata Engine Under the Hood

    Full Cylinder ReadTable HeaderEach Table is given a Table IDHow Data Blocks are Dynamically BuiltData BlocksHow Teradata Finds a Row of Data

    The Master IndexThe Cylinder IndexCylinder Index ChangesHow Teradata Writes to an AMPWriting to Data Blocks of Equal LengthWhen a Data Block is not Big Enough for a WriteHow Teradata Allocates BlocksBlock and Row Definitions

    Large Row versus Oversized RowDefragmentationWhen a Cylinder becomes FullA Node and its Memory Allocations

    Chapter 5 The Extended Logical Data Model

    The Application Development Life CycleAsking the Right QuestionsLogical Data ModelPrimary KeysForeign KeysNormalizationExtended Logical Data Model

    The End Goal of the ELDM is to build Table TemplatesColumn ACCESS in the WHERE Clause

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    Data DemographicsDistinct Values

    Typical Rows Per ValueMaximum Rows NULL

    Change RatingExtended Logical Data Model Template

    Chapter 6 The Phys ical Data Model

    Step 1 Look at DistributionStep 2 Eliminate based on Change RatingStep 3 NUSI Elimination via Value Access FrequencyStep 4 Pick the Primary IndexPrimary Index FactorsWhy J oin Access Frequency is Top Priority?Why Value Access Frequency is Second Priority?What have we learned about picking the Primary Index?Step 5 Pick Secondary IndexesUSI to eliminate Duplicate Row CheckingNUSI considerationsMulti-Column NUSI Columns used as a Covered QueryValue-Ordered NUSIsA Strongly Vs Weakly Selective NUSI

    A formula for calculating a strongly selective NUSITypical Row SizeTypical Block Size

    Chapter 7 Denormalization

    Derived DataStoring AggregatesPreJ oining TablesRepeating GroupsHorizontal PartitioningVertical PartitioningCovered QuerySingle-Table J oin IndexesMulti-Table J oin Indexes

    Temporary Tables

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    Coffing Data WarehousingEducation Outline02/17/05

    Derived TablesVolatile Temporary TablesGlobal Temporary Tables

    Chapter 8 Secondary Indexes

    Unique Secondary Index (USI)USI Subtable ExampleHow Teradata retrieves an USI queryNUSI Subtable ExampleHow Teradata retrieves a NUSI queryValue-Ordered NUSIHow Teradata retrieves a Value-Ordered NUSI queryNUSI BitmappingPrototyping indexes with EXPLAINSecondary Index Summary

    Chapter 9 Join Strategies

    A J oin in Simple TermsThe key things to know about Teradata and J oinsMerge J oin Strategies

    J oins need the joined rows to be on the same AMP

    Another Great J oin PictureJ oining Tables with matching rows on different AMPsJ oining Tables with matching rows on different AMPsRedistributing a Table for J oin PurposesBig Table Small Table J oin StrategyBig Table Small Table DuplicationNested J oinHash J oinExclusion J oinProduct J oinsCartesian Product J oin

    Chapter 10 Join Indexes

    Three basic types of J oin IndexesJ oin Index Fundamentals

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    Coffing Data WarehousingEducation Outline02/17/05

    J oin Indexes versus other objectsMulti-Table J oin IndexSingle-Table J oin IndexesAggregate J oin Index

    Sparse IndexSparse Index PictureGlobal J oin IndexGlobal J oin Index PictureGlobal J oin Index Multi-Table J oin BackHash IndexesHash Indexes vs. Single-Table J oin Indexes

    Chapter 11 Explains

    The Teradata Optimizer knows how to Explain in DetailRow Estimate Confidence LevelsExplain TerminologyVisual Explain

    Chapter 12 The Parsing Engine in Detail

    The Parsing Engine (PE) goes through Six StepsEach PE has a Plan Library called RTS Cache

    The Parsing Engine has Data Dictionary CacheWhy the PE loves the Macro

    The Parsing Engine in DetailThe Parsing Engine Knows All

    Chapter 13 Understanding Views and Macros

    ViewsCreating Simple VIEWs and VIEWs that J oin TablesHow to Change a VIEW Using REPLACEHow to Drop a VIEWView Aggregation and Nested ViewsAll About MacrosMacros that Use ParametersChanging a MACRO Using REPLACEHow to Drop a MACRO

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    Chapter 14 - Locks

    Teradata has Four locks at Three levels

    Locks and their compatibilityHow Teradata Locks Objects

    Teradata Locks First Come First ServeLocking Queue Example 2Locking Queue Example 3Locking Modifier

    The NOWAIT Option

    Chapter 15 - Collect Statist ics

    Dynamic AMP SamplingHow Collect Statistics WorksSample StatisticsSample StatisticsWhat You Should COLLECT STATISTICS OnCOLLECT STATISTICS DETAILED SYNTAXCOLLECT STATISTICS Examples

    Chapter 16 - MISC

    Identity ColumnsIdentity Columns ExampleLIKE ClauseSUBSTRING and SUBSTR FunctionsReferential IntegritySoft Referential IntegrityMaterialized ViewsCompressionImplementing CompressionHow Compression WorksCreating a Table With DATABLOCKSIZE

    The ALTER TABLE CommandIndex WizardIndex Wizard

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