TERADATA

 

 

Faculty: SRINIVAS

 

24 Hours Lab, Unlimited WiFi

|Duration: 45 Hours

 

Demo Date:


Teradata Architecture and Components

PDE(Parallel Data Extension)

Vprocs (virtual processors)

PE (Parsing Engine)

AMP (Access Module processing)

Board less Bynet

TDP (Teradata Directory Program)

CLI (Call Level Interface)

TPA (Trusted parallel Application)

Going in depth by explaining the process of SQL statement execution
Various Architecture

Node Architecture (shared nothing)

SMP Architecture

MPP Architecture

Parallelism Architecture

Benefit & types

Data Recovery and Protection

Object Locks-Various locks for simultaneous access

RAIDI

RAIDI5

Disk Arrays

Fall Back

Clique

AMP Clustering

Journals

Teradata Storage and Retrieval Architecture

Request processing


a.       Syntaxer


b.      Resolver


c.       Security Module


d.      Optimizer


e.       Step Generator


f.       GNC Apply


g.       Dispatcher
   

Primary index


1.      Hash algorithm


2.      Row hash


3.      DSW

4.      Hash Map


5.      Hash Bucket


6.      Reaching Vdisk


Teradata Indexes

Primary Index


Unique


Non unique


Partitioned


Secondary Index

Unique
Non unique


Hash, Join, Value Ordered


Skewness
        

Secondary Index Sub table


Accessing Records via primary index


Accessing records via Secondary index

Keys vs indexes
          

Teradata Sql Reference 


Fundamentals

Data Types and Literals


Data Definition Statements


Data manipulation statements

Teradata Functions and Operators 


String functions


Format functions


Cast functions


Group & Aggregate functions

With & with by clauses


Teradata Transactions

Implicit Transaction

Explicit Transaction


Performance Tuning and Explain Utility

Explain Usage


Collection Statistics


Tuning SQL Performance

Usage of PMON

Joints & Unions

Inner Join


Left Outer Join


Right Outer join

Full Outer Join

Join strategies 


Product join

Merge Join

Hash Join


Nested join


Teradata Basic Commands

HELP

SHOW


EXPLAIN


COLLECT STATISTICS


Teradata objects 


Tables


1.      SET table


2.      Multi Set table


3.      Volatile tables


4.      Global Temporary tables


5.      Derived tables


Views


Macros


Stored procedures

Triggers


Teradata spaces

PERM space


SPOOL space


TEMPORARY space


Teradata user and managing.

Teradata Transaction Modes

BTET

ANSI


Interactive

Batch


Load and Unload Utilities & Tools

Teradata Sql Assistant (Queryman)

 Teradata Performance Monitor


Teradata BTEQ

a. Batch Scripts with samples


b. Branching and Looping


c. Importing data through scripts


d. Exporting data through scripts


e.  Session Control Command Set


f.  Error handling.


Teradata Fast load


a. Various Phases of Fast Load


b.  Advantages and Process


c.  Limitations of Fast Load


d. Sample scripts


Teradata Multi Load


a. Various Phases of Multi load


b. Limitations of Multi Load


c. Working with multiple tables


d. Applying various operations


e. Sample Scripts

DATAWAREHOUSING CONCEPTS

History of Data warehouse

Need of a Data warehouse

OLTP Systems

Definition of Data warehouse

Difference b/w OLTP & DW

Datawarehousing Architecture

Data warehouse Vs. Data mart

Datawarehousing Approaches

Dimension Tables & Types

Slowly changing Dimension-Type1

Type 2 and Type 3

Conformed Dimension

Lately Arriving Dimension

Junk Dimension

Degenarated Dimension

Dirty Dimension

Fact Table & types

Measures & facts

Types of Facts

Datawarehousing Schemas

Data Acquisation Process

Project Life Cycle

Extraction types

 

WEBSPHERE DATASTAGE AND QUALITYSTAGE

Introduction to Websphere Datastage & Quality Stage

Parallel Processing

Pipeline parallel processing

Partition parallel processing

Difference b/w Informatica & Data Stage

Information Server Architectur

 

WEBSPHERE DATASTAGE AND QUALITYSTAGE DESIGNER

Introduction to Datastage Designer

Metadata Repository

Palette

Genaral Stages

Data Quality

Investigate Stage

Match Frequency

MNS

Standardize Stage

Unduplicate Match

WAVES

Survive Stage

File Stages

Database Stages

Informix Stage

Classic Fedaration

IWay Enterprise

Netteza Enterprise

Development/Debug Stages

Processing Stages

FTP Stage

Transformer Stage

Slowly Changing Dimension Stage

Websphere TX Map

Real Time Stages

Web services Client

Web services Transformer

WISD Input

WISD Output

XML input

XML Output

XML Transformer

Websphere MQ

Java Client

Java Transformer

SAP Plug-In Stages

ABAP Stage

IDoc Extract Stage

IDoc Load Stage

BAPI Stage

Restrecture Stages

Buildop Stages

Exporting the DS Components

Importing the DS Components

Managing the Repository

Data Set Management Utility

Message Handler for Manager

Performance Analysis

Resource Estimation

 

WEBSPHERE DATASTAGE AND QUALITYSTAGE DIRECTOR

Introduction to Datastage Director

Validation of Data Stage Jobs

Run, Stop, Reset, Kill, and Unlock the DS Jobs

Scheduling the DS Jobs

Debugging

Saving Log into a File

Creation of Batch jobs

Monitoring the DS Jobs

View status, Job Log, Schedule Logs

 

WEBSPHERE DATASTAGE AND QUALITYSTAGE ADMINISTRATOR

Introduction to Administrator

Adding & Deleting a Project

Cleaning the Project Files

Changing & upgrading the license

Auto Purging

NLS Configuration

Share Metadata when importing connections

Protection of DS Projects

Set Environmental Variables

Set permissions to USERS

Enable RCP

 

WEBSPHERE INFORMATION ANALYZER

Data quality Assessment Methodology

IBM Websphere Information Analyzer Architecture

Column Analysis

Primary Key Analysis

Foreign Key Analysis

Cross Domain Analysis

Publish Analysis Results

IBM Websphere Audit Stage Business Rule Validation

Baseline Analysis

Reports

Migration of Datastage Jobs 7.5x2 to 8.0.1

Websphere Information Services Director (WISD)

 

JOB SEQUENCE

Intraduction to Sequencer

Arrange Job Activities in Sequencer

Triggers in Sequencer

Job Activity Stage

Notification Activity Stage

Wait-for-file Activity Stage

Routine Activity

Add Check Points

Terminate Activity

User Variable Activity

SCD IMPLIMENTATION

Performance Tunning in Parallel Jobs