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    Oracle Database 11g Data Warehousing Fundamentals 1.0 Training & Placements in Chennai SQL STAR

    Oracle Database 11g: Data Warehousing Fundamentals

    (The Course materials and Course Completion Certificates are directly delivered from Oracle University to those seeking for Foreign Opportunity)

    (Government of India Approved Education Center – SQLSTAR Global)

    This Database 11G Data Warehousing training teaches data warehousing concepts and technologies, while examining Oracle’s approach to data warehouse implementation. Review partitioning, parallel operations, materialized views and more. This Oracle Database 11g: Data Warehousing Fundamentals training will teach you about the basic concepts of a data warehouse. Explore the issues involved in planning, designing, building, populating and maintaining a successful data warehouse.

    Learn To:

    • Define the terminology and explain basic concepts of data warehousing.
    • Identifythe technology and some of the tools from Oracle to implement asuccessful data warehouse.
    • Describe methods and tools for extracting, transforming and loading data.
    • Identifysome of the tools for accessing and analyzing warehouse data.
    • Describe the benefits of partitioning, parallel operations, materializedviews and query rewrite in a data warehouse.
    • Explainthe implementation and organizational issues surrounding a datawarehouse project.
    • Improveperformance or manageability in a data warehouse using variousOracle Database features.

    Oracle’s Database Partitioning Architecture You'll also explore the basics of Oracle’s Database partitioning architecture, identifying the benefits of partitioning. Review the benefits of parallel operations to reduce response time for data-intensive operations. Learn how to extract, transform and load data (ETL) into an Oracle database warehouse.

    Improve Data Warehouse Performance Learn the benefits of using Oracle’s materialized views to improve the data warehouse performance. Instructors will give a high-level overview of how query rewrites can improve a query’s performance. Explore OLAP and Data Mining and identify some data warehouse implementations considerations.

    Use Data Warehousing Tools During this training, you'll briefly use some of the available data warehousing tools. These tools include Oracle Warehouse Builder, Analytic Workspace Manager and Oracle Application Express.

    Suggested Prerequisite

    • Knowledgeof client-server technology
    • Knowledgeof relational server technology
    • Knowledgeof general data warehousing concepts

    Audience

    • Application Developers
    • ProjectManager
    • Developer
    • Support Engineer
    • DataWarehouse Analyst
    • FunctionalImplementer
    • DataWarehouse Developer
    • DataWarehouse Administrator

    Course Objectives

    • Describe methods and tools for extracting, transforming, and loading data
    • Identifysome of the tools for accessing and analyzing warehouse data
    • Identifythe technology and some of the tools from Oracle to implement asuccessful data warehouse
    • Define the decision support purpose and end goal of a data warehouse
    • Describe the benefits of partitioning, parallel operations, materializedviews, and query rewrite in a data warehouse
    • Explainthe implementation and organizational issues surrounding a datawarehouse project
    • Usematerialized views and query rewrite to improve the data warehouseperformance
    • Define the terminology and explain the basic concepts of data warehousing
    • Developfamiliarity with some of the technologies required to implement adata warehouse

    Course Topics

    Introduction

    • Thesh and dm Sample Schemas and Appendices Used in the Course
    • ClassAccount Information
    • CourseSchedule
    • Course Objectives
    • SQLEnvironments and Data Warehousing Tools Used in this Course
    • Oracle11g Data Warehousing and SQL Document ation and Oracle By Examples
    • CoursePre-requisites and Suggested Pre-requisites
    • Continuing Your Education: Recommended Follow-Up Classes

    Data Warehousing, Business Intelligence, OLAP, and Data Mining

    • DataWarehouse Definition and Properties
    • Extraction,Transformation, and Loading (ETL)
    • WarehouseDevelopment Approaches
    • TheDimensional Model and Oracle OLAP
    • DataWarehouses, Business Intelligence, Data Marts, and OLTP
    • Oracle Data Mining
    • TypicalData Warehouse Components

    Defining Data Warehouse Concepts and Terminology

    • DataWarehouse Definition and Properties
    • DataWarehouses Versus Data Marts
    • StrategyPhase Deliverables
    • TypicalData Warehouse Components
    • WarehouseDevelopment Approaches
    • Introducing the Case Study: Roy Independent School District (RISD)
    • DataWarehousing Process Components
    • DataWarehouse Versus OLTP

    Business, Logical, Dimensional, and Physical Modeling

    • Defining the Business Model
    • Factand Dimension Tables Characteristics
    • Translating Business Dimensions into Dimension Tables
    • Translating Dimensional Model to Physical Model
    • Defining the Physical Model: Star, Snowflake, and Third Normal Form
    • Defining the Logical Model
    • Defining the Dimensional Model
    • DataWarehouse Modeling Issues

    Database Sizing, Storage, Performance, and Security Considerations

    • Indexing
    • Oracle’sStrategy for Data Warehouse Security
    • Securityin Data Warehouses
    • Optimizing Star Queries: Tuning Star Queries
    • DatabaseSizing and Estimating and Validating the Database Size
    • Oracle Database Architectural Advantages
    • Parallelism
    • DataPartitioning

    The ETL Process: Extracting Data

    • ExtractionTechniques and Maintaining Extraction Metadata
    • PossibleETL Failures and Maintaining ETL Quality
    • Extraction,Transformation, and Loading (ETL) Process
    • Extracting Data and Examining Data Sources
    • Mapping Data
    • Logicaland Physical Extraction Methods
    • Oracle’sETL Tools: Oracle Warehouse Builder, SQL*Loader, and Data Pump
    • ETL:Tasks, Importance, and Cost

    The ETL Process: Transforming Data

    • Remoteand Onsite Staging Models
    • Transformation
    • Maintaining Transformation Metadata
    • QualityData: Importance and Benefits
    • DataAnomalies
    • TransformationRoutines
    • TransformationTechniques and Tools
    • Transforming Data: Problems and Solutions

    The ETL Process: Loading Data

    • DataGranularity
    • Loading Data into the Warehouse
    • TransportationUsing Flat Files, Distributed Systems, and TransportableTable spaces
    • Loading Techniques Provided by Oracle
    • Indexing and Sorting Data and Verifying Data Integrity
    • DataRefresh Models: Extract Processing Environment
    • Building the Loading Process
    • Postprocessing of Loaded Data

    Refreshing the Warehouse Data

    • Time-and Date-Stamping, Database triggers, and Database Logs
    • Developing a Refresh Strategy for Capturing Changed Data
    • Planning and Scheduling the Load Window
    • FinalTasks
    • Capturing Changed Data for Refresh
    • UserRequirements and Assistance
    • LoadWindow Requirements
    • Applying the Changes to Data

    Materialized Views

    • Using Summaries to Improve Performance
    • Working With Dimensions and Hierarchies
    • Types of Materialized Views
    • BuildModes and Refresh Modes
    • Using Materialized Views for Summary Management
    • QueryRewrite: Overview
    • Cost-BasedQuery Rewrite Process

    Leaving a Metadata Trail

    • Integrating Multiple Sets of Metadata
    • Managing Changes to Metadata
    • Defining Warehouse Metadata
    • Identifying Target Metadata Users and Choosing Metadata Tools and Techniques
    • MetadataUsers and Types
    • Extraction,Transformation, and Loading Metadata
    • Examining Metadata: ETL Metadata
    • Defining Metadata Goals and Intended Usage

    Data Warehouse Implementation Considerations

    • ProjectManagement
    • DataWarehouse Architecture
    • SomeUseful Resources and White Papers
    • ETL,Reporting, and Security Considerations
    • Logical,Dimensional, and Physical Data Models
    • Testing the Implementation and Post Implementation Change Management
    • MetadataManagement
    • RequirementsSpecification or Definition

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