Oracle - Oracle Database 11g: Data Warehousing Fundamentals

PROKODA Gmbh
In Berlin, Erlangen und Münster

1.890 
Möchten Sie den Bildungsanbieter lieber direkt anrufen?
0221/... Mehr ansehen

Wichtige informationen

Beschreibung

Oracle - Oracle Database 11g: Data Warehousing Fundamentals: Data Warehouse-Administrator. Data Warehouse-Analytiker. Entwickler. Projektmanager. In this course, students learn the basic concepts of a data warehouse and study the issues involved in planning, designing, building, populating, and maintaining a successful..
Gerichtet an: Data Warehouse-Administrator. Data Warehouse-Analytiker. Entwickler. Projektmanager. In this course, students learn the basic concepts of a data warehouse and study the issues involved in planning, designing, building, populating, and maintaining a successful data warehouse. Students learn to improve performance or manageability in a data warehouse using various Oracle Database features.. Students...

Wichtige informationen
Veranstaltungsort(e)

Wo und wann

Beginn Lage
auf Anfrage
Berlin
Zimmerstr. 79/80, 10117, Berlin, Deutschland
Plan ansehen
auf Anfrage
Erlangen
Zeppelinstr.10, 91052, Bayern, Deutschland
Plan ansehen
auf Anfrage
Münster
Münsterstraße 111, 48155, Nordrhein-Westfalen, NRW, Deutschland
Plan ansehen

Häufig gestellte Fragen

· Voraussetzungen

Empfohlene Vorkenntnisse:_x000D_
Knowledge of general data warehousing concepts _x000D_
Knowledge of client-server technology _x000D_
Knowledge of relational server technology _x000D_

Themenkreis

KURSZIEL
Oracle - Oracle Database 11g: Data Warehousing Fundamentals:
  • Data Warehouse-Administrator
  • _x000D_

  • Data Warehouse-Analytiker
  • _x000D_

  • Entwickler
  • _x000D_

  • Projektmanager
  • _x000D_

    _x000D_

    In this course, students learn the basic concepts of a data warehouse and study the issues involved in planning, designing, building, populating, and maintaining a successful data warehouse. Students learn to improve performance or manageability in a data warehouse using various Oracle Database features. _x000D_

    _x000D_

    Students also learn the basics about Oracle's Database partitioning architecture and identify the benefits of partitioning. Students review the benefits of parallel operations to reduce response time for data-intensive operations. Students learn about the extract, transform, and load of data phase (ETL) into an Oracle database warehouse. Students learn the basics about the benefits of using Oracle's materialized views to improve the data warehouse performance. Students also learn at a high level how query rewrite can improve a query's performance. Students review OLAP and Data Mining and identify some data warehouse implementations considerations._x000D_

    _x000D_

    Students briefly use some of the available data warehousing tools such as Oracle Warehouse Builder, Analytic Workspace Manager, and Oracle Application Express._x000D_

    _x000D_

    Learn To: _x000D_

  • Define the terminology and explain basic concepts of data warehousing
  • _x000D_

  • Identify the technology and some of the tools from Oracle to implement a successful data warehouse
  • _x000D_

  • Describe methods and tools for extracting, transforming, and loading data
  • _x000D_

  • Identify some of the tools for accessing and analyzing warehouse data
  • _x000D_

  • Describe the benefits of partitioning, parallel operations, materialized views, and query rewrite in a data warehouse
  • _x000D_

  • Explain the implementation and organizational issues surrounding a data warehouse project


  • ZIELGRUPPE
  • Data Warehouse-Administrator
  • _x000D_

  • Data Warehouse-Analytiker
  • _x000D_

  • Entwickler
  • _x000D_

  • Projektmanager
  • _x000D_

    _x000D_

    In this course, students learn the basic concepts of a data warehouse and study the issues involved in planning, designing, building, populating, and maintaining a successful data warehouse. Students learn to improve performance or manageability in a data warehouse using various Oracle Database features. _x000D_

    _x000D_

    Students also learn the basics about Oracle's Database partitioning architecture and identify the benefits of partitioning. Students review the benefits of parallel operations to reduce response time for data-intensive operations. Students learn about the extract, transform, and load of data phase (ETL) into an Oracle database warehouse. Students learn the basics about the benefits of using Oracle's materialized views to improve the data warehouse performance. Students also learn at a high level how query rewrite can improve a query's performance. Students review OLAP and Data Mining and identify some data warehouse implementations considerations._x000D_

    _x000D_

    Students briefly use some of the available data warehousing tools such as Oracle Warehouse Builder, Analytic Workspace Manager, and Oracle Application Express._x000D_

    _x000D_

    Learn To: _x000D_

  • Define the terminology and explain basic concepts of data warehousing
  • _x000D_

  • Identify the technology and some of the tools from Oracle to implement a successful data warehouse
  • _x000D_

  • Describe methods and tools for extracting, transforming, and loading data
  • _x000D_

  • Identify some of the tools for accessing and analyzing warehouse data
  • _x000D_

  • Describe the benefits of partitioning, parallel operations, materialized views, and query rewrite in a data warehouse
  • _x000D_

  • Explain the implementation and organizational issues surrounding a data warehouse project


  • KURSINHALT
    Kursziele:
    • Use materialized views and query rewrite to improve the data warehouse performance
    • Identify some of the new Oracle Database 11g features that aid in implementing the data warehouse
    • Describe methods and tools for extracting, transforming, and loading data
    • Identify some of the tools for accessing and analyzing warehouse data
    • Describe the benefits of partitioning in a data warehouse
    • Use parallel operations to reduce response time for data-intensive operations
    • Define the terminology and explain the basic concepts of data warehousing
    • Identify the technology and some of the tools from Oracle to implement a successful data warehouse
    • Describe methods and tools for extracting, transforming, and loading data
    • Identify some of the tools for accessing and analyzing warehouse data
    • Describe the benefits of partitioning, parallel operations, materialized views, and query rewrite in a data warehouse
    • Explain the implementation and organizational issues surrounding a data warehouse project
    • Define the terminology and explain the basic concepts of data warehousing
    • Define the decision support purpose and end goal of a data warehouse
    • Develop familiarity with some of the technologies required to implement a data warehouse
    • Identify some of the technology and tools from Oracle to implement a successful data warehouse
    Sachgebiete:
    Introduction
    • Course Objectives
    • Course Schedule
    • Course Pre-requisites and Suggested Pre-requisites
    • The sh and dm Sample Schemas and Appendices Used in the Course
    • Class Account Information
    • SQL Environments and Data Warehousing Tools Used in this Course
    • Oracle 11g Data Warehousing and SQL Documentation and Oracle By Examples
    • Continuing Your Education: Recommended Follow-Up Classes
    Data Warehousing, Business Intelligence, OLAP, and Data Mining
    • Data Warehouse Definition and Properties
    • Data Warehouses, Business Intelligence, Data Marts, and OLTP
    • Typical Data Warehouse Components
    • Warehouse Development Approaches
    • Extraction, Transformation, and Loading (ETL)
    • The Dimensional Model and Oracle OLAP
    • Oracle Data Mining
    Defining Data Warehouse Concepts and Terminology
    • Data Warehouse Definition and Properties
    • Data Warehouse Versus OLTP
    • Data Warehouses Versus Data Marts
    • Typical Data Warehouse Components
    • Warehouse Development Approaches
    • Data Warehousing Process Components
    • Strategy Phase Deliverables
    • Introducing the Case Study: Roy Independent School District (RISD)
    Business, Logical, Dimensional, and Physical Modeling
    • Data Warehouse Modeling Issues
    • Defining the Business Model
    • Defining the Logical Model
    • Defining the Dimensional Model
    • Defining the Physical Model: Star, Snowflake, and Third Normal Form
    • Fact and Dimension Tables Characteristics
    • Translating Business Dimensions into Dimension Tables
    • Translating Dimensional Model to Physical Model
    Database Sizing, Storage, Performance, and Security Considerations
    • Database Sizing and Estimating and Validating the Database Size
    • Oracle Database Architectural Advantages
    • Data Partitioning
    • Indexing
    • Optimizing Star Queries: Tuning Star Queries
    • Parallelism
    • Security in Data Warehouses
    • Oracle's Strategy for Data Warehouse Security
    The ETL Process: Extracting Data
    • Extraction, Transformation, and Loading (ETL) Process
    • ETL: Tasks, Importance, and Cost
    • Extracting Data and Examining Data Sources
    • Mapping Data
    • Logical and Physical Extraction Methods
    • Extraction Techniques and Maintaining Extraction Metadata
    • Possible ETL Failures and Maintaining ETL Quality
    • Oracle's ETL Tools: Oracle Warehouse Builder, SQL*Loader, and Data Pump
    The ETL Process: Transforming Data
    • Transformation
    • Remote and Onsite Staging Models
    • Data Anomalies
    • Transformation Routines
    • Transforming Data: Problems and Solutions
    • Quality Data: Importance and Benefits
    • Transformation Techniques and Tools
    • Maintaining Transformation Metadata
    The ETL Process: Loading Data
    • Loading Data into the Warehouse
    • Transportation Using Flat Files, Distributed Systems, and Transportable Tablespaces
    • Data Refresh Models: Extract Processing Environment
    • Building the Loading Process
    • Data Granularity
    • Loading Techniques Provided by Oracle
    • Postprocessing of Loaded Data
    • Indexing and Sorting Data and Verifying Data Integrity
    Refreshing the Warehouse Data
    • Developing a Refresh Strategy for Capturing Changed Data
    • User Requirements and Assistance
    • Load Window Requirements
    • Planning and Scheduling the Load Window
    • Capturing Changed Data for Refresh
    • Time- and Date-Stamping, Database triggers, and Database Logs
    • Applying the Changes to Data
    • Final Tasks
    Materialized Views
    • Using Summaries to Improve Performance
    • Using Materialized Views for Summary Management
    • Types of Materialized Views
    • Build Modes and Refresh Modes
    • Query Rewrite: Overview
    • Cost-Based Query Rewrite Process
    • Working With Dimensions and Hierarchies
    Leaving a Metadata Trail
    • Defining Warehouse Metadata
    • Metadata Users and Types
    • Examining Metadata: ETL Metadata
    • Extraction, Transformation, and Loading Metadata
    • Defining Metadata Goals and Intended Usage
    • Identifying Target Metadata Users and Choosing Metadata Tools and Techniques
    • Integrating Multiple Sets of Metadata
    • Managing Changes to Metadata
    Data Warehouse Implementation Considerations
    • Project Management
    • Requirements Specification or Definition
    • Logical, Dimensional, and Physical Data Models
    • Data Warehouse Architecture
    • ETL, Reporting, and Security Considerations
    • Metadata Management
    • Testing the Implementation and Post Implementation Change Management
    • Some Useful Resources and White Papers


    VORRAUSSETZUNG
    Empfohlene Vorkenntnisse:_x000D_

  • Knowledge of general data warehousing concepts
  • _x000D_

  • Knowledge of client-server technology
  • _x000D_

  • Knowledge of relational server technology
  • _x000D_


    Vergleichen Sie diesen Kurs mit ähnlichen Kursen
    Mehr ansehen