Oracle Database

Data Warehousing Fundamentals

Visão Geral do Treinamento
  • 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.

  • Define the terminology and explain 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.
  • Improve performance or manageability in a data warehouse using various Oracle Database features.


  • 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.

    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.

    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.

Público Alvo

  • Administrador de Data Warehouse
  • Analista de Data Warehouse

Carga Horária

  • 24 Horas

Pré-Requisitos

  • Knowledge of general data warehousing concepts
  • Knowledge of client-server technology
  • Knowledge of relational server technology

Metodologia

  • Aula expositiva e dinâmica com exercícios previamente desenvolvidos, mas não limitados a estes, podendo assim ser reproduzido cenários de interesse dos participantes.

No final do Treinamento o Aluno poderá

  • Run data manipulation statements (DML) to update data in the Oracle Database.
  • Design PL/SQL anonymous block that execute efficiently.
  • Describe the features and syntax of PL/SQL.
  • Handle runtime errors.
  • Describe stored procedures and functions.
  • Use PL/SQL programming constructs and conditionally control code flow (loops, control structures, and explicit cursors).
  • Use cursors to process rows.
  • Identify the major structural components of the Oracle Database 11g.
  • Retrieve row and column data from tables with the SELECT statement.
  • Create reports of sorted and restricted data.
  • Employ SQL functions to generate and retrieve customized data.
  • Display data from multiple tables using the ANSI SQL 99 JOIN syntax.
  • Create reports of aggregated data.
  • Run data definition language (DDL) statements to create and manage schema objects.

Recursos Utilizados

  • Instrutor qualificado para o projeto
  • Material didático
  • Laboratório com infraestrutura adequada
  • Certificado na conclusão



Conteúdo Programático
  • 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 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
  • 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
  • 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 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Project Management
  • Requirements Specification or Definition
  • ogical, 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

Redes Sociais

Comentários de participantes

Sugestão: Novos cursos na Evolutiontech
Empresa: gasNatural
_____________________________________________
O treinamento atendeu as expectativas
Empresa: Wood Group
_____________________________________________
O curso poderia ser um pouco mais extenso em função da quantidade de recursos abordados, porém o instrutor, material e sala de aula são excelentes.
Empresa: Wood Group
_____________________________________________
O instrutor foi consciente das nossas necessidades. Bom Coffee-Break e excelente material e sala de aula
Empresa: Oi
_____________________________________________
Muito bom curso!
Empresa: Oi

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