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Oracle Data Warehouse Management

Course Description

What is Data Warehousing?

• Data Warehousing course is a fundamental course that is designed to establish the basic knowledge about data warehousing , including planning, designing, building, populating and maintaining a data warehouse.

• This course is a pre-requisite to the Oracle Business Intelligence course for the students that do not have work experiences in the related fields.

• Dimensional Modeling is a proven technique for designing and creating a data warehouse. This course will describe the dimensional modeling technique.

• The data warehouse ETL is considered as the major factor for the success of a data warehouse and considered the major efforts and costs spent on implementing a data warehouse.  This course will describe designing and building the process of extracting, cleaning, conforming and delivering data for the data warehouse

Who Should Attend

This course is designed for anyone who is interested in the career of working as a data warehouse designer, a BI application developer, an ETL designer and developer, a project manager who may be in charge of implementing and deploying a data warehouse, or a data warehouse database administrator.

About the Instructor

• 15 year experience in enterprise business application development
• 5 year in developing prepackaged BI Apps
• Various roles in the application development, including application engineer, application architect, solution architect, product manager, and development manager.

Course Duration

6 Weeks (Call for more details.)

Course Content

Week One – Data Warehouse Overview

  • • OLTP vs. OLAP
  • • Data Warehouse Lifecycle
  • • Dimensional Modeling
  • • Dimension
  • • Fact
  • • Granularity
  • • Surrogate key vs. Nature Key
  • • Star Schema
  • • Snow Flake Schema
  • • Data Mart
  • • Operational Data Store (ODS)
  • • Data Modeling
  • • ER and ERD
  • • Normalization
  • • Denormalization
  • • 3NF
  • • Primary Key and Foreign Key
  • • ETL
  • • Extract
  • • Transform
  • • Load

Week Two – Dimensional Modeling basic

  • • Dimension
  • • Conformed Dimension
  • • Slowly Changing Dimension (SCD)
  • • Snow Flake Model
  • • Dimension Hierarchy
  • • Rollup Dimension
  • • Degenerate Dimension
  • • Rapidly Changing Dimension
  • • Junk Dimension
  • • Minidimension
  • • Outrigger Dimension
  • • Role Playing Dimension
  • • Fact
  • • Transaction Fact
  • • Periodic Snapshot
  • • Accumulating Snapshot
  • • Factless Fact
  • • Multiple Currency Support
  • • Aggregate
  • • Rollup Dimension

Week Three – Dimensional Modeling Advance

  • • Determining Fact or Dimension
  • • Star or Snow Flake
  • • Dimension to Dimension Relationship
  • • Partition the Large Dimension
  • • Bitmap Index
  • • Star Transformation
  • • Materialized View

Week Four – ETL Process Design

  • • Source Study and Data Profiling
  • • Logical Mapping and Design
  • • Explore the change data capture process
  • • Capture and apply deletes to the data warehouse
  • • Support Multiple Source systems
  • • Conform and Standardize the data
  • • Generate the Time dimension
  • • Build the slowing change dimensions
  • • Resolve the Dimension Foreign Key from Fact
  • • Dimension Hierarchy Data Flattening
  • • Loading and Updating Aggregates

Week Five – ETL Development

  • • ETL Tool Evaluation Criteria
  • • Informatica Power Center Overview
  • • Designer
  • • Workflow Manager
  • • Repository Manager
  • • Informatica Overall Architecture
  • • Oracle Data Integrator Overview
  • • ODI studio
  • • ETL and ELT

Week Six – Analyzing and Presenting Data

  • • BI Tool Overview
  • • Ad Hoc Query Tool
  • • Report Creation
  • • Dashboard Creation
  • • Various Delivery Mechanisms


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