A data warehouse is a centralized repository that stores data from multiple information sources and transforms them into a common, multidimensional data model for efficient querying and analysis. In this course, students learn about implementation and organizational issues surrounding a data warehouse project.
- 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.
- Lectures 21
- Quizzes 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 0
- Certificate No
- Assessments Yes
- What is and OLTP?
- What is Data Warehouse?
- OLTP Vs Data Warehouse?
- Why do you need Data Warehouse?
- How do you build datawarehouse
- Data Staging
- Definitions and descriptions
- Data Warehouse Architecture
- Introduction to BI tools
- Introduction to ETL tools
- Top Down Vs Bottom Up Approach
- Data Warehouse Vs Data Marts
- Type 1, Type 2 and Type 3 Slowly Changing Dimension updates
- Surrogate key Concept.
- Operational Data Store (ODS)
- Star Schema Vs Snow Flake Schema
- Facts & Dimensions
- Relational Data Modeling Vs Dimensional Data Modeling?
- Physical Vs Logical Modeling?