Data WareHouse Training
Live Online & Classroom Enterprise Training Course
Become an expert on Datawarehouse.Acquire the expertise to efficiently load data into the data warehouse and leverage techniques such as partition exchange load and data compression during the Extract, Transform, Load (ETL) process to optimize performance
Data warehousing training Overview Duration and Structure
Course Overview
Data warehousing training teaches individuals how to design, develop, and maintain a data warehouse. This involves learning how to extract data from various sources, transform and clean the data, and then load it into a central repository for efficient querying and analysis. Data warehousing training also covers how to create and manage a data mart, which is a subset of a data warehouse focused on a specific subject or department. Data warehousing training may also include topics such as data modeling, ETL (extract, transform, load) processes, and dimensional modeling.
Data warehouse full course Details
At the end of the training, participants will be able to:
- Explain the concept and need of Data warehouse
- Implement concepts of dimension and fact table
- Implement data modeling, normalization and schema concepts
- Model a Data warehouse
- Implement ETL jobs
Pre-requisite
- The pre-requisites for data warehouse course includes basic understanding of Databases.
Duration
2 days
Course Outline
- Understanding a Data Warehouse
- Why a Data Warehouse is Separated from Operational Databases
- Data Warehouse Features
- Data Warehouse Applications
- Types of Data Warehouse
- Metadata
- Metadata Repository
- Data Cube
- Data Mart
- Virtual Warehouse
- Delivery Method
- IT Strategy
- Business Requirements
- Technical Blueprint
- Building the version
- History Load
- Ad hoc Query
- Automatio
- Process Flow in Data Warehouse
- Extract and Load Process
- Business Analysis Framework
- Three-Tier Data Warehouse Architecture
- Data Warehouse Models
- Load Manager
- Warehouse Manager
- Query Manage
- Relational OLAP (ROLAP)
- Multidimensional OLAP (MOLAP)
- Hybrid OLAP (HOLAP)
- Specialized SQL Servers
- Star Schema
- Snowflake Schema
- Fact Constellation Schema
- Horizontal Partitioning
- Vertical Partitio
- What is Metadata?
- Categories of Metadata
- Role of Metadata
- Metadata Repository
- Challenges for Metadata Management
- Dimensions and facts – What are dimensions and facts?
- Types of dimensions – emphasis on SCD 1,2,3 implementations
- Types of facts
- What are hierarchies – Types of Hierarchies
- Normalizatio
- Requirement gathering
- Principles of dimensional modelling
- Modeling – ER diagrams
- ETL Concept – Architectural components – like Source, Staging, Atomic, Dimension
- Transformation – Data Validation, Data Accuracy, Data Type Conversion, Business Rule Application
- Data Loading techniques.
- Why Do We Need a Data Mart?
- Cost-effective Data Marting
- Designing Data Marts
- System Managers and Process Managers
- Security Requirements
- Documentation
- Impact of Security on Design
- Backup Terminologies
- Hardware Backup
- Software Backups
- Unit testing
- Integration testing
- System testing
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