Data Warehouse Fundamentals
Description
In our training you’ll learn about the main challenges you can face in the process of building data warehouses. You’ll understand how goals influence the selection of architecture and what consequences insufficient attention to components may lead to. You will also get an idea of team members’ roles and impact on the result.
We cover the practical approaches to designing and implementing a data warehouse and its components - life cycle management, including decommissioning and migration to new systems, and review topics such as data management and building related services.
Throughout the training you will be involved in various exercises that help you put your skills to the test – you will either work on a project of data warehouse migration or assess it in terms of capabilities, resources, and timing.
is issued on the Luxoft Training form
Objectives
- Understand the role and tasks of data warehouse in the enterprise IT landscapes
- Learn all the stages of the DWH life cycle, from designing to implementation, operation and decommissioning
- Learn how to avoid typical mistakes in creating data warehouses, and become familiar with methods and best practices for successful maintenance of data warehouses
Target Audience
- Software architects
- Technical leads and senior developers
- System analysts and designers
- DWH Product Owners
- DWH Project Managers
- DWH Unit Managers
- Data quality (DQ) engineers
- Business intelligence (BI) experts
Roadmap
Introduction
- The idea of “data warehouse.” - its capabilities and constraints
- The purpose of DWH and business tasks it solves
Components and Architecture
- Traditional approaches to data warehouse design
- Standard components and processes
- Concepts of Inmon, Kimball, and DataVault methodologies
- Overview of major components (stage, ODS, DDS, Data Marts, BI, Metadata) and processes (ETL, ELT, DQ, lineage)
Data Governance
- General and specific issues of enterprise data governance
- Information as an asset that brings value and requires costs on obtaining
- The concept of “master data” and master data management (MDM)
Methods of Data Warehouse Design
- Design steps
- Standard techniques and tools
- Stakeholders and infrastructure expertise
Initial Data Store Area - Stage
- Need to store initial data from the source system
- Typical mistakes in organizing this store area and its difference from “Data Lake.”
Permanent Storage Areas - ODS and DDS
- Layers of operational and multi-dimensional data storage
- Retrieval, cleaning, control, and storing processes - ETL\ELT
- Transformation into a target storage system
Data Consumer Systems
- Typical use cases of data retrieval from data warehouses
- BI systems as major data warehouse consumers
- Standard BI systems and reasons for their diversity
New Challenges in the Evolution of Data Warehouses
- Overview of major scalability problems with a data warehouse
- New challenges in machine learning
- The concept of Data Mesh as an alternative for future development