Introduction to Modern Data Architecture

Overview

The primary aim of this course is to educate participants about modern aspects of data architecture. Today’s modern data architectures must support data-driven transformation, meaning we must be able to implement various concepts that complement each other. This education is designed first to define the needs, added value, and requirements for architecture, and then show the assessment process that will identify which architectures and design patterns are needed to support the desired data-driven transformation. After the introduction, we delve into architectures such as DWH, Data Lake, Lakehouse, Data Fabric, etc. In the end, we also go through the new concept of Data as a Product and Data Mesh.

Target audience

The course is intended for business and technical teams that need an introduction and a structured overview of Modern Data Architecture. Its intended roles are Business stakeholders, Architects, data engineers, and business and data analysts.

Prerequisites

The prerequisite for this course is that the participants are familiar with the concepts and architectures of data management.

Content

Day 1:

• What does it mean to be Data-driven?
  • Basic concepts
  • The basis of every data architecture
• Data Warehouse (DWH) and Business Intelligence (BI)
  • General architecture overview and application
  • How to model a DWH platform
  • The process of developing a DWH platform
  • Practical examples and tools
• Data Lake
  • General architecture overview
  • How to design a Data Lake platform (we don’t want a data swamp)
  • What is the process of developing a Data Lake platform
  • New derivatives Lakehouse, Data Fabric, tools used

Day 2:

• LakeHouse
  • General architecture overview and application
  • How to design a LakeHouse platform
  • The process of development within the LakeHouse platform
  • Practical examples and tools
• Data fabric
  • General architecture overview and application
  • How to design a Data Fabric platform
  • The process of development within the Data Fabric platform
  • Practical examples and tools
• Data Mesh
  • Why Data Mesh?
  • Four basic characteristics of Data Mesh (Domain driven, Data Product, self-service, and computational federate governance)

For all inquiries regarding education, please contact us at learn@croz.net.

Our experience: