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Module 1: Explore Core Data Concepts
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This module focuses on foundational data-platform ideas: how data is represented, the distinction between relational/non-relational and transactional/analytical workloads, and the roles and responsibilities in the data ecosystem.
Lessons:

  • What is data?

  • How is data stored?

  • Transactional versus analytical data workloads

  • Data roles and responsibilities (DBA, Data Engineer, Data Analyst)

  • Cloud-based data service overview in Azure
    Key Topics:

  • Structured, semi-structured and unstructured data

  • Relational vs. non-relational models

  • Data workloads: transactional (OLTP) and analytical (OLAP)

  • Data professional roles

  • Azure data-services landscape

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Module 2:Explore Relational Data in Azure
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In this module learners examine the relational-database offerings in Azure, how to provision and query them, and when they are appropriate.
Lessons:

  • Introduction to Azure relational-database services

  • Provisioning Azure SQL family (Azure SQL Database, Managed Instance, SQL Server on VM)

  • Working with relational data objects and SQL queries in Azure

  • Open-source relational options (Azure Database for MySQL, PostgreSQL)
    Key Topics:

  • Relational database fundamentals: tables, normalization, SQL

  • Azure SQL services and deployment models

  • Relational query logic in Azure

  • Choosing the correct relational service

  • Operational and structural considerations for relational data in Azure

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Module 3: Explore Non-Relational Data in Azure
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This module covers non-relational (NoSQL) data stores and Azure options such as Blob storage, Data Lake, Table storage and Azure Cosmos DB.
Lessons:

  • Non-relational data-store types and uses

  • Azure storage for non-relational data: Blob, File, Table

  • Provisioning and deploying Azure Cosmos DB

  • APIs and models for Cosmos DB (SQL API, MongoDB API, Cassandra, etc.)
    Key Topics:

  • Non-relational data models and use-cases (key-value, document, graph)

  • Azure Blob, Data Lake Gen2, File and Table storage features

  • Azure Cosmos DB key capabilities and global distribution

  • Managing non-relational data in Azure

  • Choosing non-relational solutions based on workload

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Module 4: Explore Data Analytics in Azure
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This final module introduces analytics workloads: data ingestion, large-scale warehousing, real-time streaming, and visualization with tools like Microsoft Power BI.
Lessons:

  • What is large-scale data warehousing and analytics?

  • Data ingestion and processing pipelines in Azure

  • Batch vs. streaming data-processing approaches

  • Introduction to Power BI and visualization of processed data
    Key Topics:

  • Analytical data-stores and architecture for big data

  • Azure services for large-scale analytics (e.g., Azure Synapse, Databricks)

  • Real-time analytics concepts and streaming services

  • Data-visualization fundamentals and Power BI basics

  • Building end-to-end analytics pipelines in Azure

Labs / Practical Exercises (if applicable):

  • Provision an Azure SQL Database and execute sample SQL queries (Module 2)

  • Deploy an Azure Cosmos DB account, choose an API and perform CRUD operations (Module 3)

  • Build a simple data-pipeline for ingestion and visualize results in Power BI (Module 4)