Navigate Azure with confidence - You will understand cloud models, IaaS, PaaS and SaaS services, and how to work in Azure Portal, so you can implement BI solutions in the Microsoft ecosystem more effectively.
Build a secure data environment - You will learn to use Azure Active Directory, Key Vault and monitoring features, allowing you to set up a data environment with proper access control, auditing and protection.
Work effectively with Data Lake - You will master data organization in Azure Data Lake Storage, file management and core data lake architecture, helping you build a well-structured foundation for analytics.
Use Azure SQL in practice - You will learn how to create, scale and maintain databases in Azure SQL Database, and how to plan backup and recovery, so your reporting and analytics sources stay reliable.
Automate data flows - You will learn to use Azure Data Factory and Logic Apps for copying, transforming and integrating data across services, helping you reduce manual effort in data processes.
Process large data sets - You will explore Azure Databricks fundamentals and see how to analyze large data volumes and connect them with other Azure services to prepare data for advanced analytics work.
Improve Power BI models and diagnostics - You will work with Power BI External Tools such as ALM Toolkit, Tabular Editor and DAX Studio, so you can refine semantic models, optimize DAX and manage changes more cleanly.
Compare Azure and Fabric wisely - You will understand when to choose Azure services and when Microsoft Fabric is a better fit, how capacity works, and what the key differences are for BI architecture and cost planning.
Cloud computing sets the foundation for the rest of the program by defining the service and deployment models used in Azure solutions. The section explains public, private, and hybrid approaches, distinguishes IaaS, PaaS, and SaaS, and discusses key benefits alongside common challenges.
The course starts from the Azure platform itself, so participants can place later services in a clear operational context. It covers the main service groups and features, account creation, navigation in Azure Portal, and resource management through Azure Resource Manager.
In Azure environments, security depends on consistent identity control, protection of sensitive assets, and traceability of actions. The section introduces cloud security principles, Azure Active Directory, secure secret storage in Azure Key Vault, and monitoring and auditing mechanisms.
Controlling cloud spending requires linking technical decisions with financial visibility. This section covers planning and monitoring Azure costs, using dedicated cost management tools, applying optimization practices, and preparing cost tracking and reporting that supports ongoing governance.
Azure Data Lake Storage provides the repository layer for files and large data sets used across analytics solutions. Participants examine its architecture and core capabilities, then work through principles of organizing, storing, and managing data and file structures in the lake.
From an operational perspective, Azure SQL Database supports both transactional workloads and managed administration in the cloud. The section includes database creation and management, scaling and performance considerations, as well as backup and recovery mechanisms used to protect data.
To move data reliably between sources, repositories, and downstream services, Azure Data Factory uses pipelines designed for repeatable execution. The section covers its role in copying and transforming data, with attention to how these processes connect to the wider Azure environment.
When data volume exceeds the limits of standard processing approaches, Azure Databricks becomes a central component of the platform. The section covers its architecture, core functions for big data processing and analysis, and the way it integrates with other Azure services.
Automating cross-system processes often requires more than point-to-point integration. This part focuses on Azure Logic Apps, showing how to build and manage workflows, connect services and external systems, and apply practical automation patterns in Azure-based solutions.
Working with Power BI can be extended through external tools that support model development, analysis, and deployment control. Participants use ALM Toolkit, Tabular Editor, and DAX Studio to understand how each tool fits specific tasks in a reporting and semantic model workflow.
Microsoft Fabric is presented as a BI platform that combines analytics services within one environment. The section explains how to create and manage Capacity, outlines the main Fabric capabilities, and compares its scope with selected Azure services in terms of roles and usage.
Basic data handling - You should be comfortable working with tables, files and common data formats, so you can more easily follow data loading, storage and integration across Azure services.
Relational database basics - You should understand relational database concepts such as tables, relationships and simple queries, because the training includes Azure SQL and reporting data sources.
Power BI fundamentals - You should know the Power BI interface and understand reports, data models and measures, because the training covers external tools and semantic model development.
Confident computer use - You should be comfortable using a web browser, desktop applications and basic system settings, so you can work efficiently in Azure Portal and Power BI tools.
