Understand Fabric architecture - You will learn how Microsoft Fabric works in terms of architecture, data team roles, and network backbone, so you can better judge how to apply the platform in your own environment.
Set up the platform yourself - You will go through Microsoft Fabric configuration and activation step by step, so after the course you can start working faster and avoid common implementation mistakes.
Master the Lakehouse approach - You will understand how a Lakehouse differs from a traditional data warehouse and when to use it, helping you design modern analytics solutions with better architectural decisions.
Work with data in Lakehouse - You will learn to explore, transform, and analyze data in Fabric Lakehouse, then present the results, so you can move from raw data to useful insights much more efficiently.
Run Apache Spark in practice - You will learn Spark requirements, environment setup, and session initialization, giving you confidence to launch processing jobs and prepare the platform for larger data workloads.
Process data with Spark and SQL - You will load data into DataFrames, perform transformations, and use Spark SQL, which will help you prepare datasets for analysis and reporting in a practical, repeatable way.
Use Delta Lake effectively - You will learn how to create and modify Delta Lake tables and track change history, making it easier for you to maintain data consistency, versioning, and control over updates.
Automate data flows - You will work with Gen2 dataflows and Data Factory pipelines, learning how to copy data, trigger processes, and monitor execution to build more reliable ETL and integration flows.
Microsoft Fabric is presented as an integrated analytics platform combining multiple data capabilities in one environment. The section explains its architecture, key functions, implementation benefits, and the role of data teams together with backbone network infrastructure in daily operation.
Before analytical work begins, the platform must be enabled and prepared correctly. This section addresses practical configuration steps, the activation process, initial setup decisions, and implementation practices that help teams establish a stable starting point in Microsoft Fabric.
The relationship between traditional data warehouses and the Lakehouse model is examined to show why this approach has become central in modern analytics. The section defines the concept, highlights key differences, and connects it with typical Microsoft Fabric use cases.
Within Fabric Lakehouse, data can be explored and refined in one analytical environment instead of being split across separate layers. The section covers browsing available datasets, transforming them for downstream use, and presenting analysis results produced from the prepared data.
Apache Spark architecture sets the foundation for later processing, so the training first clarifies its operating model and technical requirements. It then moves to configuring the environment, starting the platform, and initializing a session needed to begin practical work.
From loading raw datasets into a Spark DataFrame, the work moves toward preparing data for further analysis. The section covers core transformations as well as querying and manipulating data with SQL and the Spark SQL API, linking programmatic and declarative processing approaches.
Turning analytical output into readable insight requires more than generating charts. This section focuses on presenting Spark results with suitable visualization tools and on improving the clarity, structure, and effectiveness of data presentation for different analytical scenarios.
Delta Lake tables add transactional consistency and a structured storage layer to analytical workloads. Participants work on creating and modifying tables, then examine how versioning and change history support controlled updates, traceability, and reliable processing over time.
When ingestion needs to be prepared quickly, Gen2 dataflows provide a low-code way to collect and shape data inside Microsoft Fabric. The section explains how they work, where they fit well, what constraints to consider, and how they can be integrated into broader data processes.
Data Factory pipelines are used to automate recurring integration tasks and control data movement across Fabric. The section includes hands-on copying of data, work with ready-made templates, and methods for triggering runs and monitoring pipeline execution in practice.
Data analysis basics - You should understand core data concepts such as tables, columns, records, and simple transformations so you can work comfortably with the hands-on exercises.
SQL familiarity - You should be able to write basic SQL queries, including filtering, selecting columns, and simple joins, because these operations will appear in Spark SQL tasks.
Cloud environment skills - You should feel comfortable working with browser-based cloud interfaces, configuring resources, and navigating service portals used to manage platform settings.
Basic data handling - You should have experience loading files, reviewing datasets, and interpreting analysis results so you can focus on Fabric tools rather than core data basics.
