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This course is designed to build your foundational skills in data engineering on Microsoft Fabric, focusing on the Lakehouse concept. This course will explore the powerful capabilities of Apache Spark for distributed data processing and the essential techniques for efficient data management, versioning, and reliability by working with Delta Lake tables. This course will also explore data ingestion and orchestration using Dataflows Gen2 and Data Factory pipelines. This course includes a combination of lectures and hands-on exercises that will prepare you to work with lakehouses in Microsoft Fabric.

Audience Profile

The primary audience for this course is data professionals who are familiar with data modeling, extraction, and analytics. It is designed for professionals who are interested in gaining knowledge about Lakehouse architecture, the Microsoft Fabric platform, and how to enable end-to-end analytics using these technologies.

Job role: Data Analyst, Data Engineer, Data Scientist

You should be familiar with basic data concepts and terminology

Introduction to end-to-end analytics using Microsoft Fabric

Discover how Microsoft Fabric can meet your enterprise's analytics needs in one platform. Learn about Microsoft Fabric, how it works, and identify how you can use it for your analytics needs

Introduction
Explore end-to-end analytics with Microsoft Fabric
Data teams and Microsoft Fabric
Enable and use Microsoft Fabric
Knowledge Check
Summary

Get started with lakehouses in Microsoft Fabric

Lakehouses merge data lake storage flexibility with data warehouse analytics. Microsoft Fabric offers a lakehouse solution for comprehensive analytics on a single SaaS platform.

Introduction
Explore the Microsoft Fabric Lakehouse
Work with Microsoft Fabric Lakehouses
Exercise - Create and ingest data with a Microsoft Fabric Lakehouse
Knowledge check
Summary

Use Apache Spark in Microsoft Fabric

Apache Spark is a core technology for large-scale data analytics. Microsoft Fabric provides support for Spark clusters, enabling you to analyze and process data in a Lakehouse at scale.

Introduction
Prepare to use Apache Spark
Run Spark code
Work with data in a Spark dataframe
Work with data using Spark SQL
Visualize data in a Spark notebook
Exercise - Analyze data with Apache Spark
Knowledge check
Summary

Work with Delta Lake tables in Microsoft Fabric

Tables in a Microsoft Fabric lakehouse are based on the Delta Lake storage format commonly used in Apache Spark. By using the enhanced capabilities of delta tables, you can create advanced analytics solutions.

Introduction
Understand Delta Lake
Create delta tables
Work with delta tables in Spark
Use delta tables with streaming data
Exercise - Use delta tables in Apache Spark
Knowledge check
Summary

Ingest Data with Dataflows Gen2 in Microsoft Fabric

Data ingestion is crucial in analytics. Microsoft Fabric's Data Factory offers Dataflows (Gen2) for visually creating multi-step data ingestion and transformation using Power Query Online.

Introduction
Understand Dataflows (Gen2) in Microsoft Fabric
Explore Dataflows (Gen2) in Microsoft Fabric
Integrate Dataflows (Gen2) and Pipelines in Microsoft Fabric
Exercise - Create and use a Dataflow (Gen2) in Microsoft Fabric
Knowledge check
Summary

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Implementing a Lakehouse with Microsoft Fabric - Applied Skills Workshop Training Course in Switzerland

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Implementing a Lakehouse with Microsoft Fabric - Applied Skills Workshop Training in Switzerland

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Implementing a Lakehouse with Microsoft Fabric - Applied Skills Workshop Training in Switzerland

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