Overview
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
Prerequisites
You should be familiar with basic data concepts and terminology
Outline
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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