DP-601 : Implementing a Lakehouse with Microsoft Fabric

AUDIENCE

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.

Module 1: Introduction to end-to-end analytics using Microsoft Fabric

• Describe end-to-end analytics in Microsoft Fabric

Module 2: Get started with lakehouses in Microsoft Fabric

• Describe the core features and capabilities of lakehouses in Microsoft Fabric
• Create a lakehouse
• Ingest data into files and tables in a lakehouse
• Query lakehouse tables with SQL

Lab: Create and ingest data with a Microsoft Fabric Lakehouse

Module 3: Use Apache Spark in Microsoft Fabric

• Configure Spark in a Microsoft Fabric workspace
• Identify suitable scenarios for Spark notebooks and Spark jobs
• Use Spark data frames to analyze and transform data
• Use Spark SQL to query data in tables and views
• Visualize data in a Spark notebook

Lab: Analyze data with Apache Spark

Module 4: Work with Delta Lake tables in Microsoft Fabric

• Understand Delta Lake and delta tables in Microsoft Fabric
• Create and manage delta tables using Spark
• Use Spark to query and transform data in delta tables
• Use delta tables with Spark structured streaming

Lab: Use delta tables in Apache Spark

Module 5: Ingest Data with Dataflows Gen2 in Microsoft Fabric

• Describe Dataflow (Gen2) capabilities in Microsoft Fabric
• Create Dataflow (Gen2) solutions to ingest and transform data
• Include a Dataflow (Gen2) in a pipeline

Lab: Create and use a Dataflow (Gen2) in Microsoft Fabric

Module 6: Use Data Factory pipelines in Microsoft Fabric

• Describe pipeline capabilities in Microsoft Fabric
• Use the Copy Data activity in a pipeline
• Create pipelines based on predefined templates
• Run and monitor pipelines

Lab: Ingest data with a pipeline