DP-600: Implementing Analytics Solutions Using Microsoft Fabric Course Outline

Special Note to New Hampshire Residents
This course has not yet been approved by the New Hampshire Department of Education. Please contact us for an update on when the class will be available in New Hampshire.

Overview
The DP-600T00: Microsoft Fabric Analytics Engineer course covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric. Students will build on existing analytics experience and will learn how to use Microsoft Fabric components, including lakehouses, data warehouses, notebooks, dataflows, data pipelines, and semantic models, to create and deploy analytics assets.

DP-600T00: Microsoft Fabric Analytics Engineer training is best suited for those who have the PL-300 certification or similar expertise in using Power BI for data transformation, modeling, visualization, and sharing. Also, learners should have prior experience in building and deploying data analytics solutions at the enterprise level.

What you will learn
Implement Dataflow solutions for data ingestion and transformation in Fabric, including Spark integration
Access external data sources, configure authentication, and optimize data intake for Fabric lakehouses
Learn techniques to load data into Fabric lakehouses as files or Delta tables
Understand and utilize Fabric's pipeline capabilities, including the Copy Data activity and pre-built templates, to orchestrate data flows
Design and create Fabric lakehouses, ingest data efficiently in various formats, and query data using SQL
Apply the principles of the medallion architecture within the Fabric environment for effective data management
Analyze data stored in Fabric lakehouses directly from Power BI using DirectLake capabilities
Configure and utilize Spark within Fabric, choose suitable scenarios for Spark notebooks and jobs, and manipulate data through Spark data frames
Understand and manage Delta Lake and delta tables in Fabric using Spark for efficient data management and transformations
Differentiate data warehouses from lakehouses, work with data warehouses in Fabric, implement data loading strategies, and build pipelines using T-SQL.

Who should attend
Data Analyst
Data Engineer

Prerequisites
Recommended
PL-300T00: Microsoft Power BI Data Analyst

Course Outline

Introduction to end-to-end analytics using Microsoft Fabric
Describe end-to-end analytics in Microsoft Fabric

Get started with lakehouses in Microsoft Fabric
Describe 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

Use Apache Spark in Microsoft Fabric
Configure Spark in a Microsoft Fabric workspace
Identify suitable scenarios for Spark notebooks and Spark jobs
Use Spark dataframes 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

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

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

Ingest Data with Dataflows Gen2 in Microsoft Fabric
Describe Dataflow capabilities in Microsoft Fabric
Create Dataflow solutions to ingest and transform data
Include a Dataflow in a pipeline
Lab: Create and use a Dataflow Gen2 in Microsoft Fabric

Get started with data warehouses in Microsoft Fabric
Describe data warehouses in Fabric
Understand a data warehouse vs a data Lakehouse
Work with data warehouses in Fabric
Create and manage fact tables and dimensions within a data warehouse
Lab: Analyze data in a data warehouse

Get started with Real-Time Intelligence in Microsoft Fabric
Describe Real-Time Intelligence in Microsoft Fabric
Create eventhouse databases and tables using KQL database
Describe Real-Time hub in Microsoft Fabric
Use KQL to query tables and create querysets
Lab: Explore Real-Time Intelligence in Fabric

Get started with data science in Microsoft Fabric
Understand the data science process
Train models with notebooks in Microsoft Fabric
Track model training metrics with MLflow and experiments
Lab: Explore data science in Microsoft Fabric

Get started with Data Activator in Microsoft Fabric
Understand Data Activator
Understand triggers, conditions, and actions in Data Activator
Get data from Power BI Reports and EventStreams with Data Activator
Assign data and create triggers in Data Activator
Interact with Real-Time Intelligence
Lab: Use Data Activator in Fabric

Administer Microsoft Fabric
Describe Fabric admin tasks
Navigate the admin center
Manage user access

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

Organize a Fabric lakehouse using medallion architecture design
Describe the principles of using the medallion architecture in data management
Apply the medallion architecture framework within the Microsoft Fabric environment
Analyze data stored in the lakehouse using DirectLake in Power BI
Describe best practices for ensuring the security and governance of data stored in the medallion architecture
Lab: Organize your Fabric lakehouse using a medallion architecture

Ingest Data with Dataflows Gen2 in Microsoft Fabric
Describe Dataflow capabilities in Microsoft Fabric
Create Dataflow solutions to ingest and transform data
Include a Dataflow in a pipeline
Lab: Create and use a Dataflow Gen2 in Microsoft Fabric

Ingest data with Spark and Microsoft Fabric notebooks
Ingest external data to Fabric lakehouses using Spark
Configure external source authentication and optimization
Load data into lakehouse as files or as Delta tables
Lab: Ingest data with Spark and Microsoft Fabric notebooks

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

Get started with data warehouses in Microsoft Fabric
Describe data warehouses in Fabric
Understand a data warehouse vs a data Lakehouse
Work with data warehouses in Fabric
Create and manage fact tables and dimensions within a data warehouse
Lab: Analyze data in a data warehouse

Load data into a Microsoft Fabric data warehouse
Learn different strategies to load data into a data warehouse in Microsoft Fabric
Learn how to build a data pipeline to load a warehouse in Microsoft Fabric
Learn how to load data in a warehouse using T-SQL
Learn how to load and transform data with dataflow (Gen 2)
Lab: Load data into a warehouse in Microsoft Fabric

Query a data warehouse in Microsoft Fabric
Use SQL query editor to query a data warehouse
Explore how visual query editor works
Learn how to connect and query a data warehouse using SQL Server Management Studio
Lab: Query a data warehouse in Microsoft Fabric

Monitor a Microsoft Fabric data warehouse
Monitor capacity unit usage with the Microsoft Fabric Capacity Metrics app
Monitor current activity in the data warehouse with dynamic management views
Monitor querying trends with query insights views
Lab: Monitor a data warehouse in Microsoft Fabric

Secure a Microsoft Fabric data warehouse
Learn the concepts of securing a data warehouse in Microsoft Fabric
Learn how to implement dynamic data masking to obscure sensitive information
Learn how to configure row-level security to provide granular control
Learn how to implement column-level security to protect sensitive data
Learn how to configure granular permissions using T-SQL
Lab: Secure a warehouse in Microsoft Fabric

Understand scalability in Power BI
Describe the importance of building scalable data models
Implement Power BI data modeling best practices
Use the Power BI large dataset storage format

Create Power BI model relationships
Understand how model relationship work
Set up relationships
Use DAX relationship functions
Understand relationship evaluation
Lab: Work with model relationships

Use tools to optimize Power BI performance
Optimize queries using performance analyzer
Troubleshoot DAX performance using DAX Studio
Optimize a data model using Tabular Editor
Lab: Use tools to optimize Power BI performance

Enforce Power BI model security
Restrict access to Power BI model data with RLS
Restrict access to Power BI model objects with OLS
Apply good development practices to enforce Power BI model security
Lab: Enforce model security
View outline in Word

ADP600

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