Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Cert Prep by Microsoft Press

Course details

Advance your professional skills and prepare for the DP-700 certification with this course on Microsoft Fabric. Master data ingestion and transformation using PySpark, Power Query, and KQL. Learn lifecycle management, security, optimization, and data storage options, including lakehouses and warehouses. Orchestrate data flow with pipelines and scheduling. Gain hands-on experience through mini-projects. Explore ways to effectively design, deploy, and optimize data engineering solutions using Microsoft Fabric. This course is ideal for analytics engineers, architects, analysts, and administrators.

Learning objectives
Implement and manage an analytics solution.
Ingest and transform data.
Monitor and optimize an analytics solution.

Concepts

Introduction

Exam DP-700 - Implementing Data Engineering Solutions Using Microsoft Fabric - Introduction

Lesson 1 - Introduction to Microsoft Fabric Data Engineering

Learning objectives
Understanding Microsoft Fabric fundamentals
Surveying the key components of Microsoft Fabric data engineering
Understanding the role of data engineers in modern data ecosystems
Comparing data engineering, data science, and analytics
Accessing Microsoft Fabric
Understanding required permissions and licensing
Quiz

Lesson 2 - Configure Fabric Workspaces

Learning objectives
Configuring Spark settings
Configuring domain settings
Configuring OneLake settings
Configuring data workflow settings
Quiz

Lesson 3 - Implement Lifecycle Management

Learning objectives
Using Git to version control workspaces and items
Utilizing database projects for warehouse
Implementing deployment pipelines
Quiz

Lesson 4 - Configure Security and Governance

Learning objectives
Configuring access controls for workspaces
Configuring access controls for items
Configuring RLS, CLS, object-level, and folder file-level access controls
Configuring dynamic data masking
Applying sensitivity labels and endorsing items
Configuring and using workspace logging
Configuring and using OneLake security
Quiz

Lesson 5 - How to Orchestrate Fabric Items

Learning objectives
Configuring pipeline schedules
Configuring notebook schedules
Configuring Dataflow Gen2 schedules
Using parameters and dynamic expressions in pipelines
Using parameters in notebooks
Quiz

Lesson 6 - Design and Implement Loading Patterns

Learning objectives
Implementing full data loads
Implementing incremental data loads
Preparing data for ingestion into a dimensional model
Quiz

Lesson 7 - Ingest and Transform Batch Data

Learning objectives
Choosing between a lakehouse and a warehouse for data storage
Transforming data using Power Query, PySpark, KQL, and T-SQL
Creating and managing lakehouse shortcuts
Creating and managing mirroring
Using pipelines to ingest data
Ingesting data by using continuous integration from OneLake
Designing a dimensional model
Grouping and aggregating data
Handling duplicate, missing, and late-arriving data
Quiz

Lesson 8 - Ingest and Transform Streaming Data

Learning objectives
Choosing between Eventstream, Spark Structured Streaming, and KQL for streaming
Understanding KQL database native storage, followed storage, and OneLake shortcuts
Using Eventstreams to process data
Using Spark Structured Streaming to process data
Using KQL to process data
Using windowing functions to query streaming data
Quiz

Lesson 9 - Monitor Fabric Items

Learning objectives
Monitoring data ingestion
Monitoring data transformation
Monitoring Power BI semantic model refreshes
Configuring alerts
Quiz

Lesson 10 - How to Identify and Resolve Errors

Learning objectives
Troubleshooting and resolving pipeline errors
Troubleshooting and resolving notebook errors
Troubleshooting and resolving Eventhouse and Eventstream errors
Troubleshooting and resolving T-SQL errors
Troubleshooting and resolving Shortcut errors
Quiz
Troubleshooting and resolving dataflow errors

Lesson 11 - How to Optimize Performance

Learning objectives
Optimizing a warehouse
Optimizing Eventstreams and Eventhouses
Optimizing Spark performance
Optimizing query performance
Quiz
Optimizing a lakehouse table
Optimizing a data factory pipeline

Lesson 12 - Practical Applications and Case Studies

Learning objectives
Surveying industry-specific examples (finance, healthcare, retail)
Exploring success stories using Microsoft Fabric
Applying lessons learned and best practices
Exploring guided mini-projects

Summary

Exam DP-700 - Implementing Data Engineering Solutions Using Microsoft Fabric - Summary