Exam DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric (Video)

Description

Master data engineering with Microsoft Fabric in this comprehensive training course and become a certified Microsoft Fabric Data Engineer Associate, skilled in data loading, transformation, and analytics solutions.

The DP-700 certification validates your ability to design, build, secure, and optimize endtoend data engineering solutions using Microsoft Fabric, covering key workloads such as data ingestion, transformation, orchestration, Lakehouse and Warehouse implementation, realtime intelligence, and performance optimization. It focuses on practical, enterprisegrade data engineering skills using technologies like SQL, PySpark, Kusto Query Language (KQL), Fabric notebooks, pipelines, Lakehouses, Warehouses, and OneLake, assessing your capability to support analytics, BI, and AI workloads across an organization.

This video course will equip learners with the knowledge necessary to approach the DP-700 exam with confidence, whilst also teaching the skills to implement data engineering solutions.

Skill Level:

• Beginner

Learn How To:

• Configure workspace settings in Microsoft Fabric, including Spark, domain, OneLake, and data workflow settings to ensure optimal performance and organization.
• Master data ingestion and transformation processes using SQL, PySpark, and Kusto Query Language (KQL) to prepare data for analysis.
• Develop skills to monitor and optimize data solutions, ensuring efficient and reliable data operations.
• Learn to design and implement data storage solutions that support scalable and secure data management.
• Understand and apply data security measures to protect sensitive information and ensure compliance with industry standards.
• Gain the ability to troubleshoot and resolve data-related issues, ensuring the integrity and availability of data for business intelligence.

Course requirement:

Before taking this DP-700 video course, learners should have the following knowledge:

• Basic Understanding of Data Engineering: Familiarity with data engineering concepts and practices.
• Proficiency in SQL: Ability to write and execute SQL queries for data manipulation
• Experience with PySpark: Basic knowledge of using PySpark for data processing
• Knowledge of Kusto Query Language (KQL): Basic understanding of KQL for querying and managing data.
• Analytical Skills: Strong analytical skills to interpret and transform data effectively.

Who Should Take This Course:

Job titles: Microsoft Fabric Data Engineer, Microsoft Fabric Data Developer, Microsoft Fabric Engineer, Data Analysts, Data Scientists

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video training at  http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Sample Content

Table of Contents

Introduction
Lesson 1: Introduction to Microsoft Fabric Data Engineering
Learning objectives
1.1 Understanding Microsoft Fabric fundamentals
1.2 Surveying the key components of Microsoft Fabric data engineering
1.3 Understanding the role of data engineers in modern data ecosystems
1.4 Comparing data engineering, data science, and analytics
1.5 Accessing Microsoft Fabric
1.6 Understanding required permissions and licensing
1.7 Quiz

Lesson 2: Configure Fabric Workspaces
Learning objectives
2.1 Configuring Spark settings
2.2 Configuring domain settings
2.3 Configuring OneLake settings
2.4 Configuring data workflow settings
2.5 Quiz

Lesson 3: Implement Lifecycle Management
Learning objectives
3.1 Using Git to version control workspaces and items
3.2 Utilizing database projects for warehouse
3.3 Implementing deployment pipelines
3.4 Quiz

Lesson 4: Configure Security and Governance
Learning objectives
4.1 Configuring access controls for workspaces
4.2 Configuring access controls for items
4.3 Configuring RLS, CLS, object-level, and folder/file-level access controls
4.4 Configuring dynamic data masking
4.5 Applying sensitivity labels and endorsing items
4.6 Configuring and using workspace logging
4.7 Configuring and using OneLake security
4.8 Quiz

Lesson 5: How to Orchestrate Fabric Items
Learning objectives
5.1 Configuring pipeline schedules
5.2 Configuring notebook schedules
5.3 Configuring Dataflow Gen2 schedules
5.4 Using parameters and dynamic expressions in pipelines
5.5 Using parameters in notebooks
5.6 Quiz

Lesson 6: Design and Implement Loading Patterns
Learning objectives
6.1 Implementing full data loads
6.2 Implementing incremental data loads
6.3 Preparing data for ingestion into a dimensional model
6.4 Quiz

Lesson 7: Ingest and Transform Batch Data
Learning objectives
7.1 Choosing between a lakehouse and a warehouse for data storage
7.2 Transforming data using Power Query, PySpark, KQL, and T-SQL
7.3 Creating and managing lakehouse shortcuts
7.4 Creating and managing mirroring
7.5 Using pipelines to ingest data
7.6 Ingesting data by using continuous integration from OneLake
7.7 Designing a dimensional model
7.8 Grouping and aggregating data
7.9 Handling duplicate, missing, and late-arriving data
7.10 Quiz

Lesson 8: Ingest and Transform Streaming Data
Learning objectives
8.1 Choosing between Eventstream, Spark Structured Streaming, and KQL for streaming
8.2 Understanding KQL database native storage, followed storage, and OneLake shortcuts
8.3 Using Eventstreams to process data
8.4 Using Spark Structured Streaming to process data
8.5 Using KQL to process data
8.6 Using windowing functions to query streaming data
8.7 Quiz

Lesson 9: Monitor Fabric Items
Learning objectives
9.1 Monitoring data ingestion
9.2 Monitoring data transformation
9.3 Monitoring Power BI semantic model refreshes
9.4 Configuring alerts
9.5 Quiz

Lesson 10: How to Identify and Resolve Errors
Learning objectives
10.1 Troubleshooting and resolving pipeline errors
10.2 Troubleshooting and resolving dataflow errors
10.3 Troubleshooting and resolving notebook errors
10.4 Troubleshooting and resolving Eventhouse and Eventstream errors
10.5 Troubleshooting and resolving T-SQL errors
10.6 Troubleshooting and resolving Shortcut errors
10.7 Quiz

Lesson 11: How to Optimize Performance
Learning objectives
11.1 Optimizing a lakehouse table
11.2 Optimizing a data factory pipeline
11.3 Optimizing a warehouse
11.4 Optimizing Eventstreams and Eventhouses
11.5 Optimizing Spark performance
11.6 Optimizing query performance
11.7 Quiz

Lesson 12: Practical Applications and Case Studies
Learning objectives
12.1 Surveying industry-specific examples (finance, healthcare, retail)
12.2 Exploring success stories using Microsoft Fabric
12.3 Applying lessons learned and best practices
12.4 Exploring guided mini-projects

Summary

Updates

Submit Errata