DP-700 : Implement data engineering solutions using Microsoft Fabric

DP-700 : Implement data engineering solutions using Microsoft Fabric


  Intermediate

Regular Price : $2400.00
Offer Price :$1999.00

Course Overview

This course covers methods and practices to implement data engineering solutions by using Microsoft Fabric. Students will learn how to design and develop effective data loading patterns, data architectures, and orchestration processes. Objectives for this course include ingesting and transforming data and securing, managing, and monitoring data engineering solutions. This course is designed for data professionals with some data integration and orchestration experience.

Course Outline

Learning Path1Explore end-to-end analytics with Microsoft Fabric

  • Describe end-to-end analytics in Microsoft Fabric

  • Understand data teams and roles that use Fabric

  • Describe how to enable and use Fabric

 

Learning Path 2: 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

 

Learning Path 3: Use Apache Spark in Microsoft Fabric

 

  • Describe end-to-end analytics in Microsoft Fabric

  • Understand data teams and roles that use Fabric

  • Describe how to enable and use Fabric

 

Learning Path 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

  • Optimize delta tables

  • Use delta tables with Spark structured streaming

 

Learning Path 5: Ingest data with Dataflow Gen2 in Microsoft Fabric

 

  • Describe Dataflow Gen2 capabilities in Microsoft Fabric

  • Create Dataflow solutions to ingest and transform data

  • Include a Dataflow in a pipeline

     

     

Learning Path 6: Orchestrate processes and data movement with 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.

 

Learning Path 7: Organize a lakehouse with medallion architecture

 

  • Describe the principles of using the medallion architecture in data management.

  • Apply the medallion architecture framework.

  • Analyze data stored in the lakehouse using DirectLake in Power BI.

 

Learning Path 8: Get started with Real-Time Intelligence in Microsoft Fabric

 

  • Understand real-time data analytics concepts

  • Explore core components of Real-Time Intelligence in Microsoft Fabric

 

Learning Path 9: Use Real-Time Eventstreams in Microsoft Fabric

 

  • Establish source and destinations in Microsoft Fabric Eventstreams.

  • Capture, transform, and route data using Microsoft Fabric Eventstreams.

 

Learning Path 10: Work with real-time data in a Microsoft Fabric eventhouse

 

  • Create an eventhouse in Microsoft Fabric

  • Query real-time data by using Kusto Query Language (KQL)

  • Create materialized views and stored functions in a KQL database

 

Learning Path 11: Create real-time dashboards with Microsoft Fabric

 

  • Create a real-time dashboard in Microsoft Fabric.

  • Use advanced feature of real-time dashboards.

  • Apply best practices for real-time dashboards.

 

Learning Path 12: Use Activator in Microsoft Fabric

 

  • Define data objects and properties in Activator

  • Create rules that evaluate conditions in your data.

  • Configure actions that execute when rule conditions are met.

 

Learning Path 13: 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.

 

Learning Path 14: Load data into a Microsoft Fabric data warehouse

 

  • Strategies to load data into a data warehouse in Microsoft Fabric.

  • Build a data pipeline to load a warehouse in Microsoft Fabric.

  • Load data in a warehouse using T-SQL.

  • Load and transform data with dataflow (Gen 2).

 

Learning Path 15: 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.

     

Learning Path 16: Secure a Microsoft Fabric data warehouse

 

  • Learn the concepts of securing a data warehouse in Microsoft Fabric.

  • Implement dynamic data masking, row-level security, and column-level security.

  • Configure granular permissions using T-SQL.

 

Learning Path 17: Implement continuous integration and continuous delivery (CI/CD)

 

  • Define CI/CD and describe how it's implemented in Fabric.

  • Implement version control and Git integration.

  • Use deployment pipelines to automate the deployment process.

  • Automate CI/CD using Fabric APIs.

 

Learning Path 18: Monitor activities in Microsoft Fabric

 

  • Apply monitoring concepts to Microsoft Fabric.

  • Use Monitoring Hub in Microsoft Fabric.

  • Trigger actions using Activator in Microsoft Fabric.

 

Learning Path 19: Secure data access in Microsoft Fabric

 

  • Understand Fabric security model

  • Configure workspace and item permissions

  • Apply granular permissions

     

Learning Path 20: Administer Microsoft Fabric

 

  • Describe Fabric admin tasks

  • Navigate the admin center

  • Manage user access

Course Objectives

By the end of this course, learners will be able to:

 

  • Design and implement data engineering solutions using Microsoft Fabric

  • Develop efficient data ingestion and transformation processes

  • Build and manage data architectures, including lakehouses and data warehouses

  • Implement data orchestration workflows for automated processing

  • Secure, manage, and monitor data engineering solutions

  • Work with real-time analytics and data streaming scenarios

  • Optimize and maintain enterprise-scale data platforms

Pre-requisites

Before taking this course, learners should have:

 

  • Experience with data extraction, transformation, and loading (ETL) processes

  • Knowledge of data integration and orchestration concepts

  • Familiarity with at least one of the following:

    • Structured Query Language (SQL)

    • PySpark

    • Kusto Query Language (KQL)

  • Basic understanding of data architectures and analytics workflows

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This course includes:

  • Official MS Learn Courseware
  • Exam Preps
  • Achievement Badge from Microsoft
  • Course Completion Certificate
  • Post Training Support
  • Experienced & Certified Instructors
  • Train from AnyWhere
  • Interactive Hands-On Labs
  • Personalized Learning Plans
  • Flexible Scheduling
  • Accredited Training
  • Cost-Effective Pricing

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