Training: Microsoft Fabric Analytics Engineer (DP-600)

Ref. DP-600
Duration:
4
 days
Exam:
Optional
Level:
Advanced

Microsoft Fabric Analytics Engineer (DP-600)

This course: Microsoft Fabric Analytics Engineer (DP-600), covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric.

Participants in the Microsoft Fabric Analytics Engineer (DP-600) training will develop their analytics experience and learn to use Microsoft Fabric components, including lakehouses, data warehouses, notebooks, data streams, data pipelines, and semantic models, to create and deploy analytics assets.

Participant profiles

  • Solution Architects
  • Data Engineers
  • Data Scientists
  • AI Engineers
  • Database Administrators
  • Power BI Data Analysts

Objectives

  • Mastering Data Ingestion with Microsoft Fabric
  • Leveraging Data Pipelines and Lakehouses
  • Implementing Advanced Data Architectures
  • Developing Skills in Apache Spark
  • Working with Delta Lake and Data Warehouses
  • Optimizing Data Analysis with Power BI

Prerequisites

  • Understanding data concepts through Microsoft Azure AI Fundamentals (AI-900).
  • Experience in Power BI for data modeling and advanced analytics (PL-300).

Course content

Module 1: Data Ingestion with Dataflows Gen2 in Microsoft Fabric

  • Understanding Dataflows (Gen2) in Microsoft Fabric
  • Exploring Dataflows (Gen2) in Microsoft Fabric
  • Integrating Dataflows (Gen2) and Pipelines in Microsoft Fabric

Module 2: Data Ingestion with Spark and Microsoft Fabric Notebooks

  • Connecting to Data with Spark
  • Writing Data into a Lakehouse
  • Considering the Uses of Ingested Data

Module 3: Using Data Factory Pipelines in Microsoft Fabric

  • Understanding Pipelines
  • Using the Copy Data Activity
  • Using Pipeline Templates
  • Executing and Monitoring Pipelines

Module 4: Getting Started with Lakehouses in Microsoft Fabric

  • Exploring the Microsoft Fabric Lakehouse
  • Working with Microsoft Fabric Lakehouses
  • Exploring and Transforming Data in a Lakehouse

Module 5: Organizing a Fabric Lakehouse using Medal Architecture Design

  • Describing Medal Architecture
  • Implementing Medal Architecture in Fabric
  • Querying and Reporting on Data in Your Fabric Lakehouse
  • Considerations for Managing Your Lakehouse

Module 6: Using Apache Spark in Microsoft Fabric

  • Preparing to Use Apache Spark
  • Running Spark Code
  • Working with Data in a Spark DataFrame
  • Working with Data Using Spark SQL
  • Visualizing Data in a Spark Notebook

Module 7: Working with Delta Lake Tables in Microsoft Fabric

  • Understanding Delta Lake
  • Creating Delta Tables
  • Working with Delta Tables in Spark
  • Using Delta Tables with Streaming Data

Module 8: Getting Started with Data Warehouses in Microsoft Fabric

  • Understanding the Fundamentals of Data Warehouses
  • Understanding Data Warehouses in Fabric
  • Querying and Transforming Data
  • Preparing Data for Analysis and Reporting
  • Securing and Monitoring Your Data Warehouse

Module 9: Loading Data into a Microsoft Fabric Data Warehouse

  • Exploring Data Loading Strategies
  • Using Data Pipelines to Load a Warehouse
  • Loading Data Using T-SQL
  • Loading and Transforming Data with Dataflow Gen2

Module 10: Querying a Data Warehouse in Microsoft Fabric

  • Using the SQL Query Editor
  • Exploring the Visual Query Editor
  • Using Client Tools to Query a Warehouse

Module 11: Monitoring a Microsoft Fabric Data Warehouse

  • Monitoring Capacity Metrics
  • Monitoring Current Activity
  • Monitoring Queries

Module 12: Understanding Scalability in Power BI

  • Describing the Importance of Scalable Models
  • Implementing Best Data Modeling Practices in Power BI
  • Configuring Large Datasets

Module 13: Creating Model Relationships in Power BI

  • Understanding Model Relationships
  • Configuring Relationships
  • Using DAX Relationship Functions
  • Understanding Relationship Evaluation

Module 14: Using Tools to Optimize Power BI Performance

  • Using the Performance Analyzer
  • Diagnosing DAX Performance Issues Using DAX Studio
  • Optimizing a Data Model Using Best Practice Analyzer

Module 15: Applying Security to Power BI Models

  • Restricting Access to Power BI Model Data
  • Restricting Access to Power BI Model Objects
  • Applying Good Data Modeling Practices

Documentation

  • Digital course material included

Complementary courses

Temptraining funding

ITTA is a partner of Temptraining, the continuing education fund for temporary workers. This training fund can subsidize continuing education for anyone who works for an employer subject to the Collective Work Agreement (CCT) Rental of services.

Additional information

Mastering Dataflows and Pipelines with Microsoft Fabric Analytics Engineer Course (DP-600)

In the constantly evolving world of data science, standing out as a Data Analytics Engineer requires mastering the most advanced tools. Microsoft Fabric, with its second-generation (Gen2) Dataflows and integrated pipelines, provides a robust platform for manipulating and ingesting data on a large scale. Understanding and leveraging these Dataflows enables you to effectively orchestrate complex data streams, ensuring smooth integration within your analytics pipelines.

Leveraging Spark and Notebooks in Microsoft Fabric

Data ingestion is reimagined with Spark and notebooks within Microsoft Fabric. The ability to easily connect to data, write into a lakehouse, and consider practical applications opens new horizons for data analytics engineers. This approach allows for unmatched flexibility and analytical power, propelling the capabilities of data exploration and transformation.

The Art of Data Factory Pipelines

Creating and managing efficient pipelines with Data Factory under Microsoft Fabric transcends mere technical understanding. The judicious use of the Copy Data activity, pipeline templates, and the ability to execute and monitor these pipelines transform raw data into valuable insights, crucial for any strategic decision-making.

The Era of Lakehouses

The concept of a Lakehouse, a significant innovation in the Microsoft Fabric ecosystem, revolutionizes the way data is stored, accessed, and analyzed. By exploring and transforming data within a Lakehouse, engineers unlock advanced analytical perspectives, going well beyond the traditional capabilities of data lakes and data warehouses.

Architecting with Medal

Adopting the medal architecture in managing Microsoft Fabric’s Lakehouses is not just about structure, but a comprehensive strategy to optimize access, security, and the efficiency of data analysis. This methodology enables sophisticated querying and reporting, essential for navigating the modern data ocean.

Power of Apache Spark

Mastering Apache Spark within Microsoft Fabric equips data analytics engineers with a powerful arsenal for data processing. Whether it’s running Spark code, manipulating dataframes, or using Spark SQL, the possibilities are vast and allow for high-level data analysis and visualization.

Delta Lake: A Revolution in Data Management

Delta Lake introduces a transactional management layer to your data lakes, enabling more efficient and secure creation and manipulation of delta tables. This innovation opens the door to real-time analytics and data streaming applications, essential for today’s dynamic data environments.

By embracing these key skills and technologies within Microsoft Fabric, data analytics engineers position themselves at the forefront of data science innovation, ready to tackle the challenges of tomorrow and transform data into strategic decisions.

Prix de l'inscription
CHF 3'400.-
Inclus dans ce cours
  • Training provided by a certified trainer
  • 180 days of access to Official Microsoft Labs
  • Official documentation in digital format
  • Official Microsoft achievement badge

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