Microsoft Fabric Complete Guide - Future Of Data With Fabric

Microsoft Fabric Masterclass | Data Factory, Warehouse, Data Science, Power BI all in Fabric | Learn Microsoft Fabric

What you'll learn

Understand Microsoft Fabric, what it is, how it works and its various components

Learn how to explore, create, assign and configure workspaces

Discover the principles of data engineering in Microsoft Fabric, including Lakehouse and Delta Lake

Explore the application of OneLake on Fabric

Learn about data loading and ingestion options in the Fabric enviroment

Understand the connection of Power BI and Fabric in data visualization

Discover how to use data factory to create and execute data pipelines in Fabric

Find out the uses of Fabric within the domains of data science and data engineering

Gain comprehensive understanding of performance management, SQL, and KQL, and learn how to use KSQL Queryset, KSQL database, and KSQLMagic for data analysis.

Explore how to manage access control, governance, and monitoring in the Fabric environment

Requirements

Basic understanding and knowledge of data concepts and terminologies.

Programming experience and familiarity with data engineering and data science languages such as SQL, Spark and Python is not required, but recommend

Simple understanding of cloud computing concepts and services, particularly for Microsoft Azure.

Description

Are you ready to immerse yourself in the cutting-edge world of Microsoft Fabric and revolutionize your data-professional and data engineering skills? This in-depth course will take you on an exploration of the power of Microsoft Fabric, Microsoft's cutting-edge data tools and analytics platform. With over 9 hours of engaging content, you will obtain a solid grasp of Microsoft Fabric's capabilities and how it can assist you with your data journey.In this comprehensive Microsoft Fabric course, you will learn about important topics like Lakehouse versus warehouse and the concept of workspaces. Learn how to set up and configure workspace access, how to use OneLake and Delta Lake, and how to apply authentication and authorization techniques for data protection. Improve your knowledge of shortcuts, monitoring hubs, and data hubs. Learn about Spark integration, different ingest strategies for efficient data loading, and key topics like SQL vs. KQL and performance management.Whether you're a seasoned data expert or just starting out, this course will prepare you to thrive in the world of data.What is this course all about?The main goal of this course is to offer a comprehensive guide to Microsoft Fabric and showcase its diverse applications across multiple domains in the data field. By delving into data engineering, data science, and data analytics, you will develop a holistic comprehension of how Microsoft Fabric can be effectively utilized in the world of data. What is Microsoft Fabric?Microsoft Fabric is an all-in-one cloud-based analytics platform that includes data migration, data lakes, data engineering, data integration, data science, real-time analytics, and business intelligence. It offers a user-friendly, cloud SaaS interface that makes it accessible even to people with limited data analytics knowledge. All analytics components are available on a single platform, easing data pipeline management, model deployment, and insight sharing.Who are the instructors for this course?Sawyer NyquistA data professional from West Michigan, USA, holding the position of Sr. Data Engineering Consultant at Microsoft. He specializes in business intelligence, data engineering, data warehousing, and data platform architecture. Possessing cloud data certifications in data engineering, Apache Spark, and business intelligence, he has collaborated with numerous companies to strategize and deploy data platforms, analytics, and technology and to foster growth.Hitesh GovindHitesh is a cloud solutions architect from Southern California, USA, with a wide expertise in database administration and enterprise architecture. He is also a published author who is passionate about mentoring teenagers and an entrepreneur who believes in the power of technology to tackle real-world business challenges. Hitesh also has expert-level certification in Azure Solutions Architecture, as well as Data Engineer and Power Platform certifications.Why learn Microsoft Fabric?Role-Tailored Tools: Microsoft Fabric provides specialized tools for different roles involved in the data analytics process, catering to the demands of data professionals, analysts, and engineers.Unified Platform: Microsoft Fabric unifies diverse components of an analytics solution into a single platform.Cloud-Based Accessibility: As a cloud-based platform, Fabric allows users to access it from anywhere, making it ideal for organizations with distributed teams or those requiring quick scalability for their analytics capabilities.AI-Powered Capabilities: Microsoft Fabric incorporates features like Copilot, which aids in efficient code writing, and Data Activator, which provides real-time data monitoring, enhancing data analysis and decision-making.Adaptation to Current Trends and Upskilling: Embracing Microsoft Fabric allows individuals and organizations to stay current with emerging trends in data analytics, providing opportunities for continuous learning and skill enhancement to remain competitive in the data realm.Why choose this course?Learn from Experts: The course is taught by industry experts who have extensive knowledge and experience in the fields of data science, data analytics, and data engineering.Comprehensive Coverage: This course provides a complete guide covering the areas of data engineering, data analytics, and data science, making it a useful and well-rounded resource for data professionals.Practical Approach: Going beyond theoretical explanations, we provide practical examples, allowing students to practice alongside the instructor, which allows the learners to apply the concepts in real-world scenarios, enhancing their learning experience.Instructor support: Whether you're stuck on a particular subject, seeking clarification, or looking for expert insights, our instructors are committed to helping you every step of the way.Course Overview:Sections 1: Learn about the course objectives, the instructors, and how to align analytical solutions with the needs of the clients.Section 2: An introduction to Microsoft Fabric along with workspace set-up and configuration.Sections 3 to 5: An overview of data engineering in Fabric, covering OneLake, Delta Lake, shortcuts, authentication process, and monitoring Spark jobs.Section 6: Introduction to data warehouse in Fabric covering data ingestion, data loading, performance management, etc.Section 7: Learn real-time analytics including SQL and KQL, monitoring queries and data, KSQLmagic along with Spark integration.Sections 8 and 9: Understanding data factory, data flows, pipelines and workspace set-up.Section 10: Exploring the integration of Power BI with Fabric.Section 11: Introduction to data science process, model management and practical exercises.Section 12: Covers the fundamentals of data management including access control, governance and security, and monitoring.

Overview

Section 1: Introduction

Lecture 1 Instructors Introduction

Lecture 2 Learning objectives

Lecture 3 Understanding the Objectives

Lecture 4 Success Criteria

Lecture 5 Introduction

Lecture 6 Course Roadmap

Section 2: Microsoft Fabric Foundation

Lecture 7 Overview of Microsoft Fabric

Lecture 8 Lakehouse vs Warehouse

Lecture 9 Fabric License Types

Lecture 10 Getting-Started: Sign-up Screen

Lecture 11 Concept of Workspaces

Lecture 12 Create and Configure Workspace Access

Lecture 13 Workspace Settings

Section 3: Data Engineering - OneLake

Lecture 14 Introduction to Data Engineering in Fabric

Lecture 15 Introduction to OneLake

Lecture 16 Lakehouse

Lecture 17 Delta Lake

Lecture 18 OneLake Explorer

Lecture 19 Authentication and Authorization

Lecture 20 Introduction to Shortcuts

Lecture 21 Creating Shortcuts

Lecture 22 Monitoring Hub and Data Hub

Section 4: Data Engineering - Lakehouse

Lecture 23 Introduction

Lecture 24 Architecture

Lecture 25 Distinctions between Lakehouse & Warehouse

Section 5: Data Engineering - ETL with Lakehouse

Lecture 26 What is Spark?

Lecture 27 Notebook Overview

Lecture 28 Web based and VS Code Notebooks

Lecture 29 Spark + Monitoring Spark Jobs

Section 6: Data Warehouse - Serverless Engine

Lecture 30 Warehouse-SQL Scripts

Lecture 31 Introduction

Lecture 32 Default Dataset and Modelling

Lecture 33 Ingest Methods

Lecture 34 Load Data Introduction

Lecture 35 Load Data into Lakehouse

Lecture 36 Load Data Using Data Pipeline Part 1

Lecture 37 Load Data Using Dataflows

Lecture 38 Load Data Using Data Pipeline Part 2

Lecture 39 Models and Power BI reports

Lecture 40 Cross-database Query

Lecture 41 Roles + Permissions (RLS, CLS)

Lecture 42 Manage Performance

Lecture 43 Warehouse-SQL Scripts

Section 7: Real-Time Analytics

Lecture 44 Real-Time Analytics - KQL Scripts

Lecture 45 SQL vs KQL Introduction

Lecture 46 Create, Process and Monitor

Lecture 47 KSQL Queryset

Lecture 48 KSQL Database

Lecture 49 KSQLmagic

Lecture 50 Spark

Lecture 51 Real-Time Analytics - KQL Scripts

Section 8: Data Factory Introduction

Lecture 52 What is Data Factory?

Lecture 53 Data Flows and Pipelines

Lecture 54 Architecture

Lecture 55 Workspace Setup

Section 9: Data Factory End-to-End Build

Lecture 56 Control Table and Copy Data

Lecture 57 Metadata Copy Pattern

Lecture 58 Script Activity

Lecture 59 Data Flows Gen2 Introduction

Lecture 60 Data Flows Gen2 Continuation

Lecture 61 Execute Pipeline

Lecture 62 Shortcut to Other Workspaces

Lecture 63 Notebooks

Lecture 64 Data Flow Gen2 Transformations

Lecture 65 Pipelines, Notebooks and Parameters

Lecture 66 Monitoring Notebooks in Pipelines

Section 10: Data Visualization with Power BI

Lecture 67 Power BI and Fabric

Lecture 68 Version Control

Lecture 69 Direct Lake

Section 11: Data Science

Lecture 70 Data Science - Resources

Lecture 71 What is Data Science?

Lecture 72 The Data Science Process

Lecture 73 Items and Models

Lecture 74 Excercise

Lecture 75 Saving Models

Lecture 76 Model Management

Lecture 77 Data Science - Resources

Section 12: Data Management

Lecture 78 Introduction

Lecture 79 Access Control

Lecture 80 Governance

Lecture 81 Monitoring

Citizen and professional data practioners,Data professionals working with data infrastructure, ETL, and data integration, who are seeking to learn how they can utilize Fabric in their data workflows,Business analysts who want to expand their knowledge and skills by learning about data engineering practices, data lakes, and the integration with Power BI for reports and visualization,Data scientists who are looking at how they can leverage Fabric in their data science projects, including model management and integration with Azure services