Dp-600: Fabric Analytics Engineer Associate

Prepare yourself for the Microsoft Certified: Fabric Analytics Engineer Associate exam

What you'll learn

Students will learn how to build warehouse and lakehouses on Microsoft Fabric.

Students will learn on how to build semantic models in Power BI Desktop.

Students will learn SQL aspects and how to ingest data into Microsoft Fabric.

Students will also learn about several security aspects around Microsoft Fabric.

Requirements

No prior knowledge on Microsoft Fabric is required, students will learn about Microsoft Fabric.

No prior knowledge is required for Power BI Desktop, we will learn about Power BI Desktop in this course.

Description

"This course requires you to download Power BI Desktop on your local machine. If you are a Udemy Business user, please check with your employer before downloading software."This intensive, comprehensive course is designed to prepare data professionals for the Microsoft DP-600 certification exam, focusing on data warehouse implementation and optimization using Microsoft Fabric. Participants will gain the knowledge and practical skills necessary to design, implement, and manage semantic models, data warehouses , lakehouses that leverage the full power of Microsoft's modern data analytics platform.What are we going to learnBasics around data and getting the required tools in place for the course.Getting and transforming data within Power BI Desktop.Building various aspects in Power BI Desktop such reports, visualizations, Measures etc.How to use Power Query in Power BI Desktop to transform data. We will see an example on how build a semantic model around Fact and Dimension tables.Getting started with Microsoft Fabric. How we get trial capacity to start working with the service.How we can build data warehouses in Microsoft Fabric. Using T-SQL, data pipelines and Data Flow Gen2 we can see how to ingest data into Microsoft Fabric.How to run basic T-SQL commands against our data warehouse.How to build Lakehouses on Microsoft Fabric.How we can ingest data into Lakehouses using data pipelines and Data Flow Gen2.How to use Apache Spark in Microsoft Fabric to work with data sets.

Overview

Section 1: Introduction

Lecture 1 Data in today's world

Lecture 2 Tools and services

Lecture 3 Microsoft Power BI

Lecture 4 Lab - Installing Power BI Desktop

Lecture 5 Tour of Power BI Desktop

Lecture 6 Our data sets

Lecture 7 Setting up an Azure Free Tier Account

Lecture 8 About the Azure SQL database service

Lecture 9 Creating an Azure SQL database server

Lecture 10 Setting up the Azure SQL database

Section 2: Implement and manage semantic models - Power BI Desktop

Lecture 11 Section Resources

Lecture 12 Lab - Power BI Desktop - Get Data

Lecture 13 Lab - Power BI Desktop - Removing columns

Lecture 14 Lab - Power BI Desktop - Handing missing values

Lecture 15 Lab - Power BI Desktop - Adding new columns

Lecture 16 Online Analytical Processing Systems

Lecture 17 What goes into building a data warehouse

Lecture 18 Power BI Desktop - Merge Queries

Lecture 19 Lab - Power BI Desktop - Merge Queries - DimProduct

Lecture 20 Lab - Power BI Desktop - Merge Queries - DimCustomer

Lecture 21 Lab - Power BI Desktop - Merge Queries - FactSales

Lecture 22 Different views in Power BI Desktop

Lecture 23 What are we going to do next

Lecture 24 Lab - Power BI Desktop - Building simple visualizations

Lecture 25 Lab - Power BI Desktop - Hierarchies

Lecture 26 Power BI Desktop - Quick look at drill down

Lecture 27 Overview on DAX

Lecture 28 Lab - DAX Expressions - Creating a measure

Lecture 29 Lab - DAX Expressions - Aggregation Functions

Lecture 30 Lab - DAX Expressions - Filter Functions

Lecture 31 Lab - DAX Expressions - Information Functions

Lecture 32 Lab - DAX Expressions - Using Quick measures

Lecture 33 Lab - Power BI Desktop - Building the Date Dimension table

Lecture 34 Lab - Power BI Desktop - Using calculation groups

Lecture 35 Lab - Power BI Desktop - Using Field parameters

Lecture 36 Semantic Model - Storage Design

Lecture 37 Lab - Using the Direct Query mode

Lecture 38 Power BI Desktop - ColumnProfiling tools

Lecture 39 Quick note on other performance considerations in Power BI Desktop

Lecture 40 Lab - Power BI Desktop - Performance Analyzer

Section 3: Prepare Data - Microsoft Fabric - Data Ingestion and Data warehouse

Lecture 41 Section Code

Lecture 42 What is Microsoft Fabric

Lecture 43 Microsoft Fabric terms

Lecture 44 Note on Microsoft Fabric Licensing

Lecture 45 On-boarding ourselves onto Microsoft Fabric

Lecture 46 Getting Microsoft Fabric Trial capacity

Lecture 47 Lab - Publishing a report from Power BI Desktop to Fabric

Lecture 48 Review of the data warehousing fundamentals

Lecture 49 Lab - Microsoft Fabric - Creating a sample data warehouse

Lecture 50 Microsoft Fabric - Ingesting data

Lecture 51 Lab - Creating an Azure Storage Account

Lecture 52 Lab - Microsoft Fabric data warehouse - Ingesting data - COPY command

Lecture 53 Lab - Microsoft Fabric data warehouse - Ingesting data - COPY command - SAS

Lecture 54 Lab - Microsoft Fabric data warehouse - Visual Query

Lecture 55 Lab - Microsoft Fabric data warehouse - Cloning tables

Lecture 56 Lab - Microsoft Fabric warehouse - CREATE TABLE AS SELECT

Lecture 57 Lab - Microsoft Fabric - Ingesting data - Data Pipeline

Lecture 58 Lab - Microsoft Fabric - Ingesting data - Data Flow Gen2

Lecture 59 Lab - Microsoft Fabric data warehouse - Building our Fact table

Lecture 60 Lab - Microsoft Fabric data warehouse - Building our Dimension tables

Lecture 61 Lab - Microsoft Fabric data warehouse - Building the Dimension Date table

Lecture 62 Lab - Microsoft Fabric data warehouse - Default semantic model

Lecture 63 Lab - Microsoft Fabric data warehouse - Column profiling

Lecture 64 Data warehouse - Slowly Changing Dimensions

Lecture 65 Microsoft Fabric - Power Query - Query folding

Lecture 66 Lab - Microsoft Fabric data warehouse - SQL - Basics

Lecture 67 Lab - Microsoft Fabric data warehouse - SQL - Group by

Lecture 68 Lab - Microsoft Fabric data warehouse - SQL - Finding duplicate values

Lecture 69 Lab - Microsoft Fabric data warehouse - SQL- JOIN

Lecture 70 Lab - Microsoft Fabric data warehouse - SQL user-defined function

Section 4: Prepare Data - Microsoft Fabric - Lakehouse

Lecture 71 Section Code

Lecture 72 What is a Lakehouse

Lecture 73 Lab - Microsoft Fabric Lakehouse - Creating the Lakehouse

Lecture 74 Lab - Microsoft Fabric Lakehouse - Ingesting data via files

Lecture 75 Lab - Microsoft Fabric Lakehouse - Ingesting data via parquet files

Lecture 76 Microsoft Fabric Lakehouse - Delta Lake

Lecture 77 Lab - Microsoft Fabric Lakehouse - Ingesting data - Data pipeline

Lecture 78 Lab - Microsoft Fabric Lakehouse - Ingesting data - Data Flow Gen2

Lecture 79 Lab - Microsoft Fabric Lakehouse - Shortcuts - Azure Data Lake

Lecture 80 Lab - Microsoft Fabric Lakehouse - Shortcuts - AWS S3

Lecture 81 About using Apache Spark on Microsoft Fabric

Lecture 82 Lab - Microsoft Fabric Apache Spark - Creating a notebook

Lecture 83 Lab - Microsoft Fabric Apache Spark - Loading data into a Dataframe

Lecture 84 Lab - Microsoft Fabric Apache Spark - Performing operations on the data frame

Lecture 85 Lab - Microsoft Fabric Apache Spark - Saving the data frame

Lecture 86 Lab - Microsoft Fabric Apache Spark - Further working with data

Lecture 87 Microsoft Fabric Apache Spark - Running SQL commands in the notebook

Section 5: Practice Tests

This course is for students who want to prepare and take the DP-600 exam.,This course is for students who want to learn to use the Microsoft Fabric service.,This course will also help students learn to build semantic models in Power BI desktop.