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    Azure Data Factory | Data Engineering On Azure And Fabric

    Posted By: ELK1nG
    Azure Data Factory | Data Engineering On Azure And Fabric

    Azure Data Factory | Data Engineering On Azure And Fabric
    Last updated 5/2024
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 5.37 GB | Duration: 10h 57m

    Data Factory on Azure and Microsoft Fabric, Azure DevOps (CI/CD), Azure Data Lake, Azure SQL and more (DP-203, DP-600)

    What you'll learn

    Azure Data Factory

    Microsoft Fabric Data Factory

    Azure Storage Accounts

    Azure SQL Databases and Upsert Operations

    Overview and Background of Azure Big Data Analytics Solutions

    Integrating Data From Various File Formats

    Authentication and Access: Service Principals, Managed Identities and Azure Key Vault

    Data Pipelines, Copy Activity, Data Flows, Control Flow and Transformation Activities

    Implementing a Modern Data Lake

    Control Flow, Parameters and Variables

    Real World Application of Azure Data Factory

    Azure DevOps Source Control with Data Factory

    Continuous Integration and Continuous Deployment (CI/CD)

    Requirements

    Microsoft Azure Subscription

    Basic SQL

    Description

    I am pleased to present this course on one of the most in demand data engineering tools around… Azure Data Factory!As the demand for cloud-based data integration services continues to skyrocket, there is a huge demand for professionals with knowledge of services like Azure Data Factory and Microsoft Fabric. By learning these applications, users can enhance their skills and increase their job prospects in the field of data engineering and analytics.In this course you will primarily be using Azure Data Factory on Microsoft Azure and Microsoft Fabric in addition to other services such as Azure Blob Storage, Azure Data Lake Storage Gen 2 and Azure SQL Database.The course is packed with lectures, code-along videos and a dedicated course project. As an added benefit you will also have lifetime access to all of the lectures…This course will cover the following topics:Azure Storage Solutions such as Azure Blob Storage and Azure Data Lake Gen2 StorageThe basics of Azure Data Factory including the core components such as Linked Services, Datasets, Activities, Data Flows, Pipelines and Integration RuntimesIntegrating data from various file formats such as CSV, JSON and ParquetThe Copy Activity in Azure Data FactoryData Flows, Control Flow and Transformation Activities in Azure Data FactoryOrchestrating Data Integration WorkflowsHow to create Schedules and Triggers to execute your pipelinesHow to use Parameters and Variables with your Linked Services, Datasets and PipelinesHow to use Azure Data Factory with SQL DatabasesAuthentication and Access including Managed Identities, Service Principals and Azure Key VaultThe Data Factory Experience in Microsoft FabricAzure DevOps Source ControlContinuous Integration and Continuous Deployment

    Overview

    Section 1: Course Overview and Set Up

    Lecture 1 Course Overview

    Lecture 2 Azure Big Data Analytics

    Lecture 3 Azure Account Set Up

    Lecture 4 Azure UI Overview and Creating Your Subscription

    Lecture 5 Services and Regions

    Lecture 6 Naming Conventions and Creating the Course Resource Group

    Lecture 7 Cost Management and Pricing

    Lecture 8 Links and Resources

    Section 2: Azure Storage

    Lecture 9 Azure Storage Solutions Overview

    Lecture 10 Classification of Data

    Lecture 11 ADF Supported File Formats

    Lecture 12 Customer Orders Dataset Download and Overview

    Lecture 13 Blueprint for the Data Lake

    Lecture 14 Storage Account Creation

    Lecture 15 Container Creation and File Upload

    Lecture 16 Links and Resources

    Section 3: ADF - Overview and Set Up

    Lecture 17 Azure Data Factory Overview

    Lecture 18 ADF Resource Creation

    Lecture 19 ADF User Interface and Components Overview

    Lecture 20 Links and Resources

    Section 4: Data Movement

    Lecture 21 Copy Activity Overview

    Lecture 22 Section Objective

    Lecture 23 Copy Activity - Order Items Part 1 (Linked Services and Source/Sink Datasets)

    Lecture 24 Copy Activity - Order Items Part 2 (Copy Activity and Pipeline)

    Lecture 25 Copy Activity - Customers JSON

    Lecture 26 Copy Activity - Orders Parquet

    Lecture 27 Copy Activity - Stores and Products JSON

    Lecture 28 Organising Data Factory Objects in Folders

    Lecture 29 Chaining Activities

    Lecture 30 Working with Multiple Files as a Source and Copy Behaviour

    Lecture 31 Links and Resources

    Section 5: Data Transformation

    Lecture 32 Data Flow Overview

    Lecture 33 Section Objective

    Lecture 34 Data Flow Demo and User Interface

    Lecture 35 Data Flow Debug (Pricing)

    Lecture 36 Data Flow Debug (Row Limit)

    Lecture 37 Source and Sink Partitioning

    Lecture 38 Select Transformation

    Lecture 39 Cast Transformation

    Lecture 40 Import Projection

    Lecture 41 Derived Column Transformation

    Lecture 42 Raw to Cleansed Data Flow Requirement

    Lecture 43 Raw to Cleansed Data Flow Walkthrough

    Lecture 44 Partitioned Datasets

    Lecture 45 Filter and Sort Transformations

    Lecture 46 Aggregate Transformation

    Lecture 47 Join Transformation

    Lecture 48 Conditional Split and Union Transformations

    Lecture 49 Cleansed to Structured Data Flow Requirement

    Lecture 50 Cleansed to Structured Data Flow Walkthrough

    Lecture 51 Structured to Analytics Data Flow Requirement

    Lecture 52 Structured to Analytics Data Flow Walkthrough

    Lecture 53 Links and Resources

    Section 6: Scheduling and Chaining Pipelines for Execution

    Lecture 54 Execute Pipeline Activity

    Lecture 55 Data Flow Debug Session vs Activity Runtime

    Lecture 56 Schedule Triggers

    Lecture 57 Storage Events Trigger

    Lecture 58 Tumbling Window Triggers

    Lecture 59 Links and Resources

    Section 7: Control Flow, Parameters and Variables

    Lecture 60 Data Lake Preparation

    Lecture 61 Delete Activity

    Lecture 62 Get Metadata Activity

    Lecture 63 Lookup Activity

    Lecture 64 If Condition and Switch Activities

    Lecture 65 Introduction to Parameters in ADF

    Lecture 66 Introduction to Variables in ADF

    Lecture 67 ForEach Activity

    Lecture 68 Metadata Based Approach (Example)

    Lecture 69 Course Project Activity: Dynamic Datasets and Folder Structure

    Lecture 70 Links and Resources

    Section 8: Azure SQL Database for Structured Data

    Lecture 71 Section Overview

    Lecture 72 Creating our Azure SQL Resource

    Lecture 73 Creating our Schema and Tables

    Lecture 74 Adding a Unique Identifier Column to Structured Orders Data

    Lecture 75 Creating Linked Service and Datasets for SQL

    Lecture 76 Important Note Regarding the Dynamic Datasets

    Lecture 77 Copy Activity with SQL Sink

    Lecture 78 Data Flow with SQL Sink

    Lecture 79 Course Project Activity: Storing our Structured Data in the SQL Database

    Lecture 80 Links and Resources

    Section 9: Tumbling Window Triggers

    Lecture 81 Tumbling Window Triggers Overview

    Lecture 82 Ingest Data for Specific Time Intervals

    Lecture 83 Tumbling Window Trigger Dependencies

    Lecture 84 Links and Resources

    Section 10: Authentication and Access

    Lecture 85 Overview

    Lecture 86 Azure Key Vault

    Lecture 87 System Assigned Managed Identities

    Lecture 88 User Assigned Managed Identities

    Lecture 89 Service Principals

    Lecture 90 Fine Grained Access Control for Storage Accounts

    Lecture 91 Links and Resources

    Section 11: Data Factory in Microsoft Fabric

    Lecture 92 Section Overview

    Lecture 93 What is Microsoft Fabric?

    Lecture 94 What is OneLake?

    Lecture 95 What are Fabric Capacities?

    Lecture 96 Set Up a Fabric Trial with a Business Email

    Lecture 97 Set Up a Fabric Trial with a Personal Email

    Lecture 98 Overview of Fabric UI and Project Workspace Creation

    Lecture 99 Data Pipeline and Dataflow Gen2 in Data Factory

    Lecture 100 Demo 1: Copy Data Between External ADLS Containers

    Lecture 101 Overview of Data Lakehouses in Microsoft Fabric

    Lecture 102 Demo 2: Copy Data from Azure to Fabric

    Lecture 103 Demo 3: Dataflow Gen2

    Lecture 104 (Optional) Semantic Modelling and Power BI Reporting

    Lecture 105 Section Debrief

    Lecture 106 Links and Resources

    Section 12: CI/CD: Source Control with Azure DevOps Git

    Lecture 107 Section Overview

    Lecture 108 Overview of CI/CD in Azure Data Factory

    Lecture 109 Azure Resource Set Up

    Lecture 110 Azure DevOps Overview and Set Up

    Lecture 111 Azure DevOps Parallelism Request

    Lecture 112 Initialising and Configuring Git Repo in ADF

    Lecture 113 ADF Git Workflow Demo 1: Feature Development

    Lecture 114 ADF Git Workflow Demo 2: Merge Conflicts

    Lecture 115 Section Debrief

    Lecture 116 Links and Resources

    Section 13: CI/CD: Automated Build and Deploying to Different Environments

    Lecture 117 Section Overview

    Lecture 118 Reminder to Complete Parallelism Request

    Lecture 119 Automated Build Pipeline

    Lecture 120 Enabling Release Pipelines in Azure DevOps

    Lecture 121 Release Pipeline: Deploy to Dev

    Lecture 122 ARM Template Deployment Limitations: Overview

    Lecture 123 ARM Template Deployment Limitations: Adding Pre and Post Deployment Tasks

    Lecture 124 Test and Prod Set Up in Azure

    Lecture 125 Release Pipeline: Deploy to Test

    Lecture 126 Using Pipeline Variables

    Lecture 127 Release Pipeline: Deploy to Production

    Lecture 128 End to End Walkthrough PART 1: CI/CD with ADF and Azure Data Lake Gen2

    Lecture 129 End to End Walkthrough PART 2: CI/CD with ADF and Azure Data Lake Gen2

    Lecture 130 Deleting Build and Release Pipelines

    Lecture 131 Links and Resources

    Section 14: Continue learning with me!

    Lecture 132 BONUS: Check out my other courses

    Anyone interested in Azure, Microsoft Fabric or Cloud Data Engineering,Anyone interested in working with Big Data,Anyone working with cloud data