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Azure Advanced: Data Factory| PAAS| App Services & Migration
Rating: 5.0 out of 5(1 rating)
1,053 students

Azure Advanced: Data Factory| PAAS| App Services & Migration

Master advanced Azure skills and elevate your cloud expertise with Data Factory, PAAS, App Services, and Migration!
Last updated 6/2024
English
English [Auto],

What you'll learn

  • Comprehensive understanding of Microsoft Azure Data Factory
  • Setting up and configuring Azure Data Factory
  • Creating and managing pipelines in Azure Data Factory
  • Handling errors and debugging issues in Azure Data Factory
  • Understanding and utilizing Azure PAAS services
  • Deploying and managing Azure App Services
  • Implementing Azure CDN and handling CORS
  • Creating and managing Azure SQL databases and storage
  • Migrating web applications and databases to Azure
  • Developing and deploying applications on Azure, including serverless computing and web jobs

Course content

6 sections148 lectures19h 0m total length
  • Introduction to Azure Data Factory7:25

    Explore the architecture of Azure Data Factory, linking data sources via linked services, and orchestrate pipelines of activities with datasets to enable data movement and migration.

  • Creating Copy Visa8:07

    Create a copy wizard in Azure Data Factory to move data from Azure Blob Storage to Azure SQL Database, set up source and destination, and configure a new ADF instance.

  • Creating Free Subscription6:51

    Create a free subscription and a resource group, learn automation templates to deploy Azure resources across environments, and explore storage accounts with blobs, files, queues, and tables using Storage Explorer.

  • Creating a Blob6:18

    Create a blob in an Azure storage account, review read access geo redundant storage and endpoints, and manage access with keys, connection strings, and container types.

  • Properties of Blog6:21

    Explore blob storage properties in Azure, comparing block, append, and page blobs, including size limits and use cases. Learn how entity tags enable efficient caching between client and server.

  • Creating Destination11:25

    Create a destination by provisioning an Azure SQL database and SQL server, configure server and database settings for ADF, set firewall rules, and verify connectivity from SQL Management Studio.

  • Creating ADF in Microsoft Azure5:49

    Create an Azure Data Factory instance and upload a sample csv file to blob storage using Storage Explorer, then review deployment status and basic portal steps.

  • Create Schema in SQL Azure4:57

    Create a sql azure schema that mirrors a csv file by using a create table statement or the online query editor, then deploy via Visual Studio with sql server authentication.

  • Create Copy Activity in ADF10:17

    Learn to create a copy activity in Azure Data Factory to copy data from blob storage to SQL, configuring source and destination, linked services, and schema preview.

  • Create Copy Activity in ADF Continues9:27

    Configure a copy activity in Azure Data Factory to map blob data to a SQL table, with schema and column mapping, upsert via repeatability, fault tolerant settings, and performance options.

  • ADF Resource Explorer in Microsoft Azure5:25

    Explore the Azure Data Factory resource explorer, review pipelines and copy activities from blob storage to Azure SQL database, and configure linked services, gateways, alerts, and monitoring dashboards.

  • Creating Author Deploy Environment7:56

    Author and deploy Azure Data Factory pipelines to move data from blob storage to Azure SQL Database by creating datasets and linked services, with manual and scheduled runs.

  • Create Pipeline and DataSets in Microsoft Azure6:22

    Create a blob storage and Azure SQL data sets linked to their services. Build a copy pipeline that uses source and destination datasets to move data.

  • Fix Errors in the Pipeline4:41

    Fix a failed Azure Data Factory pipeline by inspecting the copy activity error, correcting the source dataset and blob container path, and redeploying until the pipeline succeeds.

  • Scheduling the data3:26

    Explore scheduling daily data ingestion with Azure Data Factory, handling year-month-day folders, and setting up linked services, datasets, and pipelines for repeatable ETL through copy activities.

  • Setup for ADF Scheduling7:04

    Demonstrates setting up a daily ADF scheduling pipeline to monitor a groceries data feed in blob storage and load new CSV files into a SQL database.

  • Uploading the Files6:59

    Upload groceries data to Azure blob via a linked service, then set up source and destination data sets in Azure Data Factory and validate the upload in the portal.

  • Creating the Pipeline7:07

    Create and deploy a groceries pipeline in azure data factory, using a copy activity from blob storage to a SQL sink with column mappings, datasets, and a scheduled window.

  • Monitoring the Pipeline10:52

    Monitor and manage Azure Data Factory pipelines by inspecting consumed and produced datasets, slices, and validation states to ensure data readiness and troubleshoot missing blobs.

  • Understanding the Scheduling6:27

    Explore the architecture of scheduling in Azure Data Factory, including slices, data sets, and blob storage, and how execution policies like oldest first or newest first govern retrospective processing.

  • How Pipeline Works6:34

    See how Azure Data Factory pipelines use partition by and the slice start runtime to build dynamic folder paths from year, month, and day.

  • Analyzing the Pipeline4:09

    Explore how to analyze an Azure Data Factory pipeline using the diagram view to visualize activities, inputs, and outputs, and learn to use show lineage for focused data flow.

  • Monitor and Manage Tool6:33

    Explore the monitor and manage tool in Azure Data Factory, view activity windows and pipelines, and use the resource explorer to highlight datasets and linked services.

  • Failed Data Slices5:06

    Learn to recover a failed data slice by manually uploading the relevant files to Azure blob storage, triggering a re-run, and verifying results via SQL counts, addressing browser cache issues.

  • Debugging Issues in a Pipeline2:32

    Pause the Azure Data Factory pipeline to stop execution, inspect the underlying data source or storage, fix the issue, then resume the pipeline using the monitoring and manage tools.

  • ADF Pipeline Setup4:22

    Create a production ADF pipeline that transforms and aggregates blob data with Azure Data Lake Analytics using U-SQL, storing results in Azure Data Lake Store and loading into SQL Azure.

  • Azure Data Lake Store3:29

    Create and configure Azure Data Lake Analytics and Data Lake Store. Use U-sql, Yarn, and Hiveql to run big data jobs with encryption and pay-as-you-go pricing.

  • Data Lake Analytics2:49

    Explore data lake analytics and data lake store, focusing on the new job option and data explorer, writing SQL code to Azure Data Factory, and preparing for blob storage uploads.

  • Brief on the Dataset Used9:09

    Shows creating grocery sales data set, uploading to blob storage, using a u sql script to parse space-delimited records, aggregate by date, and store summary in sql and data lake.

  • Azure Data Lake Pipeline6:58

    Build an Azure Data Factory pipeline to transform blob data with Azure Data Lake Analytics, store results in Azure Data Lake Store, and copy to SQL Azure.

  • Creating Data Sets8:50

    Create and reuse data sets to connect blob storage, data lake store, and SQL Azure within Azure Data Factory. Manage three data sets representing each transformation and copy step.

  • Creating Data Sets Continues6:44

    Continue creating data sets for SQL Azure and JSON in Azure Data Factory, aligning the columns with grocery_sales, and configure data sources for Azure Blob Storage and Data Lake Analytics.

  • Creating Pipelines8:40

    Create an Azure Data Factory pipeline that summarizes blob storage data using data lake analytics, then copies the results into SQL Azure, configuring scripts, inputs, outputs, and mappings.

  • Analyzing Missing Inputs3:48

    Identify and fix missing inputs in Azure Data Factory pipeline by correcting dataset names, adjusting validations, deploying, and re-running pipelines until the run turns green.

Requirements

  • Basic understanding of cloud computing concepts
  • Familiarity with Microsoft Azure
  • Knowledge of SQL and databases
  • Experience with web development
  • Basic programming skills, preferably in .NET or similar languages
  • Understanding of data processing and ETL concepts
  • Access to a computer with internet connectivity
  • An active Microsoft Azure subscription or free trial account

Description

Course Introduction

Welcome to "Azure Advanced: Data Factory | PAAS | App Services | Application Migration." This comprehensive course is designed to equip you with advanced knowledge and practical skills to effectively utilize Microsoft Azure. Whether you're looking to deepen your understanding of Azure Data Factory, explore Platform as a Service (PAAS), manage Azure App Services, or master the nuances of migrating applications to Azure, this course covers it all. Through a blend of theoretical insights and hands-on exercises, you'll gain the expertise needed to excel in the dynamic field of cloud computing.

Section 1: Microsoft Azure - Data Factory

In this section, we delve into the essentials of Azure Data Factory. You'll start with an introduction to its capabilities and practical applications. From creating a Blob and setting up ADF in Microsoft Azure to managing pipelines and handling errors, you'll gain a comprehensive understanding of the data integration process. Key topics include creating schemas in SQL Azure, copy activities, and monitoring pipelines. By the end of this section, you’ll be proficient in setting up and managing data workflows in Azure.

Section 2: Microsoft Azure - PAAS Overview

This section offers a detailed overview of Platform as a Service (PAAS) in Microsoft Azure. You’ll learn about various Azure services, including Web Apps, mobile app deployment, Azure Storage, and SQL Databases. The section focuses on deploying web applications through Visual Studio, provisioning mobile apps, and managing Azure SQL databases. By understanding these core services, you'll be able to leverage PAAS for building and deploying scalable applications.

Section 3: Azure Practical - App Services

Here, we focus on the practical aspects of Azure App Services. Starting with an introduction to Azure App Service, you'll learn how to deploy websites, make changes, and redeploy them. The section covers key services such as Dot Net Core Web Apps, resource group creation, and database migration functions. You’ll also explore enabling CORS, deploying REST APIs, and implementing Azure CDN. This section equips you with the skills to manage and optimize Azure App Services effectively.

Section 4: Azure Practical - Developing Your Applications

This section is dedicated to developing applications on Azure. It begins with an introduction to Azure application development, including web app components and FTP deployment. You’ll learn about using Azure Active Directory, creating web jobs, and managing databases for applications. Practical examples and step-by-step guidance help you understand how to develop, deploy, and manage applications in Azure, ensuring you can handle real-world scenarios with confidence.

Section 5: Azure Practical - Website and Database Migration to Azure Platform

In this section, you’ll learn about migrating websites and databases to the Azure platform. Topics include moving local web apps, using migration tools, and configuring Azure SQL databases. The section guides you through connecting to Azure SQL servers and optimizing performance. By the end, you'll have a solid understanding of the migration process, from initial setup to final implementation, ensuring a smooth transition of your applications to the cloud.

Section 6: Azure Practical - Migrating .NET-based Web Applications to Azure PAAS

The final section focuses on migrating .NET-based web applications to Azure PAAS. You’ll explore the steps involved in migrating web applications, connecting to Azure databases, and generating SQL scripts for migration. Detailed tutorials on MVC application migration, content and database transfer, and testing configurations provide a complete guide to the migration process. This section ensures you can seamlessly migrate and manage .NET applications in the Azure environment.

Conclusion

"Azure Advanced: Data Factory | PAAS | App Services | Application Migration" is designed to provide you with in-depth knowledge and hands-on experience in using Microsoft Azure. Each section builds on the previous one, ensuring a comprehensive learning experience. By the end of this course, you’ll have the skills and confidence to manage complex Azure environments, develop robust applications, and execute seamless migrations, positioning you for success in the ever-evolving world of cloud computing.

Who this course is for:

  • IT professionals looking to enhance their cloud skills
  • Data engineers and architects interested in Azure Data Factory
  • Web developers aiming to deploy and manage applications on Azure
  • Database administrators seeking to migrate databases to Azure
  • Cloud enthusiasts wanting to learn about Azure's PAAS and App Services
  • Software developers focused on .NET-based applications
  • Technical project managers overseeing cloud migration projects
  • Students and professionals preparing for Azure certification exams
  • Anyone interested in mastering advanced Azure functionalities