English | Size: 1.23 GB
Genre: eLearning
What you’ll learn
Introduction to Azure Data Factory. You will understand how it can be used to integrate many other technologies with an ever-growing list of connectors.
How to set up a Data Factory from scratch using the Azure Portal and PowerShell.
Activities and Components that makeup Data Factory. It will include Pipelines, Datasets, Triggers, Linked Services, and more.
How to transform, ingest, and integrate data code-free using Mapping Data Flows.
How to integrate Azure Data Factory and Databricks. We’ll cover how to authenticate and run a few notebooks from within ADF.
Azure Data Factory Deployment using Azure DevOps for continuous integration and continuous deployment (CI/CD)
TL;DR.
This course will introduce Azure Data Factory and how it can help in the batch processing of data. Students will learn with hands-on activities, quizzes, and a project, how Data Factory can be used to integrate many other technologies together to build a complete ETL solution, including a CI/CD pipeline in Azure DevOps. Some topics related to Data Factory required for the exam DP-203: Data Engineering on Microsoft Azure, are covered in this course.
Learn by Doing
Together, you and I are going to learn everything you need to know about using Microsoft Azure Data Factory. This course will prepare you with hands-on learning activities, videos, and quizzes to help you gain knowledge and practical experience as we go along.
At the end of this course, students will have the opportunity to submit a project that will help them to understand how ADF works, what are the components, and how to integrate ADF and Databricks.
Student key takeaways:
The student should understand how ADF orchestrates the features of other technologies to transform or analyze data.
The student should be able to explain and use the components that make up ADF.
The student should be able to integrate two or more technologies using ADF.
The student should be able to confidently create medium complex data-driven pipelines
The student should be able to develop a CI/CD pipeline in Azure DevOps to deploy Data Factory pipelines
What You’ll Learn:
Introduction to Azure Data Factory. You will understand how it can be used to integrate many other technologies with an ever-growing list of connectors.
How to set up a Data Factory from scratch using the Azure Portal and PowerShell.
Activities and Components that makeup Data Factory. It will include Pipelines, Datasets, Triggers, Linked Services, and more.
How to transform, ingest, and integrate data code-free using Mapping Data Flows.
How to integrate Azure Data Factory and Databricks. We’ll cover how to authenticate and run a few notebooks from within ADF.
Azure Data Factory Deployment using Azure DevOps for continuous integration and continuous deployment (CI/CD)
Data Factory Essentials Training – Outline
Introduction
Modules introduction
Getting Started
Understand Azure Data Factory Components
Ingesting and Transforming Data with Azure Data Factory
Integrate Azure Data Factory with Databricks
Continuous Integration and Continuous Delivery (CI/CD) for Azure Data Factory
Getting started
Sign up for your Azure free account
Setting up a Budget
How to set up Azure Data Factory
Azure Portal
PowerShell
Azure Data Factory Components
Linked Services
Pipelines
Datasets
Data Factory Activities
Parameters
Pipeline Parameters
Activity Parameters
Global Parameters
Triggers
Integration Runtimes (IR)
Azure IR
Self-hosted IR
Linked Self-Hosted IR
Azure-SSIS IR
Quiz
Ingesting and Transforming Data
Ingesting Data using Copy Activity into Data Lake Store Gen2
How to Copy Parquet Files from AWS S3 to Azure SQL Database
Creating ADF Linked Service for Azure SQL Database
How to Grant Permissions on Azure SQL DB to Data Factory Managed Identity
Ingesting Parquet File from S3 into Azure SQL Database
Copy Parquet Files from AWS S3 into Data Lake and Azure SQL Database (intro)
Copy Parquet Files from AWS S3 into Data Lake and Azure SQL Database
Monitoring ADF Pipeline Execution
Transforming data with Mapping Data Flow
Mapping Data Flow Walk-through
Identify transformations in Mapping Data Flow
Multiple Inputs/Outputs
Schema Modifier
Formatters
Row Modifier
Destination
Adding source to a Mapping Data Flow
Defining Source Type; Dataset vs Inline
Defining Source Options
Spinning Up Data Flow Spark Cluster
Defining Data Source Input Type
Defining Data Schema
Optimizing Loads with Partitions
Data Preview from Source Transformation
How to add a Sink to a Mapping Data Flow
How to Execute a Mapping Data Flow
Quiz
Integrate Azure Data Factory with Databricks
Project Walk-through
How to Create Azure Databricks and Import Notebooks
How to Transfer Data Using Databricks and Data Factory
Validating Data Transfer in Databricks and Data Factory
How to Use ADF to Orchestrate Data Transformation Using a Databricks Notebook
Quiz
Continuous Integration and Continuous Delivery (CI/CD) for Azure Data Factory
How to Create an Azure DevOps Organization and Project
How to Create a Git Repository in Azure DevOps
How to Link Data Factory to Azure DevOps Repository
How to version Azure Data Factory with Branches
Data Factory Release Workflow
Merging Data Factory Code to Collaboration Branch
How to Create a CI/CD pipeline for Data Factory in Azure DevOps
How to Create a CICD pipeline for Data Factory in Azure DevOps
How to Execute a Release Pipeline in Azure DevOps for ADF
Quiz
Who this course is for:
Data Professionals
Data Architects
Data Engineers
ETL Developers
Software Developers
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