infyni

Azure Data Factory : Beginner to Pro

This live online training equips students with skills needed to design & implement data storage, processing, & transformation solutions using Azure. Led by our expert, this course provides personalized 1-1 coaching to equip you with industry relevant skills.

Live Course

Live Class: Saturday, 09 Mar

Duration: 15 Hours

Enrolled: 1

Offered by: infyni

(1)

Live Course

$273

About Course

Azure is a versatile cloud platform that can meet the needs of organizations across a wide range of industries and use cases, and its continued growth and investment by Microsoft make it a reliable choice for organizations looking to take advantage of the benefits of cloud computing.

The objective of this live and online course is to equip participants with the skills needed to design and implement data storage, processing, and transformation solutions using Azure. You will learn :

  • Azure Data Lake
  • Azure Blob
  • Azure SQL Server
  • Logic Apps
  • Azure Function Apps
  • Azure Data Bricks

Language that you will be using are Pyspark, Spark Sql. 

We will cover the following topics:

• Introduction to Azure Cloud and Azure Data Services

• Implementing data storage solutions using Azure Storage(ADL’s and Blob) and Azure SQL Database

• Ingesting and orchestrating data into Azure using Azure Data Factory.

• Transforming and processing data using Azure Databricks.

• Modeling the data into facts and dimensions.

• Loading the final transformed data into Azure Synapse.

• Designing and implementing ETL pipelines for various scenarios using Azure Data Factory 

The demand for Azure Data Engineers is growing as more organizations adopt cloud computing and seek to leverage the benefits of big data and advanced analytics.

To read why a career as an Azure Data Engineer can be a good choice check our blog section. 

Join us for a free live trial with an expert Azure engineer to answer all your doubts. 

Course Offerings

  • Instructor-led interactive classes
  • Clarify your doubts during class
  • Access recordings of the class
  • Attend on mobile or tablet
  • Live projects to practice
  • Case studies to learn from
  • Lifetime mentorship support
  • Industry specific curriculum
  • Certificate of completion
  • Employability opportunity
  • Topics
  • Instructor (1)
  • Reviews
  • Types of cloud, Cloud services

  •  
  • Azure Portal Explore – Various Services
  •  
  • Azure Storage Services
  •  
  • Azure Accounts – Subscription, Resources, Management
  •  
  • Blob Storage , Azure SQL server – Introduction, Evolution of 
  • Storages, and creation in Portal
  •  
  • Azure Data Lake vs Blob


  • Stages of Data Processing
  • Azure Data Factory – Introduction, Why ADF?
  • ADF Architecture
  • Data Factory Components
  • Working of ADF
  • ADF Creation
  • Integration RunTime
  • DataSets, Linked Services
  • Triggers
  • ARM Templates
  • Securities
  • Linked Services - Authorization
  • Creation of Linked Services & Datasets
  • Brief introduction to CI/CD
  • Data Movement Activities
  • Control Flow Activities
  • Creation of Pipelines
  • Scenario – Copy /Move Data by building pipelines [4 Possible Scenarios]
  • Schema & Data Type Mapping
  • Move Files from ADL’s to Azure SQL Server
  • Data Flows
  • Mapping Data Flows
  • Azure Functions
  • Working with Azure Functions
  • Durable Functions
  • Data Bricks – Introduction
  • ADF Mapping Data Flows VS Azure Data Bricks
  • Connecting ADF & ADB
  • Why Azure Data Bricks?
  • Spark Architecture 1-4 4
  • How Spark Works?
  • Exploring ADB Workspace
  • Clusters, Cluster Pool, Data Bricks Runtime
  • Cluster Creation
  • Data Bricks File System (DBFS)
  • Note book Creation & Manipulation
  • Mounting ADL’s in ADB
  • RDD, DataFrame, Datasets
  • Lazy Evaluation Spark
  • What is a DataFrame? Creating 1 st DataFrame
  • Introduction to Pyspark
  • Pyspark API Functions – Transformation
  • Slowly Changing Dimensions , Types
  • Fact & Dimensions – Brief Introduction
  • Understanding Various File Formats
  • Parquet & Avro Format
  • Narrow & Wide Transformation
  • Transformation with frequently used Data Frame API Methods
  • Difference between Pyspark & Pandas DF
  • Pyspark to Pandas & Pandas to Pyspark
  • User Defined Functions
  • Performance Optimization
  • Analytic Functions in Pyspark
  • Different Loading of data
  • Incremental Load
  • Full Load
  • Unit Testing of pipelines
  • Exploring GIT
  • Creating Feature Branch
  • Merging with Master
  • CI/CD with ADF & ADB
  • Work Flows From END to END
  • Layers of Data Handling

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