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Azure Data Factory in

Version IT offers scenario-based and industry-specific learning in the Azure Data Factory course. The course will teach how to provision and manage services in Microsoft Azure.

10 Modules

with Certifications


After Completion



ADF is an Azure cloud service that allows you to move enormous volumes of data into the cloud. All data in transit is automatically encrypted by the service. It can move gigabytes of data in a couple of hours because it is intended for large data volumes. ADF also has the ability to integrate with GitHub for continuous integration. ADF setups can be downloaded and deployed in various environments as Azure ARM templates. PowerShell is also supported.

Azure Data Factory is a software system that transforms unstructured data into useful data warehouses and data lakes. It is made up of interconnected systems that work together to give an end-to-end data engineering platform. In enterprise environments, data is always flowing at varying rates and intervals. Users can utilise the Azure Data Factory to transform data and load it into centralised storage or cognitive services.

Data sets are storage containers that describe certain data structures. The data set in the accompanying screenshot, for example, links to a directory in an Azure storage account. These directory and container names are configured on the Parameters tab. You can optionally specify whether the data is zipped in the data set’s reference, allowing ADF to automatically decompress the data when it is read. You can also specify the type of data that the data factory will store.

A Data Engineer can also assist in converting data into a useful context with relevant insights. ADE can be extremely beneficial to analysts, data scientists, and business decision makers. Companies are increasingly integrating ADE into their workforce on a big scale in response to the increase in demand for ADF applications. This has increased the opportunities for qualified and certified professionals on this platform significantly.

By Joining our Best Azure Data factory Training In Hyderabad, you will gain knowledge of real-world abilities and traits in Data Engineering that will get you recruited right away.

Who can Enroll?

Version IT’s comprehensive real-time & hands-on knowledge based Azure Data Factory Training Institute In Hyderabad is perfect for people working in analytics, data engineers, system design, cloud experts, etc.

In addition to them, this course is appropriate for applicants who want to build their career in a technology that provides multiple chances and scope for continuing career development. Another group of people that can learn from our Azure Data Factory training programme is:

The other target audience are:
  • Freshers/Graduates
  • Job seekers who are interested in Azure
  • Analysts
  • Network Admins
  • Database Administrator
Career Opportunities with Azure Data Factory

An Azure Data Factory position includes participating in all stages of a service’s lifecycle as well as interacting with customers and community members. Experience with cloud computing platforms, big data, machine learning, and data science will be useful. Furthermore, you should be at ease working in a team atmosphere.

The ability to work from home is the finest part of working with Azure Data Factory. The company provides a variety of versatile and secure data warehouses and services to assist you in organising and storing a wide range of data. These services assist businesses in processing and organising all of their data. The company offers a complete platform for data engineers to create and manage hybrid data integration projects.

As an experienced developer, you must be well-versed in Microsoft’s BI Stack. You should have substantial experience working in a team and creating new solutions on your own. You must also have strong technical skills in at least one development technology.

Version IT one of the Best Azure Data Factory Training Institutes In Hyderabad Provides All above Knowledge with Azure Data Factory training programme so join and learn the skills to enhance your career.

Topics You will Learn

  • What is the “Cloud”?
  • Why cloud services
  • Types of cloud models
    • Deployment Models
    • Private Cloud deployment model
    • Public Cloud deployment model
    • Hybrid cloud deployment model
  • Characteristics of cloud computing
    • On-demand self-service
    • Broad network access
    • Multi-tenancy and resource pooling
    • Rapid elasticity and scalability
    • Measured service
  • Cloud Data Warehouse Architecture
  • Shared Memory architecture
  • Shared Disk architecture
  • Shared Nothing architecture
  • Core Azure Architectural components
  • Core Azure Services and Products
  • Azure solutions
  • Azure management tools
  • Securing network connectivity
  • HdInsights Overview.
  • Core Azure identity services
  • Security tools and features
  • Azure Governance methodologies
  • Monitoring and reportingS
  • Privacy, compliance, and data protection standards
  • Azure subscriptions
  • Planning and managing costs
  • Azure support options
  • Azure Service Level Agreements (SLAs)
  • Service Lifecycle in Azure
  • What is tuple?
  • Different ways of creating Tuple
  • Method of Tuple object
  • Tuple is Immutable
  • Mutable and Immutable elements of Tuple
  • Process tuple through Indexing and Slicing
  • List v/s Tuple
  • Storage Service and Account
  • Creating a Storage Account
  • Standard and Premium Performance
  • Understanding Replication
  • Hot, Cold and Archive Access Tiers
  • Working with Containers and Blobs
  • Types of Blobs
  • Block Blobs,
  • Append Blobs
  • Page Blobs
  • Blob Metadata
  • Soft Delete
  • Azure Storage Explorer
  • Access blobs securely
  • Access Key
  • Account Shared Access Token
  • Service Shared Access Token
  • Shared Access Policy
  • Storage Service Encryption
  • Azure Key Vault
  • Introduction to Azure Data Lake
  • What is Data Lake?
  • What is Azure Data Lake?
  • Data Lake Architecture?
  • Working with Azure Data Lake
  • Provisioning Azure Data Lake
  • Explore Data Lake Analytics
  • Explore Data Lake Store
  • Uploading Sample File
  • Using Azure Portal
  • Using Storage Explorer
  • Using Azure CLI
  • What is Data Factory?
  • Data Factory Key Components
  • Pipeline and Activity
  • Linked Service o Data Set
  • Integration Runtime Provision Required Azure Resources
  • Create Resource Group
  • Create Storage Account
  • Provision SQL Server and Create Database
  • Provision Data Factory
  • ADF Introduction
  • Important Concepts in ADF
  • Create Azure Free Account for ADF
  • Integration Runtime and Types
  • Integration runtime in ADF-Azure IR
  • Create Your First ADF
  • Create Your First Pipeline in ADF
  • Azure Storage Account Integration with ADF
  • Copy multiple files from blob to blob
  • Filter activity __ Dynamic Copy Activity
  • Get File Names from Folder Dynamically
  • Deep dive into Copy Activity in ADF
  • Copy Activity Behavior in ADF
  • Copy Activity Performance Tuning in ADF
  • Validation in ADF
  • Get Count of files from folder in ADF
  • Validate copied data between source and sink in ADF
  • Azure SQL Database integration with ADF
  • Azure SQL Databases – Introduction Relational databases
  • Creating Your First Azure SQL Database
    • Deployment Models
    • Purchasing Modes
  • Overwrite and Append Modes in Copy Activity
  • Full Load in ADF
  • Copy Data from Azure SQL Database to BLOB in ADF
  • Copy multiple tables in Bulk with Lookup & ForEach in Data Factory
  • Logging and Notification Azure Logic Apps
  • Log Pipeline Executions to SQL Table using ADF
  • Custom Email Notifications Send Error notification with logic app
  • Use Foreach loop activity to copy multiple Tables- Step by Step Explanation
  • Incremental Load in ADF
  • Incremental Load or Delta load from SQL to Blob Storage in ADF
  • Multi Table Incremental Load or Delta load from SQL to Blob Storage
  • Incrementally copy new and changed files based on Last Modified Date
  • Azure Key Vault integration with ADF
  • Azure Key Vault, Secure secrets, keys & certificates in Azure Data
  • ADF Triggers:
  • Event Based Trigger in ADF
  • Tumbling window trigger dependency & parameters
  • Schedule Trigger
  • Self Hosted Integration Runtime
  • Copying On Premise data using Azure Self Hosted integration Runtime
  • Data Migration from On premise SQL Server to cloud using ADF
  • Load data from on premise sql server to Azure SQL DB
  • Data Migration with polybase and Bulk insert
  • Copy Data from sql server to Azure SQL DW with polybase & Bulk Insert
  • Data Migration from On premise File System to cloud using ADF
  • Copy Data from on-premise File System to ADLS Gen2
  • ToCopying data from REST API using ADF
  • Loop through REST API copy data TO ADLS Gen2-Linked Service Parameters
  • AWS S3 integration with ADF
  • Migrate Data from AWS S3 Buckets to ADLS Gen2
  • Activities in ADF
  • Switch Activity-Move and delete data
  • Until Activity-Parameters & Variables
  • Copy Recent Files From Blob input to Blob Output folder without LPV
  • Snowflake integration with ADF
  • Copy data from Snowflake to ADLS Gen2
  • Copy data from ADLS Gen2 to Snowflake
  • Azure CosmosDB integration with ADF
  • Copy data from Azure SQLDB to CosmosDB
  • Copy data from blob to cosmosDB
  • Advanced Concepts in ADF
  • Nested ForEach -pass parameters from Master to child pipeline
  • High Availability of Self Hosted IR &Sharing IR with other ADF
  • Data Flows Introduction
  • Azure Data Flows Introduction
  • Setup Integration Runtime for Data Flows
  • Basics of SQL Joins for Azure Data Flows
  • Joins in Data Flows
  • Aggregations and Derive Column Transformations
  • Joins in Azure DataFlows
  • Advanced Join Transformations with filter and Conditional Split
  • Data Flows – Data processing use case1
  • Restart data processing from failure
  • Remove Duplicate Rows &Store Summary Credit Stats
  • Difference Between Join vs.Lookup Transformation& Merge Functionality
  • Dimensions in Data Flows
  • Slowly Changing Dimension Type1 (SCD1) with HashKey Function
  • Flatten Transformation
  • Rank, Dense_Rank Transformatios
  • Data Flows Performance Metrics and Data Flow Parameters
  • How to use pivot and unpivot Transformations
  • Data Quality Checks and Logging using Data Flows
  • Batch Account Integration with ADF
  • Custom Activity in ADF
  • Azure Functions Integration with ADF
  • Azure HDInsight Integration with ADF
  • Azure HDInsight with Spark Cluster
  • Azure Databricks Integration with ADF
  • ADF Integration with Azure Databricks
  • Azure Data Lake Analytics integration with ADF
  • Introduction Azure SQL Database
  • Comparing Single Database
  • Managed Instance
  • Creating and Using SQL Server
  • Creating SQL Database Services
  • Azure SQL Database Tools
  • Migrating on premise database to SQL Azure
  • Purchasing Models
  • DTU service tiers
  • vCore based Model
  • Serverless compute tier
  • Service Tiers
    • General purpose / Standard
    • Business Critical / Premium
    • Hyperscale
  • Deployment of an Azure SQL Database
  • Elastic Pools.
  • What is SQL elastic pools
  • Choosing the correct pool size
  • Creating a New Pool
  • Manage Pools
  • Monitoring and Tuning Azure SQL Database
  • Configure SQL Database Auditing
  • Export and Import of Database
  • Automated Backup
  • Point in Time Restore
  • Restore deleted databases
  • Long-term backup retention
  • Active Geo Replication
  • Auto Failover Group

Let Your Certificates Speak


All You Need to Start this Course


Still Having Doubts?

Data Factory is a cloud-based, fully managed data-integration ETL solution that automates data transfer and transformation. Azure Data Factory, like a factory that operates machinery to change raw materials into completed items, orchestrates existing services that take raw data and transform it into ready-to-use information.

Datasets, Pipelines, Activities, and Linked Services are the four main components of Azure Data Factory. Datasets are data structures, pipelines are collections of data-driven activities, activities are processing processes, and linked services specify the information needed to connect to other data sources.

Azure SQL Database, Azure Blob Storage, Azure Table Storage, on-premises SQL Server, on-premises Oracle, and many more data sources are supported by Azure Data Factory.

Yes, you may arrange the execution of data pipelines based on your business requirements with Azure Data Factory. You may create repeating schedules and activate pipelines on the fly.

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