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Azure Data Engineer in

Azure Data engineering is the discipline of designing, maintaining, and developing a reliable infrastructure for collecting, transforming, storing, and serving data for machine learning, analytical reporting, and/or decision management.

8 Modules

with Certifications


After Completion



Microsoft Azure provides a variety of services intended to address basic commercial data engineering issues. This skill shows you how to use Azure services to design, implement, monitor, and optimise data platforms to fulfil the needs of the data pipeline.Microsoft Azure is a growing collection of integrated processing, cloud administrations, including investigation, systems administration, information base, portable, stockpiling, and web, that enable move faster, accomplish more, and save money.

Azure data engineers integrate, transform, and consolidate data from various structured and unstructured data systems into structures that are suitable for building analytics solutions.
Azure Data Engineer Training In Hyderabad- With the help of Version IT Trainers, you may become career-ready experts in the Azure Data Engineer domain.

Microsoft Azure Data Engineer helps companies

  • Use an open and adaptable platform that supports the widest range of operating systems, programming languages, structures, tools, data sets, and gadgets.
  • Use an open and adaptable platform that supports the widest range of operating systems, programming languages, structures, tools, data sets, and gadgets.
  • Extend your present IT with the largest network of secure private associations, crossover data sets, and capacity arrangements.
  • Protect your data by partnering with the most important cloud provider to adopt the new worldwide cloud security standard, ISO 27018.
  • Run your applications anywhere on an overall network of 42 Microsoft-managed datacenters.
  • Use Azure’s prescient examination administrations, for example, cortana analytics, machine learning, and stream analytics, to make better decisions.

Azure was designed to be beneficial to all levels of IT experts as a flexible, user-friendly platform. It incorporates coordinated instruments, formats, and managed administrations to effectively build and oversee project, versatile, web, and Internet of Things (IoT) applications using abilities and innovations you already have. So Begin Today with Version IT’Azure Data Engineer Training in India Program to learn all the Azure Data Engineering related skills with real time projects.

Who can Enroll?

Version IT offers the best Azure Data Engineer training in Hyderabad for all skill levels. There is no specific background required

Career Opportunities with Azure Data Engineer Training

Microsoft Azure’s confirmation statistics have been updated. You can now acquire Azure Data Engineer training and clearance that more clearly identifies your competencies in relation to your job. Demonstrate your knowledge, gain credit, and explore new possibilities by obtaining your Azure Data Engineer Training while focusing on your job. The new training structure prioritises quality over range, and with a more defined focus on the job rather than innovation, Microsoft Azure data engineer training will help you develop material abilities for your everyday activities.
Version IT is one of the best azure data engineer training institutes in Hyderabad and can assist you in learning the skills necessary to thrive in this industry. This Azure Data Engineer training will train you how to collect, process, and analyse data using Azure tools. You will also discover how to create and manage big data solutions. This best azure data engineer training in Hyderabad can help you develop in your profession and may lead to opportunities for higher-paying jobs.

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
  • Types of cloud services
  • Infrastructure as a Service
  • Platform as a Service
  • Software as a Service
  • Comparing Cloud Platforms
    • Microsoft Azure
    • Amazon Web Services
    • Google Cloud Platform
  • 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
  • Securing network connectivity
  • Core Azure identity services
  • Security tools and features
  • Azure Governance methodologies
  • Monitoring and reporting
  • Privacy, compliance, and data protection standards
  • bytes Data Type
  • byte array
  • String Formatting in Python
  • Math, Random, Secrets Modules
  • Introduction
  • Initialization of variables
  • Local variables
  • Global variables
  • 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
  • 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
  • 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

Let Your Certificates Speak


All You Need to Start this Course


Still Having Doubts?

An Azure Data Engineer is in charge of creating, deploying, and maintaining data processing systems that make use of Azure data services. Data intake, storage, processing, and visualization are all included.

SQL, data modeling, ETL (Extract, Transform, Load) procedures, understanding of Azure data services (e.g., Azure Data Factory, Azure Databricks, Azure SQL Database), and programming abilities (e.g., Python, PowerShell) are all required for an Azure Data Engineer.

An analytics platform built on Apache Spark is called Azure Databricks. Azure Databricks is frequently used by data engineers for large data processing, analytics, and developing data pipelines.

Data engineers may use Azure Data Factory to construct, plan, and manage data pipelines for data transfer and transformation.

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