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Azure Data Engineer Training in USA
Start your cloud data career today with Version IT’s Azure Data Engineering Training and gain industry-ready skills and advance your career in the fast-growing data engineering field.
Azure Data Engineer Training in USA Overview
The use of cloud data technologies is changing the way businesses handle and process data. Companies in the United States are quickly moving to use Microsoft Azure to develop scalable platforms and use it to drive analytics, reporting, and artificial intelligence solutions. This increased usage has resulted in a high demand of professionals capable of designing and managing the modern data systems.
Version IT Azure data engineer Training in USA assists the learners to develop the skills needed to operate the efficient data instruments and services of Azure. The training specializes in practical skills, which enable professionals to deal with large datasets, create data pipelines, and enable enterprise data-driven decision-making.
This program will equip the students with the knowledge to become high-achieving Azure Data Engineers in the current cloud-first technology environment with professional training and hands-on training.
Understanding the Position of Azure Data Engineer
The position of Azure Data Engineers in a modern organization that is data-driven is very important. Their main task is to create and support data systems that help businesses to quickly gather, store and work with large amounts of data.
Azure data engineering trainees in usa understand how to create scalable data pipelines to combine various data sources and provide structured information to analytics and business intelligence applications. Such professionals make sure that data is transferred efficiently between systems enabling organizations to derive useful information.
Azure Data Engineers work alongside data analysts and data scientists as well as software developers to design data environments that are capable of running advanced analytics and machine learning applications. Their knowledge will mean that organizations are able to handle big data in less time and still achieve performance, reliability and security.
With the further investments of companies in cloud-based data solutions, the task of Azure Data Engineer grows in significance in terms of managing the enterprise data infrastructure.
Why Azure Data Engineering Skills are in Demand in 2026?
The concept of cloud computing has transformed the way businesses conduct data infrastructure management. Organizations are now using cloud platforms to store and make data more effective instead of using the traditional on-premise databases.
The influx of professionals skilled in Azure data engineering training in USA has been on the high scale due to the fact that to a very large scale, Azure provides a holistic environment of data storage, data integration, analytics, and machine learning. One of the areas in which companies are using the Azure services is in order to handle massive data operations.
Individuals with an Azure engineering course in USA would acquire a good knowledge of how to design a cloud-based data solution. The capabilities are quite useful in organizations that rely on sound data systems in order to make business decisions.
The financial, health, retailing, e-commerce and technology industries need knowledgeable Azure Data Engineers to handle complex data pipelines and facilitate a seamless proceeding of data integration. With the ever-increasing use of big data, the demand of qualified Azure data professionals would keep on rising.
Microsoft data engineering training in USA
Version IT’s Microsoft Azure data engineering training in USA is aimed at enabling the learners to learn how to construct and maintain enterprise data solutions using Microsoft Azure technologies. In this training program, the focus lies on the practical implementation, and the students are able to attain the real-life expertise.
The participants get to know that Azure services can collaborate to form powerful data ecosystems. Starting with consuming raw data and ending with the development of meaningful insights, the training encompasses all the steps of the current data engineering life cycle.
Students too learn the best practice of handling cloud-based data environments. This involves learning on how to maximize performance, reliability of the data and security of the data systems in the Azure.
The training will be made available to those who are interested in gaining employment-ready cloud data engineering skills.
Benefits of Azure Engineering Online Training in USA
Version IT will make sure that the learners are ready to handle the Azure technologies by integrating theoretical knowledge and practical work. Online training enables the participants to get to listen to an instructor led session remotely and be able to get the same quality training as classroom education does.
Another advantage of the online format is that the learners in various locations of the United States can attend the training program.
Azure data engineering online training in USA will enable the students to acquire skills in cloud data engineering that are relevant in the industry and allow a flexible and convenient learning process.
Jobs on completion of training on Azure Data Engineering.
Doing Azure data engineer training in USA might open many job opportunities in the fast growing field of cloud computing. Organizations are in search of professionals that are capable of handling massive type of data systems and developing credible cloud data systems.
Azure Data engineers will design data pipelines, data sources integration and their responsibility will be to ensure their data systems perform efficiently. These experts are important in aiding business to use data to make strategic decisions.
Once the training is complete, learners will have an opportunity to seek jobs of an Azure Data Engineer, Cloud Data Engineer, Data Platform Engineer, and Big Data Engineer. These positions have good career advancement and remuneration.
Why Select Version IT to train Azure Data Engineering?
Version IT has developed itself as a reliable training company in advanced cloud and data engineering courses. The Azure training in USA by Version IT offers:
In-service Industry Trainers.
The training will be conducted by professionals who have a good experience in using the Azure cloud technologies in the real projects in the industry. Their practical knowledge also enables learners to have an insight into complex data engineering concepts in real world examples and best practices in an enterprise environment.
Practical Learning through Live projects.
Practical experience to students is attained through working on real time projects to simulate real business data systems. The projects allow learners to learn how Azure data packages, storage systems and cloud data platforms work in real enterprise environment.
Job-Centered Training Design.
The program will target the skills required by employers of Azure Data Engineers. Students, through acquiring practical tools, technologies, and methods of implementation, can be better equipped to work in the cloud data engineering field as technical workers.
Career Advice and Interviews.
Version IT offers career development assistance to learners like resume building, mock interviews, and interview preparation sessions. This counseling enables students to be optimistic about their job prospects at the end of their training.
Support and Learning Resources Certification.
The training course also incorporates the advice on preparation of the Azure certification. Students are provided with practice materials, technical resources and mentorship that make the Microsoft Azure certification exams effective preparations.
Azure Data Engineering Certification Opportunities
One of the steps which must be taken by people who want to certify their skills in cloud data engineering is professional certification. The completion of Microsoft Azure data engineering training in USA makes learners take globally recognized certification exams of the Azure.
Version IT offers advice and practice materials that assist learners to be confident about passing certifications exams. The knowledge that the students acquire in order to succeed in their certification experiment is gained through structured learning and hands-on training.
Topics You will Learn
● Understanding different Cloud Models
● Advantages of Cloud Computing
● Different Cloud Services
● Different Cloud vendors in the market
● Introduction to Azure
● Azure cloud computing features
● Azure Services for Data Engineering
● Introduction of Azure Resources/Services with examples
● Azure management portal
● Advantage of Azure Cloud Computing
● Managing Azure resources with the Azure portal
● Overview of Azure Resource Manager
● Azure management services
● What is Azure Resource Groups
● Configuration and management of Azure Resource groups for hosting Azure
services
● Completed walkthrough of the Azure Portal with all the features
● What is Resource Groups and why do we need RG’s in Azure cloud computing
platform to host resources?
● Different types of Storage Accounts provisioning in Cloud computing with
different storage services
● Details explanation & understanding of different Blob/container storage
services
● Creating and managing the data in container storage services with Public and
Private accesses as per the need of a project
● Implementation of Snapshots for Blob storage services and File share storage
service
● Generating SAS for different storage services to make the storage content
browseable across all the globe or Publicly
● What is Standard Storage Account and Premium Storage account and which to
use accordingly as per the real time scenarios
● Detail explanation and implementation of Data Lake storage Gen2 Storage
Account to store the unstructured data in cloud storage services
● All the features/properties(Overview, activity log, Tags, Access control(IAM),
Storage browser…etc) of Azure Storage Accounts
● Maintenance and management of Storage keys and connection string for Azure
Storage services
● Implementing different levels of access(Reader, contributor, owners…etc) to the
Azure Storage accounts
● Moving the storage account with storage content across different Resources
Groups based on real time scenarios
● Migrating the data from On-prem(Private cloud) to Azure Storage account
(Public cloud) using Az copy(forward migration)
● Migrating the data from public cloud to Private cloud(reverse migration)
● Implementing the Az copy commands to migrate the data
● Moving the SA & its content from one Resource Group to another
● Azure Storage explorer for creating, managing, and maintaining the Azure
storage services data
● Installation of Azure Storage Explorer and what is the purpose of this tool for
Azure Storage accounts(its Purpose & benefits with real time scenarios)
● Generate Shared Access Signature(SAS) in Azure Storage Explorer(ASE) for
security implementation of Storage account content
● Managing of Access keys & connection strings of SA with Azure Storage
Explorer
● Configuration of Authentication and Authorization for Storage Account via
Azure Active Directory
● Hosting File share Storage services to On prem servers or Cloud Servers as
shared drive for File share servers
● Introduction to SQL DB’s
● Creation of new SQL DB’s & Sample SQL DB’s both in On-prem and Cloud
computing
● Planning and deploying Azure SQL Database
● Implementing and managing Azure SQL Database
● Managing Azure SQL Database security
● Planning and deployment of SQL DB’s in Azure cloud computing with real time
scenarios
● Different DB’s Deployment options
● Databases purchasing models.(VCore & DTU’s)
● Visualization of cloud DB server, Database, and validation of data from
on-prem(private cloud)
● Implementation of Firewall security rules on Azure DB servers to access and
connect from on-prem SSMS
● Creation of Database in on-premises and synch with azure cloud
● Migrating SQL DB’s from On-premises to Azure cloud computing using
Microsoft Data migration assistant
● Restoring SQL DB’s from On-prem to cloud computing
● Migration of Specific DB objects from on-prem to cloud based upon base upon
project requirements
● Implementation of RSV and scheduling the backups of SQL DB’s and Azure
Storage Account file share services on schedule, on demand based upon real
time scenarios
● Introduction to SQL DB Queries
● SQL queries detail explanations, syntax & execution based upon real time
scenarios
● Deep understanding and implementation of concepts/Components of ADF
● Building blocks of Azure Data Factory
● Complete features and walk through of Azure Data factory studio
● Different triggers and their implementation in ADF
● What is integration run time and different types of integration run time in ADF
● When to use ADF
● Why to use ADF
● Different types of ADF pipelines
● Pipelines in ADF
● Different types of Activities in ADF
● Datasets in Azure Data factory
● Linked services in ADF
● Copying the data from Blb Storage account to ADL’s Gen2 Storage account
● Copying of zip files(.csv) from Blob SA to ADL’s Gen2 SA using ADF
● Implementation and explanation of Metadata control in ADF to find the structure
before copying the data
● Implementation and explanation of Validation and If Condition
● Implementation of Get Metadata control, filter control & For Each Control or
activities in ADF
● Implementation & execution to copy the data from GitHub platform to Azure
Storage services with variables and parameters
● Implementation of Foreach control, copy data control and Set variable to
dynamically load the data from source to target using ADF
● Creating Dynamic pipelines with lookup activity to copy multiple .csv files data
picking form Json format data in Azure Storage services
● Copying the files from GitHub Dynamically with the use of Dynamic parameters
allocation-AUTOMATION PROCESS
● Copying the data from different files formats(.csv, .xlsx, .txt, .Parquet, .Json,
.SQL…etc) using suitable ADF controls/activities
● Implementation and execution of Loading the data from Blb SA to SQL DB single
table & multiple tables using copy data activity, ForEach activity
● Executing multiple pipelines in parallel with Execute pipeline activity
● Implementation of Schedule based triggers for different ADF pipelines
containing different activities.
● Implementation of Event based triggers for different ADF pipelines containing
different activities.
● Implementation of Thumbling window-based triggers for different ADF pipelines
containing different activities.
● Implementation and execution of storage and Event based triggers.
● Detail explanation & implementation of Azure Keyvaults
● Making the SQL DB connection string to store in Keyvault to enhance the
security for SA content and SQL DB
● Generating the secrets inside the Azure keyvault and granting access by
implementing the access policies for different users
● Detail walk through of GitHub portal
● Creating an account, repo’s, in GitHub portal
● Integrating Azure Data Factory with GitHub Portal as per project requirements.
● Placing, maintaining and executing the source code via GitHub portal for Azure
Data Factory.
● Creating master branch, practice branches in GitHub portal to merge the newly
created code via Pull Requests.
● Setting up the Repo for ADF pipelines and converting to live mode from GitHub
portal covering with real time scenarios.
● Designing new Data flows
● Designing and implementing transformations
● Inline Datasets in data flow source control
● Designing and implementing of Data flow with Source transformations, Filter
transformations & Sink transformations in ADF with inline Datasets
● Implementation of Select transformations with Data flows for various source
controls
● Implementation of Dataflows using Aggregate & Sink transformation
● Implementation of Dataflow with conditional split & Sink transformation with
copy data activity
● Implementation of Dataflow with Exists & Sink transformation
● Implementation of Azure Dataflows for Derived column transformation with
Source & Sink transformation
● Implementation of Azure Dataflows to connect to SQL DB with Source & Sink
transformation
● Union & Union flow transformation implementation with ADF Data flows
● Implementation of Azure Dataflows to connect to SQL DB with Source & Sink
transformation
● Implementation of windows functions…like Rank() function, Dense_Rank()
function, Row_Number() function…etc.
● What is Apache Spark, details explanation and implementation of Apache Spark
● Illustration and Elaboration of Apache Spark Architecture
● Explanation of RDD & DAG
● Understanding of different Apache Spark components
● What are worker nodes and slaves nodes in Azure Data Bricks clusters
● Implementation of Azure Databricks cluster by considering different worker
nodes and slave nodes
● Different features and properties of Azure Data Bricks clusters
● Creating single node and multi nodes clusters
● Creation of Pyspark notebooks in Databricks cluster to fulfil different business
requirements
● What is Azure Synapse Analytics
● Implementation of Linked Services/Datasets in Synapse Analytics
● Implementation of dedicated SQL Pool inside Synapse Analytics
● Implementation of serverless SQL Pool inside Synapse Analytics
● Creation of Apache spark pool in Azure Synapse Analytics
● Writing SQL Script in Azure Synapse analytics to get the result set in tabular and
chart formats
● Visualizing the data in Synapse analytics in variety of different charts (like pie
charts, line charts, bar charts…. etc)
● Designing of Synapse Analytics pipelines by considering various activities as
per the business requirements
● Creation of Datasets, Linked services for Synapse Analytics pipelines
● Data analysis with serverless spark pools in Azure Synapse Analytics
● What is Apache spark in azure synapse analytics
● Designing and development of Apache spark pool in Azure synapse
● Creating Spark Databases and tables to load the data from source system and
analysing the data in Synapse analytics
● What is Azure Stream Analytics
● Purposes and usage of Stream Analytics in Azure cloud computing
● Benefits and advantages of stream analytics
● Architecture diagram of data flow in Azure stream analytics with other cloud
services
● Understanding & usage of browser-based Raspberry Pi simulator
● Deployment of IoT Hub services as an input for Stream analytics jobs
● Implementation & execution of stream analytics jobs and designing inputs and
outputs for IoT Hub and Datalake Gen2
● Writing SQL scripts to generate live streaming data and loading it in destination
