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Azure Data Engineer Training in Mumbai

The modern data-driven economy depends on precision, scalability and reliability of data pipelines to drive analytics, reporting and AI-driven decision making in businesses. With the organizations constantly moving to the cloud, Azure Data Engineers have become a necessity.

All About Azure Data Engineer Training

To get yourself a high paying, future-proof job, Version IT offers you the most detailed Azure Data Engineer Training in Mumbai, which is offered to beginners, working professionals, and whoever wants to change their field of work towards the cloud data sphere. This program equips you with the opportunities of working in the global arena and earning a certification as a Microsoft DP-203 with hands-on learning, practical projects, and expert guidance.

Best Reasons Why Azure Data Engineering is the Future of Cloud Careers

Azure has been a swiftly growing cloud platform in the world with its reliability, scalability, and security that enterprises have trusted. As large volumes of data are created within a second, companies require expert Azure Data Engineers to create a data solution, operate pipelines, empower analytics, and enforce cloud governance.

Best reasons to develop an Azure Data Engineering career:

High demand in industries to a very large degree.
The global career opportunity with the possibility of working remotely.
The fundamental competencies of AI, automation, and contemporary data analytics.
Good remuneration packages and fast track careers.
Cloud-first and enterprise-grade system working capability.

This is the time to be in the Azure Data Engineer Training Course in Mumbai, whether you are beginning the IT life or improving your knowledge.

Version IT’s Azure Data Engineer Course in Mumbai

The Azure Data Engineer Course in Mumbai by Version IT is designed to provide the end-to-end exposure to the tools of Azure data engineering (including):

Azure Data Factory
Azure Databricks
Azure Synapse Analytics
Azure Stream Analytics
Azure SQL & Cosmos DB
Azure Data Lake
Monitoring, governance, and security of the cloud.

You have the option of classroom-based training or the Azure Data Engineer Online Training in Mumbai which have equal quality learning experience.

What You Will Learn at our Azure Data Engineer Training?

Below provides the module-by-module breakdown of important concepts, practical projects, and results.

Azure Cloud and Data Engineering: Introduction.

The program will start with a solid base in the cloud computing environment with a focus on the Azure architecture and resource management, as well as core services.

Key Topics:

The reason why cloud computing is changing data careers.
Resource groups, deployments and azure subscription.
Learning the duties of an Azure Data Engineer.
Cloud Data Lifecycle Management.

Azure Data management and storage.

This module will learn how to save, manipulate, and process data in the cloud effectively.

What you’ll learn:

Azure Blob Storage
Azure Data Lake storage v1.0 and v2.0.
Hierarchy of data, file system and storage levels.
Establishing large-scale and scalable data storage.
Policies of access control, key vault, encryption and data backup.

Azure Databricks- Unified Analytics Engine.

Big data engineering and AI engineering on Azure are backed by Databricks. You’ll learn:

Spark architecture
Notebooks, clusters, and jobs
Delta lake and optimized data structures.
PySpark/SQL Data transformation in large volumes.
Connection to Data Lake, ADF and Synapse.

Azure Data Factory (ADF) ETL and Orchestration Mastery.

ADF enables data engineers to build and automate complicated workflows.

Topics covered:

Pipelines, datasets, triggers and linked services.
Duplicate information activities and information movements.
Patterns of ETL (incremental load, mapping flows) in the real world.
Pipeline debugging/monitoring.
Git integration of CI/CD deployment.

Azure Synapse Analytics- Enterprise Data Warehousing.

Get to know the process of designing hybrid data analytics based on SQL and Spark.

You will learn:

Bridge architecture and workspace.
Dedicated SQL pools
Heavy processing spark pools.
Making pipelines and orchestration work together.
Maximizing cost and performance.

Azure stream analytics: real-time data engineering.

The processing of data is no longer carried out in batches, real-time analytics is an essential quality of a contemporary data engineer.

Key Topics:

Real-time data ingestion
Azure Event Hub, IoT Hub
Stream processing query writing.

Data (Security) in Azure.

Any business requires high data protection and integrity.

Topics you’ll master:

RBAC authentication
Service principals and managed identities.
Encryption of data during rest and in transit.
Azure Key Vault
Azure Purview to data classification & lineage.

Combining Power BI with Azure.

Enabling business intelligence is an incomplete task of a data engineer.

You’ll learn:

Urge Synapse, ADF and Data Lake to Power BI.
Large data handling best practices.
Automation of pipeline refresh.

Who Should Enroll in Azure Data Engineer Online Course?

The course can be a great option for:

Fresh graduates
IT employees and cloud providers.
Data analysts & BI developers
Software developers
ETL engineers
Any person migrating to the cloud industry.

There are no high-order requirements or previous knowledge of cloud.

Key Features of Our Azure Data Engineer Online Training in Mumbai

✔ Live Instructor-Led Training.

✔ Live Projects/Assignments.

✔ Access to Azure Cloud Lab

✔ Resume Construction + Interviews.

✔ Job Placement Assistance

✔ Weekend and Weekday Batch.

✔ Access to Lifetime Learning Resources.

Why Version IT to Azure Data Engineering?

Experienced Faculty 10+ Years in the real industry.

Our coaches are the trainers who collaborate with major MNCs and introduce real-life case studies in the classroom.

100% Practical Training Approach.

You construct pipelines, ETL systems, warehouses and dashboards in the same way as in corporate settings.

Flexible Mode of Learning

Select between classroom sessions and options of Azure Data Engineer Online Course.

Commitment to Placement Support.

We assist you to develop a competitive resume, perform virtual interviewing, and engage with recruiting partners.

End-to-End Project Portfolio

Overall, you will have several real-life projects by the time you complete the course to present during interviews.

Career Opportunities After Azure Data Engineer Training

The skillset of the Azure Data Engineer exposes an individual to numerous exciting opportunities, such as:

Azure Data Engineer
Big Data Engineer
Cloud Data Engineer
Data Architect
ETL Engineer
Azure Developer
Analytics Engineer

Average Salary Range:

Freshers: ₹6 LPA – ₹10 LPA
Seasoned Professionals: 12 LPA- 22 and above LPA.

Established companies that are eager to recruit Azure Data Engineers are Accenture, TCS, Infosys, Capgemini, Deloitte, Cognizant, Wipro, Amazon and others.

What You Will Achieve After Completing the Course?

Upon successful completion of this course in Mumbai on the topic of an Azure Data Engineer Training, you will be able to:

Develop data solutions that are secure and scalable on a cloud based platform.
Process real-time and batch data.
Make data architecture enterprise-grade.
Implement data systems by deploying them with best practices.
Clear the DP-203 certification
Get industry prepared to earn well.

Topics You will Learn

Introduction to Cloud Computing

● Understanding different Cloud Models
● Advantages of Cloud Computing
● Different Cloud Services
● Different Cloud vendors in the market

Microsoft Azure Platform

● 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

Introduction to Azure Resource Manager & Cloud Storage 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

Migration of storage contents across Public & Private Clouds

● 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

Replication of Storage Accounts Authentication & Authorization of Storage Accounts & Azure Storage Explorer

● 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

Provisioning of SQL DB’s in Private & Public cloud computing

● 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

SQL DB Migrations

● 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 Server & SQL Queries from basics to Advance(till ADE Services)

● Introduction to SQL DB Queries
● SQL queries detail explanations, syntax & execution based upon real time
scenarios

What is Azure Data Factory(ADF)

● 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

Controls/Activities of Azure Data Factory(ADF) for copying the DATA across various sources to Azure IAAS & PAAS Services

● 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

Scheduling Triggers for automation of Dataflow/Datacopy to various sources and destinations in ADF

● 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.

What is Azure Keyvault, purpose of using Keyvault, Storing the SA keys, connection string in Azure KV with Access policies

● 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

Integrating Azure Data Factory with GitHub Portal

● 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.

Data Flows Transformations in Azure Data Factory

● 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.

Azure Data Bricks & Apache Spark

● 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

Azure Data Bricks & Apache Spark clusters features

● Creating single node and multi nodes clusters
● Creation of Pyspark notebooks in Databricks cluster to fulfil different business
requirements

Azure Synapse Analytics

● 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

Azure Stream 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