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📈 90% Placement Success
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📅 Course Duration
2 1/2 Months
💰 Course Price
₹ 25000
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📄 Course Content
Azure Data Engineer Training in Chennai
Design your data future with our Azure data engineer course in Chennai. Learn the skill of data flow through practical skills training, real-world projects and guaranteed placement.
An Overview of Azure Data Engineering Course
In the modern hectic environment, the world is moving at a fast pace with data being the key behind smarter decisions and revolutionary business innovations. The global need to employ competent data engineers to accomplish the job is on the rise, as organizations continue to push the limits of data to their utmost potential.
So, what is data engineering? Data engineering deals with the design and development of systems that are capable of collecting, storing and analyzing massive amounts of data at scale level with the ultimate effect of enabling organizations with real time information.
Are you willing to open the doors of the power of data and build your career to new heights? You can also learn data engineering in Chennai at Version IT and become the professional you desire to become in this vibrant job.
Enroll in our data engineering course, which is industry-centered and is bound to distinguish you. The intelligent combination of practical training and the best theoretical skills in our highly developed data engineering course is an elaborate and well-designed curriculum addressing the key aspects of data pipeline construction and maintenance of large-scale data systems as well as learning the cloud environments.
Highlights of Our Azure Data Engineer Online Training
Expert Trainers On-the-job training by experts on the experience of working with data engineering. | Modern Infrastructure Curriculum supported by modern tools that encompass ETL, cloud and the likes. | Personalized Learning Small batches and support to the trainer according to personal learning styles. |
Career Kick start Career placement provided to secure high positions in leading firms. | Learn From Anywhere Attend weekday and weekend classes that have reasonable cost and installment packages. | Ground Up Skill Building Much training to work towards more complicated issues such as big data processing smoothly. |
Who Can Take Our Azure Data Engineering Course?
- Technical enthusiasts with some fundamental understanding of computer basics and programming.
- New professionals in the engineering fields like IT, CS, ECE, or others.
- Experienced software developers or data analysts seeking a change of profession.
- Information technology specialists who wish to specialize in data systems; system engineers, DBAs, and others.
- Individuals who want to learn how to utilize big-data, cloud solutions, and ETL.
- Future data users in the role of data architects, data engineers or ETL developers.
Who are our Azure Data Engineering Mentors and Trainers?
- Experienced personnel in data engineering in the industry.
- Experts with outstanding expertise to create ETL pipelines, data warehouses, as well as cloud solutions.
- Instructors that are highly experienced in projects and have experience in various industries.
- Certified hardworking specialists in Spark, AWS, Azure, Python, SQL, and GCP among others.
- Involving mentors to give information on career guidance, interview preparation and portfolio building.
- Very qualified personnel to deliver the finest data engineering courses on the internet and through physical classroom instruction.
Azure Data Engineering Certifications in Chennai
To help you improve your resume-related data engineering skills, Version IT provides Azure data engineering certification:
Significant Results of Our Data Engineering Course
We are providing top Azure data engineer training online, which is in the prime IT hub in Chennai, which opens a door of amazing opportunities to fresher’s and established professionals. In data engineering, our extensive training and placement service provides you with:
- Practical learning on cloud and big data tools.
- Unlimited access to free Lab facilities and cloud labs.
- Free high-speed Wi-Fi
- Authentication of real world projects.
- Resume development in data engineering.
- Top company placement support.
- Regular mock interviews
- Interview preparation guide.
- Continuing technical assistance after the course.
- Blogging instruction to demonstrate competence.
Jumpstart Your Azure Data Engineering Career at Version IT
Version IT is the best Azure data engineer training institute in Chennai, offering extraordinary skills in the field of Azure data engineering. Placed data engineering courses prepare you to develop data systems and begin your career in it.
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
● 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
Let Your Certificates Speak
- Comprehensive training in Azure Data.
- Certificates are globally recognized & they upgrade your programming profile.
- Certificates are generated after the completion of course.
All You Need to Start this Course
- Engaging and interactive course content.
- Expert-led instruction for a deeper understanding.
FAQ's
Version IT provides training centered on industry with real-life projects and advanced skills on Azure technologies, so students have practical knowledge of the fundamentals of Azure Data Engineering and cloud data solutions. The plan is market-oriented based on market requirements, with improved employability and technical expertise in Azure environments. Career guidance and personal mentorship ensure that students get the best jobs in the profession.
The course of Azure Data Engineering presupposes the knowledge of the basics of programming (Python or SQL) skills and the familiarity with the concept of databases, as well as the concept of the cloud. Prior knowledge in the field of IT/computer science can be an advantage, but the beginners can start with introduction modules provided. The institute is also equipped with preparatory programs of people who are not yet familiar with the cloud data engineering.
Version IT focuses on practical labs, live projects on Azure systems and case studies, allowing students to develop actual data pipelines and solve problems of cloud data. In the course, there are frequent assessments and feedback to constantly improve the skills. The strategy makes students employment-viable with robust problem-solving skills on the clouds.
After completing the course, you will receive industry-accepted certification of Azure Data Engineering. The certificate confirms the knowledge on Azure cloud data technologies, which enhances trust in employers. It contributes great value to professional profiles and resumes in seeking Azure Data Engineering jobs.
We provide students with special placement services, such as resume building, interview preparation, and Azure job roles mock classes. The institute also collaborates with leading IT and cloud services, and thus it provides graduates with job placement. Frequent career workshops and networking also contribute towards successful placement.
