1. Module 1: Data Engineering Foundations (Week 1)
- •Role of a Data Engineer in cloud projects
- •Data engineering lifecycle and its main stages
- •Data warehousing vs data lakes
- •Batch data and streaming data concepts
- •Azure data ecosystem overview
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-Recruiters prefer to hire Inventateq Students - Join Inventateq NowPlacements in Azure Data Engineering: 1,342
Azure Data Engineering Online Course with Certification
Learn to design data pipelines on Azure using Azure Data Factory, Azure Databricks, Azure Synapse Analytics, ADLS Gen2, Azure SQL Database, Azure Event Hubs, and Power BI. This online course is built around ETL/ELT, batch and streaming data, warehouse design, and real project work.
4.7/5 from 1,432 reviews
Build ETL and ELT workflows that move data cleanly across Azure services.
Work hands-on with ADF pipelines, Databricks notebooks, and Synapse SQL pools.
Practice both batch ingestion and real-time streaming patterns.
Learn how ADLS Gen2, Blob Storage, and Azure SQL fit into a data platform.
Use Power BI for reporting and semantic-layer aware outputs.
Finish with projects, resume support, and interview preparation.
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Placement Assistance for Azure Data Engineering
Learning the tools is only half the work. The other half is making sure you can explain pipelines, transformations, warehouse design, and streaming systems in a way hiring teams understand. Inventateq supports that transition with role-focused guidance for Azure Data Engineer, Big Data Engineer, and junior Data Architect profiles.
Support starts during the course itself, not after the final module. As you complete the projects, the team helps you shape your resume, organize your project notes, and prepare for interviews around the exact Azure services you trained on.
Our Signature Career Support:
- Resume framing for Azure Data Engineer and related data platform roles
- Project guidance for ETL, retail warehouse, and streaming analytics work
- Mock interview practice on ADF, Databricks, Synapse, and Azure security topics
- Help presenting your Power BI and pipeline work as interview-ready proof
- Career mentoring aligned to data engineering, reporting, and platform support roles
Azure Data Engineering Salary Insights
Azure data engineers are hired by product companies, IT services firms, analytics teams, and enterprises that run reporting or streaming pipelines on Microsoft Azure. Pay usually rises as you move from pipeline execution to architecture, optimization, and end-to-end ownership.
Azure Data Engineer Average Salary by Experience
Azure Data Engineering Salary Insights
Azure data engineers are hired by product companies, IT services firms, analytics teams, and enterprises that run reporting or streaming pipelines on Microsoft Azure. Pay usually rises as you move from pipeline execution to architecture, optimization, and end-to-end ownership.
Azure Data Engineer Average Salary by Experience
Why Students Choose Our Azure Data Engineering Online Course?
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About Inventateq
Inventateq has supported learners across technical courses with a training style that stays practical, structured, and easy to follow. For the Azure Data Engineering online course, that means the same teaching discipline applied to pipelines, warehousing, streaming, and reporting without turning the class into theory-heavy noise.
We stand apart through our commitment to:
- Years of training experience across technical job-ready courses
- A teaching format built around live explanation and guided practice
- Curriculum updates that stay aligned with current Azure services and workflow patterns
- A learning environment that keeps tools, projects, and interview prep connected
- Support for learners from different backgrounds who need a clear path into data roles
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Attend live, instructor-led classes from anywhere with the same hands-on structure as our classroom batches. Follow along step-by-step, get real-time doubt support, and revisit recordings whenever you need to.
100% Live Instructor-Led Online Classes
Dedicated Doubt-Solving Sessions with Mentors
Study Guides, PPTs, and Exam Guidance Included
Class Recordings and Backup Sessions for Missed Classes
Flexible Weekday and Weekend Batch Timings
Career Guidance and Interview Preparation Support
Details of Azure Data Engineering Online Course
Fresh graduates
Good fit if you want to enter data engineering with a structured Azure path and project-based training.
Software developers
Useful if you want to move toward pipelines, data processing, and platform work on Azure.
Database professionals
Fits learners who already understand SQL and want to move into warehousing and ETL roles.
Analytics professionals
Strong choice if you work with reporting and want to understand upstream data movement.
IT support and operations staff
Helps if you want to shift into monitoring, orchestration, and cloud data workflows.
Career switchers with tech interest
Works for motivated beginners who want a clear path into Azure-based data roles.
Quick Highlights of Azure Data Engineering Online Course
A practical online schedule with trainer support and project time.
Structured modules: The syllabus is laid out from data engineering basics through projects and interview prep.
Live online format: Trainer-led sessions cover theory, demos, and exercises in the same class.
Hands-on tools: You work with ADF, Databricks, Synapse, ADLS, Azure SQL, Event Hubs, and Power BI.
Project finish: The course ends with end-to-end project work that you can discuss in interviews.
Azure Data Engineering Curriculum
W1
2. Module 2: Azure Core Data Services (Week 2)
- •Azure Data Lake Storage Gen2 for scalable storage
- •Azure Blob Storage for raw and staged data
- •Azure SQL Database for relational storage
- •Synapse Analytics overview
- •Data Factory introduction and service positioning
3. Module 3: Data Ingestion Basics (Week 3)
- •ETL vs ELT concepts
- •Batch data ingestion patterns
- •Incremental data loading methods
- •Data extraction from source systems
- •Ingestion best practices for reliable movement
4. Module 4: Azure Data Factory (Week 4)
- •ADF architecture and pipeline structure
- •Pipelines, activities, and datasets
- •Linked services configuration
- •Mapping data flows in ADF
- •Trigger setup and scheduling
5. Module 5: Azure Databricks for Data Engineering (Week 5)
- •Databricks workspace setup
- •PySpark for data processing
- •Data transformation pipelines
- •Delta Lake integration
- •Job orchestration inside Databricks
6. Module 6: Data Transformation and Processing (Week 6)
- •Data cleaning techniques
- •Joins, aggregations, and window functions
- •Schema design for engineering workflows
- •Handling large datasets
- •Performance optimization techniques
7. Module 7: Data Warehousing with Azure Synapse (Week 7)
- •Synapse Analytics overview
- •Dedicated SQL pools and serverless SQL pools
- •Data modeling techniques
- •Star schema and snowflake schema
- •Query optimization in warehouse queries
8. Module 8: Streaming and Real-Time Data (Week 8)
- •Azure Event Hubs for event ingestion
- •Azure Stream Analytics for live processing
- •Real-time data processing patterns
- •IoT data integration
- •Streaming pipeline design
9. Module 9: Data Security and Governance (Week 9)
- •Role-Based Access Control (RBAC)
- •Data encryption basics
- •Azure Key Vault usage
- •Data governance policies
- •Compliance and privacy considerations
10. Module 10: Monitoring and Optimization (Week 10)
- •Azure Monitor for service visibility
- •Pipeline monitoring
- •Logging and alerting
- •Cost optimization methods
- •Performance tuning practices
11. Module 11: Integration with BI Tools (Week 11)
- •Power BI integration with Azure data outputs
- •Data modeling for reporting
- •Semantic layer concepts
- •Dashboarding overview
- •Business reporting pipelines
12. Module 12: Real-Time Projects and Career Prep (Week 12)
- •End-to-end ETL pipeline project
- •Retail data warehouse project
- •Real-time streaming analytics project
- •Customer analytics data platform
- •Resume building and interview preparation
Student Reviews – Azure Data Engineering
4.7 Star Rating from 1,432+ Google Reviews
Rated 4.9/5 by AI Students
Why Learn Azure Data Engineering Today?
Azure data engineering is central to how companies move, secure, and serve data for reporting and operational use. Teams need people who can handle pipelines, warehousing, streaming, and monitoring on one cloud stack, not just one isolated tool.
Why Students Trust Inventateq for Azure Data Engineering
- The course follows the same service stack that data teams use on Azure.
- Learning is organized around pipeline design, transformation, governance, and reporting.
- The subject is taught in a way that connects batch work with real-time systems.
- Projects reflect the kind of work employers expect from junior data engineers.
- Inventateq keeps the training practical, so the tools stay tied to real output.
Build Real Azure Data Engineering Skills for Cloud Data Roles
By the end of the course, learners know how to move data through Azure services, shape it for analytics, and present it through reporting layers. The focus stays on what you can build and explain, not just what you have heard about.
Design data pipelines on Azure
You can plan and build pipelines that move data from source systems into storage, processing, and analytics layers using ADF and related services.
Process data with PySpark
You can use Databricks notebooks and PySpark to clean, transform, and prepare datasets for downstream use.
Build warehouse-ready structures
You can model data for Azure Synapse using star and snowflake patterns that support reporting workloads.
Handle streaming data flows
You can work with Azure Event Hubs and Stream Analytics to process live events and near-real-time inputs.
Apply security and governance controls
You can set up RBAC, encryption, and Key Vault-based practices that protect sensitive data.
Present business data in Power BI
You can connect processed data to reporting outputs and explain the end-to-end pipeline behind those dashboards.
Detailed Insights :: Azure Data Engineering Online Course in a practical format
Students Most Asked Questions
Is this course suitable for beginners?
It works for beginners who are comfortable learning technical concepts in a structured way. The syllabus starts with data engineering foundations before moving into Azure services, which helps the topic feel less scattered. A basic SQL background helps, but the course is designed to guide learners through the platform step by step.
Will I get hands-on project experience?
Yes. The course ends with projects such as an ETL pipeline, a retail data warehouse, a streaming analytics build, and a customer analytics platform. Those projects are useful because they turn the syllabus into something you can show and explain.
Does Inventateq help with placement support?
Support includes resume building, interview preparation, and guidance around the projects you complete during the course. The idea is to help you present your Azure skills clearly for data engineering roles. It is practical support tied to the tools and modules you studied.
Can someone from a non-data background join this online course?
Yes, if you are ready to learn the platform systematically and spend time on practice. People from software, support, analytics, or database backgrounds often find the transition manageable because the course explains where each Azure service fits. The project work also helps make the subject more concrete.
Is the course fully online?
The page is set up for online learners, and the live online format is built around trainer-led sessions. You can attend from anywhere while still seeing the tool demos, class walkthroughs, and project guidance in real time. That makes it practical for learners who do not want a classroom commute.
How long does it take to complete the syllabus?
The training is arranged in 12 modules, moving from foundations to projects and career prep. The exact pace depends on the batch schedule, but the structure is designed so you can move through the Azure stack in a logical sequence. That helps avoid the common problem of learning tools without understanding how they connect.
Which software and cloud tools will I learn?
You will work with Azure Data Factory, Azure Databricks, Azure Synapse Analytics, ADLS Gen2, Azure SQL Database, Azure Event Hubs, Azure Stream Analytics, Azure Monitor, Azure Key Vault, and Power BI. These are the main tools named in the syllabus and tools list. They cover storage, processing, orchestration, monitoring, security, and reporting.
Explore Our Training Locations
Inventateq offers classroom training across multiple locations. Explore the branch nearest to you and check available batch timings.
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