Learn Azure Data Factory online with hands-on practice in ADF Studio, pipelines, linked services, integration runtime, and Azure DevOps. You will work through ETL and ELT pipeline design, data movement, monitoring, security, and CI/CD the way real data teams handle it.
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Learning ADF is only half the work. The other half is presenting that skill set in a way hiring teams understand, especially for roles like Azure Data Engineer, ETL Developer, and Data Integration Engineer. Inventateq keeps the support focused on what candidates actually need to show: pipeline work, tool familiarity, and clear project discussion.
The support runs alongside the course, not after it disappears. You get project-based profile material, resume improvement around ADF and Azure services, and interview practice built around the same topics covered in class, so your preparation stays close to the role requirements.
Azure data integration roles are hired across IT services, product companies, cloud migration teams, BI groups, and data engineering functions in India and remote-first setups. Pay grows as you move from pipeline building to owning deployment, optimization, and end-to-end data workflows.
Azure Data Factory Average Salary by Experience
Azure data integration roles are hired across IT services, product companies, cloud migration teams, BI groups, and data engineering functions in India and remote-first setups. Pay grows as you move from pipeline building to owning deployment, optimization, and end-to-end data workflows.
Azure Data Factory Average Salary by Experience
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Inventateq has supported learners across technology training programs with a steady, classroom-style teaching approach that stays focused on job readiness. For the Azure Data Factory online course, that means structured lessons, guided practice, and support that keeps the subject tied to real Azure data work.
We stand apart through our commitment to:
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.
People starting with Azure data workflows can build a clear base in ETL, ELT, and ADF architecture.
Developers who already work with databases can move into pipeline automation and Azure integration.
Those with ETL or SSIS exposure can shift toward Azure Data Factory and modern data movement.
Analysts who want to understand how data reaches warehouses and dashboards can benefit from the pipeline view.
Support and application teams can add data integration skills for migration and workflow projects.
Learners aiming for Azure Data Engineer or ETL Developer roles can use the course to build role-ready skills.
Structured modules: The syllabus moves from ADF basics to monitoring, optimization, and CI/CD.
Live online access: Attend from anywhere without losing the guided classroom flow.
Project phase included: End-to-end exercises are part of the course, not an add-on.
Career prep time: Resume building and interview preparation are included near the end of the course.
4.7 Star Rating from 1,432+ Google Reviews
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Companies keep moving data workloads into Azure, and they need people who can build pipelines that are secure, scheduled, monitored, and easy to maintain. That makes Azure Data Factory useful across migration projects, analytics teams, and cloud data engineering roles.
By the end of the course, learners can design, run, and troubleshoot Azure-based data pipelines with practical confidence. The training turns ADF from a list of features into a working skill set they can explain in interviews and use in projects.
You learn how to shape ADF pipelines for file and database movement, rather than treating Copy Data as a one-click action. That includes source setup, sink setup, and the logic that keeps transfers predictable.
Mapping Data Flows, wrangling, derived columns, joins, and aggregations give you a path to prepare data before it reaches downstream systems. This matters when the raw source is not ready for direct reporting or storage.
You can set up triggers, incremental loads, variables, and expressions so pipelines run on schedule without manual work. That is the difference between a one-time demo and a usable data process.
The course shows how ADF fits with ADLS, Azure SQL Database, Synapse Analytics, Databricks, and Power BI overview points. You see how one orchestration tool can coordinate several services.
RBAC, Managed Identity, Key Vault, and network security considerations help you design data movement with access control in mind. This is useful in teams that care about permissions and production readiness.
Git integration, Azure DevOps, version control, release pipelines, and environment management help you understand how ADF moves from development to test and production. That is a practical skill for real team setups.
The course starts with data integration basics, ETL versus ELT, and ADF architecture, so new learners can follow the sequence without being thrown into advanced deployment work on day one. The later modules add transformation, triggers, monitoring, and security after the foundation is set.
Yes. The syllabus includes pipeline building, data movement, Mapping Data Flows, scheduling, monitoring, and CI/CD, and the course ends with real-time projects. That mix gives you practice with both the tool and the workflow around it.
Inventateq includes resume building and interview preparation as part of the course flow. The support is built around the actual role targets from the syllabus, so you can discuss ADF pipelines, Azure services, and project work with more clarity.
Yes. SQL, application support, testing, and related IT backgrounds often make the transition easier because the course already includes databases, data movement, monitoring, and troubleshooting. You do not need to arrive with prior ADF experience.
Yes, this page is for the online course format. The live sessions let you attend from anywhere and still follow the same module order, demos, and project work.
The syllabus is arranged across 12 modules, ending with real-time projects and interview preparation. The exact duration depends on the batch schedule, but the structure is designed to move from foundations to job-ready practice in a steady sequence.
You will work with Azure Data Factory, Azure Data Lake Storage, Azure SQL Database, Azure Databricks integration, Azure Synapse Analytics integration, Azure Key Vault, and Azure DevOps. These tools match the parts of the syllabus that matter most for real Azure data integration work.
Inventateq offers classroom training across multiple locations. Explore the branch nearest to you and check available batch timings.
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