Course Overview
Build a career in cloud data engineering with the Azure Data Engineer Training Course at VisibleCampus . This course is designed to help students and IT professionals learn how to design, build, manage, and optimize data solutions using Microsoft Azure cloud services. You will gain hands-on experience with data storage, data processing, ETL pipelines, data integration, big data solutions, and cloud analytics. The course covers essential Azure services such as Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure SQL Database, Data Lake Storage, Power BI integration, and real-world data workflows. Through practical assignments and live projects, learners will understand how organizations handle large-scale data processing, migration, transformation, and reporting in cloud environments.
Highlights
- Hands-on training with real-world projects
- Learn SQL, Python, and Azure tools
- Flexible batch schedules to fit your life
- Expert trainers with industry experience
- Job-ready skills to advance your career
- Certification support included
Learning Outcomes
- Master SQL for data manipulation and analysis
- Utilize Python for data processing tasks
- Implement Azure Data Bricks for big data solutions
- Create data pipelines using Azure Data Factory
- Manage and store data with Azure Data Lake
- Optimize data workflows for efficiency
Course Curriculum
- Overview of Azure Data Services
- Roles of a Data Engineer
- Understanding Data Workflows
- Azure Blob Storage
- Azure Data Lake Storage
- Choosing the Right Storage
- Creating Pipelines
- Data Movement Techniques
- Triggers and Scheduling
- Data Flow Transformations
- Mapping Data Flows
- Debugging Pipelines
- SQL Database Architecture
- Creating SQL Databases
- Managing SQL Database Security
- Complex Joins
- Window Functions
- Stored Procedures
- Introduction to Data Warehousing
- Star and Snowflake Schema
- ETL vs ELT Processes
- Synapse Studio
- Data Integration
- Serverless SQL Pools
- Data Lake Architecture
- Access Control in Data Lakes
- Performance Optimization
- Real-Time Data Processing
- Input and Output Configuration
- Query Language Basics
- Understanding Data Models
- Dimensional Modeling
- Normalization vs Denormalization
- Introduction to Azure Functions
- Event-Driven Architecture
- Function Triggers and Bindings
- Data Classification
- Role-Based Access Control
- Data Encryption Techniques
- Azure Monitor Overview
- Log Analytics
- Setting Up Alerts
- Databricks Architecture
- Creating Notebooks
- Running Spark Jobs
- Spark DataFrames
- RDD vs DataFrames
- Performance Tuning
- MLlib Overview
- Building ML Models
- Model Evaluation
- Creating ML Workspaces
- Data Preparation
- Model Deployment
- Azure Logic Apps
- Service Bus
- Event Grid
- Design Patterns
- Error Handling
- Optimization Techniques
- Data Masking
- Auditing Practices
- Secure Data Access
- U-SQL Basics
- Data Source Integration
- Job Scheduling
- Horizontal vs Vertical Scaling
- Load Balancing
- Caching Strategies
- Using Azure Event Hubs
- Stream Processing Patterns
- Use Cases
- Connecting to Azure Data Sources
- Creating Reports
- Dashboard Design Best Practices
- Designing Data Pipelines
- Use of APIs
- Integration Testing
- Understanding DataOps
- CI/CD for Data Pipelines
- Tools Overview
- Migration Planning
- Data Transfer Services
- Post-Migration Validation
- RESTful Services in Azure
- API Management
- Securing APIs
- Data Discovery
- Managing Metadata
- Data Lineage Tracking
- Data Quality Metrics
- Data Cleansing Techniques
- Monitoring Data Quality
- Defining Project Scope
- Building Pipelines
- Testing and Validation
- Data Lifecycle Management
- Access Patterns
- Cost Optimization Techniques
- Data Ingestion Techniques
- KQL Basics
- Dashboards Creation
- Data Stewardship
- Policy Management
- Compliance Requirements
- Project Planning
- Implementation Steps
- Final Presentation
- Key Technologies Recap
- Common Pitfalls
- Best Practices
- Exam Structure
- Study Materials
- Practice Questions
- Common Errors in Data Pipelines
- Debugging Techniques
- Using Logs Effectively
- Latest Azure Features
- Future Roadmap
- Community Resources
- Azure VNET Basics
- Interconnecting Services
- Security Considerations
- Project Review
- Feedback Sessions
- Final Adjustments
- Project Showcase
- Peer Feedback
- Lessons Learned
- Job Market Trends
- Building a Professional Network
- Resume Tips
- Key Takeaways
- Future Learning Paths
- Course Feedback
Frequently Asked Questions
A basic understanding of programming and database concepts is recommended. Familiarity with SQL will be beneficial.
Yes, upon successful completion, you will receive a certification that validates your skills as an Azure Data Engineer.
Graduates often secure roles as Data Engineers, Data Analysts, or BI Developers in various industries, leveraging their new skills.
We offer flexible batch schedules throughout the year to accommodate different learning paces and availability.
If you cancel within the first week of the course, a full refund is available. Please refer to our terms for more details.
Our team provides ongoing support through Q&A sessions, access to resources, and weekly check-ins to ensure your success.
Ready to start Azure Data Engineer?
Talk to a counselor and find out which batch fits your schedule and goals.