(Power BI – 35 Hours
SQL – 10 Hours
ADF – 15 Hours)
In this course, students will learn about, and apply, the various methods and best practices that are in line with business and technical requirements for Preparing, modeling, visualizing, and analyzing data.
One will also learn about the management aspects of Power BI, including workspaces and datasets, and then learn how to share, distribute, and appropriately secure Power BI assets
Overview of Azure Data Factory and its key features
Understanding the data integration and data orchestration concepts
Exploring the components and architecture of Azure Data Factory
Setting up an Azure subscription and creating an Azure Data Factory instance
Configuring data movement activities in Azure Data Factory
Working with different data sources and destinations
Data ingestion techniques and considerations
Implementing copy activities for data transfer
Introduction to data transformation in Azure Data Factory
Working with data transformation activities like mapping, filtering, and aggregating data
Implementing data flow activities using Azure Data Factory Mapping Data Flows
Understanding data wrangling and data profiling concepts
Exploring common data integration patterns and scenarios
Designing efficient and scalable data integration workflows
Implementing data partitioning and parallelism for optimal performance
Security and compliance considerations in Azure Data Factory
Real-world use cases and case studies of Azure Data Factory implementation
Examining best practices for data integration projects
Real-time project 1
The course is a comprehensive training program that covers Power BI for data visualization, SQL for database management, and Azure Data Factory for data integration and ETL (Extract, Transform, Load) processes.
While there are no strict prerequisites, a basic understanding of databases and familiarity with data concepts can be beneficial. Knowledge of SQL is helpful but not mandatory.
The Power BI section covers data visualization, creating reports and dashboards, data modeling, DAX (Data Analysis Expressions), and connecting to various data sources.
Yes, the course is designed to accommodate learners at various skill levels, including beginners. It provides a strong foundation and gradually builds advanced skills.
The course includes hands-on projects and real-world scenarios that simulate practical industry situations, allowing participants to apply what they’ve learned.
The SQL section covers database design, querying data, data manipulation (INSERT, UPDATE, DELETE), stored procedures, and database management.
Yes, participants typically receive access to course materials, video recordings, and resources for future reference.
Azure Data Factory is a cloud-based ETL service. The course covers ADF concepts, data pipelines, data transformation, and connecting to various data sources.
Yes, the course includes practical exercises and projects related to Azure Data Factory, allowing participants to gain hands-on experience in creating data workflows.
Yes, the course offers guidance on resume preparation, interview tips, and include mock interview sessions.
Many training programs offer a course completion certificate, which can be a valuable addition to your resume.
Yes, most online courses include live sessions with instructors, Q&A opportunities, and forums for interaction with fellow participants.
Azure data engineering skills are in high demand in today’s job market. As more and more companies move their data to the cloud, the need for skilled data engineers who can design, implement, and manage data solutions on Azure is growing rapidly.
You can enroll by visiting the Growing Tree Technologies website and following the enrollment process or contacting the support team for guidance.
Post-training assistance includes access to updated course materials, resources, and support for any questions or challenges you may encounter while applying your skills in real-world scenarios.
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