Category : Trainings Course Content | Sub Category : Trainings Course Content | By Runner Dev Last updated: 2023-12-05 14:13:16 Viewed : 273

Azure Data Engineer course involves covering a range of topics related to Azures data services and tools for designing, implementing, and managing data solutions. Below is an outline for an Azure Data Engineer course that covers key concepts, services, and best practices.

Module 1: Introduction to Azure Data Platform

1. Overview of Azure Data Services:

   • Introduction to Azure data services and tools.
   • Overview of the Azure data ecosystem.

2. Azure Data Architecture:

   • Understanding the architecture of Azure data solutions.
   • Components and their interactions.

Module 2: Azure Data Storage

3. Azure Storage Services:

   • Overview of Azure Storage options (Blobs, Tables, Queues, Files).
   • Choosing the right storage solution for different scenarios.

4. Azure SQL Database:

   • Introduction to Azure SQL Database.
   • Creating and managing relational databases in the cloud.

5. Azure Cosmos DB:

   • Overview of Azure Cosmos DB (NoSQL database).
   • Designing and implementing scalable and globally distributed databases.

Module 3: Data Integration and ETL

6. Azure Data Factory:

   • Introduction to Azure Data Factory.
   • Building data pipelines for ETL (Extract, Transform, Load).

7. Azure Databricks:

   • Overview of Azure Databricks for big data processing.
   • Collaborative and scalable Spark-based analytics.

8. Azure Synapse Analytics (formerly SQL Data Warehouse):

   • Introduction to Azure Synapse Analytics.
   • Building data warehouses and analytical solutions.

Module 4: Data Orchestration and Workflow

9. Azure Logic Apps:

   • Building workflows and orchestrating data processes.
   • Integrating with other Azure services.

10. Azure Stream Analytics:

    • Processing real-time data streams.
    • Building and deploying stream analytics solutions.

Module 5: Data Governance and Security

11. Azure Data Catalog:

    • Implementing data cataloging and discovery.
    • Managing metadata and data lineage.

12. Azure Purview:

    • Introduction to Azure Purview for data governance.
    • Cataloging and discovering data assets across the organization.

13. Data Security and Compliance:

    • Implementing security measures in Azure data solutions.
    • Ensuring compliance with industry regulations.

Module 6: Monitoring and Optimization

14. Azure Monitor and Log Analytics:

    • Monitoring Azure data solutions.
    • Collecting and analyzing telemetry data.

15. Performance Optimization:

    • Techniques for optimizing performance in Azure data solutions.
    • Scaling and tuning for efficiency.

Module 7: Advanced Analytics and Machine Learning

16. Azure Machine Learning:

    • Overview of Azure Machine Learning.
    • Building and deploying machine learning models.

17. Azure Cognitive Services:

    • Introduction to Azure Cognitive Services for AI capabilities.
    • Integrating cognitive services into data solutions.

Module 8: Case Studies and Real-world Scenarios

18. Real-world Data Engineering Projects:
    • Examining real-world use cases of Azure data solutions.
    • Lessons learned and best practices from successful implementations.

Module 9: Data Engineer Certification Preparation

19. Azure Data Engineer Certification Overview:
    • Overview of relevant Microsoft Azure data engineering certifications.
    • Exam preparation strategies and resources.

Module 10: Future Trends and Emerging Technologies

20. Future of Azure Data Engineering:
    • Exploring emerging trends and technologies in the Azure data ecosystem.
    • Preparing for continuous learning and adaptation.

This course outline provides a structured path for learning Azure Data Engineering, covering essential services, concepts, and best practices. Depending on the audiences proficiency level, the depth of coverage in each module can be adjusted. Practical hands-on exercises, projects, and real-world examples should be included to reinforce theoretical knowledge.

The Azure Data Engineer course is suitable for a variety of individuals who want to acquire skills and knowledge in designing, implementing, and managing data solutions using Microsoft Azure services. The course is particularly relevant for the following groups:

1. Data Engineers:

   • Data engineers who want to enhance their expertise in building and managing data solutions in the Azure cloud.

2. Database Administrators:

   • Database administrators looking to leverage Azure services for scalable and efficient data storage and management.

3. Data Scientists:

   • Data scientists interested in integrating Azure data services for advanced analytics and machine learning.

4. ETL Developers:

   • ETL (Extract, Transform, Load) developers aiming to design and implement data integration solutions using Azure Data Factory and related services.

5. Business Intelligence Professionals:

   • BI professionals seeking to build and optimize data warehouses, implement analytics solutions, and ensure data governance in Azure.

6. Big Data Engineers:

   • Professionals working with big data who want to use Azure services like Azure Databricks and Azure Synapse Analytics for large-scale data processing.

7. IT Professionals and System Administrators:

   • IT professionals and system administrators interested in understanding how to deploy, monitor, and manage Azure data solutions.

8. Developers:

   • Developers who want to extend their skills to include designing and implementing data solutions using Azure data services.

9. Technical Leads and Architects:

   • Technical leads and architects responsible for designing and overseeing the implementation of data solutions in Azure.

10. Students and Enthusiasts:

    • Students pursuing degrees in computer science, data science, or related fields, as well as technology enthusiasts, who want to gain practical experience with Azure data services.

Prerequisites for Learning Azure Data Engineer Course:

• Basic Understanding of Data Concepts:

  • Familiarity with fundamental data concepts, including databases, data processing, and data integration.

• Programming and Scripting Skills (Optional):

  • While not mandatory, having basic programming skills, especially in languages like SQL and Python, can be beneficial.

• Azure Fundamentals (Optional):

  • A foundational understanding of Azure fundamentals, including Azure Portal navigation and basic services, can be helpful.

• Interest and Motivation:

  • Having an interest in data engineering, cloud computing, and the desire to learn and apply new technologies is essential for a successful learning experience.

The Azure Data Engineer course is designed to accommodate a diverse audience with varying levels of expertise. It provides a structured path for learning Azure data services and their application in building end-to-end data solutions. Practical hands-on exercises, projects, and real-world scenarios are often included in the course to reinforce theoretical knowledge and promote practical application.