Course Description

Course Overview

Microsoft Data Engineering on Microsoft Azure Training (DP-203) is an advanced training program designed for data engineers and data professionals to gain expertise in designing and implementing data solutions using Microsoft Azure. Participants will learn how to build and manage data pipelines, implement data storage and processing, and utilize Azure services for data integration and analytics.

Prerequisites

To excel in this course, participants should have a solid understanding of data engineering concepts, databases, and data processing. Familiarity with Microsoft Azure fundamentals and SQL will be essential. Prior experience with data warehousing, data integration, and data transformation will be highly beneficial.

Methodology

The course will be delivered through a combination of instructor-led lectures, practical demonstrations, hands-on labs, and interactive discussions. Participants will have access to dedicated Azure environments for hands-on practice, enabling them to apply the concepts learned in real-world scenarios. The course will be led by certified instructors with extensive experience in data engineering and Microsoft Azure solutions, ensuring a high-quality learning experience.

Course Outline

• Introduction to Data Engineering on Azure
• Understanding data engineering principles and practices
• Overview of Microsoft Azure data services and components
• Identifying data engineering challenges and solutions in Azure
• Building and Managing Data Pipelines with Azure Data Factory
• Creating data pipelines and data flow activities in Azure Data Factory
• Implementing data transformation and orchestration
• Scheduling and monitoring data pipelines in Azure
• Implementing Data Storage and Processing Solutions
• Designing and implementing data storage using Azure Blob Storage and Azure SQL Database
• Implementing Azure Data Lake Storage and Azure Synapse Analytics
• Integrating with Azure Cosmos DB and Azure HDInsight for data processing
• Data Integration and Orchestration with Azure Databricks
• Utilizing Azure Databricks for data integration and ETL processing
• Implementing data orchestration with Azure Databricks notebooks
• Managing and monitoring Azure Databricks clusters and jobs
• Implementing Data Streaming and Real-Time Analytics with Azure Stream Analytics
• Configuring data streaming and event ingestion with Azure Stream Analytics
• Implementing real-time analytics and insights with Azure Stream Analytics queries
• Integrating Azure Stream Analytics with Power BI and other Azure services

Outcome

Upon completion of this course, participants will be able to:

• Design and implement data solutions using Microsoft Azure data services.
• Build and manage data pipelines with Azure Data Factory.
• Utilize Azure Databricks for data integration and orchestration.
• Implement real-time analytics and streaming solutions with Azure Stream Analytics.

Labs

• Building Data Pipelines with Azure Data Factory
• Implementing Data Transformation with Azure Data Factory
• Implementing Data Storage with Azure Blob Storage and Azure SQL Database
• Implementing Azure Data Lake Storage and Azure Synapse Analytics
• Integrating with Azure Cosmos DB and Azure HDInsight for Data Processing
• Data Integration and Orchestration with Azure Databricks
• Implementing Real-Time Analytics with Azure Stream Analytics