DP-203T00-A: Data Engineering on Microsoft Azure

Live Online (VILT) & Classroom Corporate Training Course

Reasons to Choose

The Azure Data Engineering Course will prepare you to pass the Microsoft Azure Data Engineering Exam DP-203.

You will master creating data processing pipelines, ensuring data security, and using tools such as Data Factory, Databricks, and more.
Gain hands-on experience through real-time use cases and project work.
Learn from Microsoft-certified Azure Data Engineers.

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Course Overview:

In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.

Course Prerequisites

To have a successful learning experience in the DP-203T00: Data Engineering on Microsoft Azure course, it is recommended that you meet the following prerequisites:

– Basic understanding of data processing concepts and languages, such as SQL.

– Familiarity with cloud computing concepts, especially Microsoft Azure services.

– Some experience with data solutions, including both relational and non-relational data stores.

Course Objective

Grasp the fundamentals of data engineering on Azure, including data storage, streaming, and analytics.

DP: 203 Microsoft Azure Data engineer course outline

Module 1: Introduction to data engineering on Azure

Identify common data engineering tasks
Describe common data engineering concepts
Identify Azure services for data engineering

Module 2: Data Storage in Microsoft Azure

You will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for your data.
Design and implement data storage and data security
Design and develop data processing
Monitor and optimize data storage and data processing

Module 3: Introduction to Azure Synapse Apache Spark Pools

How to use Azure Synapse Analytics to build Data Warehouses using modern architecture patterns
How to describe the features and components of Azure Synapse Analytics
How to use Azure Synapse Analytics to build your analytical solutions in one place
How to use Azure Synapse Studio application to interact with the various components of Azure Synapse Analytics
Use Delta Lake in Azure Synapse Analytics

Module 4: Data warehousing using Azure Synapse Analytics

Analyze data in a relational data warehouse
Load data into a relational data warehouse

How to query Azure Cosmos DB with SQL Serverless/Apache Spark for Azure Synapse Analytics
How to configure and enable Azure Synapse Link to interact with Azure Cosmos DB

Module 5: ETL with Azure Synapse Analytics Pipelines

Build a data pipeline in Azure Synapse Analytics
Use Spark Notebooks in an Azure Synapse Pipeline

Module 6: Working with hybrid transactional and analytical processing (HTAP) Solutions using Azure Synapse Analytics

Plan hybrid transactional and analytical processing

How to query Azure Cosmos DB with SQL Serverless/Apache Spark for Azure Synapse Analytics
How to configure and enable Azure Synapse Link to interact with Azure Cosmos DB
How to use hybrid transactional and analytical processing to perform operational analytics with Azure Synapse Analytics

Implement Azure Synapse Link for SQL

Module 7: Implement a data streaming solution with Azure Stream Analytics

How to describe the concepts of event processing and streaming data and how this applies to Azure Stream Analytics

Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics
Visualize real-time data with Azure Stream Analytics and Power BI

Module 8: Data Governance

Introduction to Microsoft Purview

How to use Advanced Threat Protection to proactively monitor your system and describe the various ways to upload data to Data Lake Storage Gen 2

Integrate Microsoft Purview and Azure Synapse Analytics

Module 9: Microsoft Azure Databricks for Data Engineering

Explore Azure Databricks
Use Apache Spark in Azure Databricks

Run Azure Databricks notebooks in Azure Data Factory

The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

Data Engineers
Data Architects
Data Scientists
Database Administrators
IT Professionals with a focus on data solutions
Business Intelligence Professionals
Cloud Solution Architects

DP: 203 exam is part of the Microsoft Certified: Azure Data Engineer Associate Certification. It evaluates a candidate’s ability to design and implement data storage, processing, and security solutions on the Azure platform, as well as perform data integration and copying using Hive and Spark. Through real projects, students learn to design Azure data solutions, process data, and ensure data security.

The Azure Data Engineer Course will take up to 40 hours of instructor-led training.