Microsoft Certified: Azure Data Engineer Associate (DP203)

In this four-day Microsoft certified course, students will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.

This training is a comprehensive preparation for the DP-203: Data Engineering on Microsoft Azure exam to earn the Microsoft Certified: Azure Data Engineer Associate certification.


Applicable solutions

Public class

Virtual classroom
Planned datePlanned date
October 23 2023
4 days / 28 hours

Private class

On site / Virtual class
Minimum no. of participants: 5
4 days / 28 hours
Price on request
English or French
Training plan: 

Data Engineering on Microsoft Azure (DP-203T00)

Module 1: Introduction to data engineering on Azure

  • What is data engineering
  • Important data engineering concepts
  • Data engineering in Microsoft Azure

Module 2: Introduction to Azure Data Lake Storage Gen2

  • Understand Azure Data Lake Storage Gen2
  • Enable Azure Data Lake Storage Gen2 in Azure Storage
  • Compare Azure Data Lake Store to Azure Blob storage
  • Understand the stages for processing big data
  • Use Azure Data Lake Storage Gen2 in data analytics workloads

Module 3: Introduction to Azure Synapse Analytics

  • What is Azure Synapse Analytics
  • How Azure Synapse Analytics works
  • When to use Azure Synapse Analytics

Module 4: Use Azure Synapse serverless SQL pool to query files in a data lake

  • Understand Azure Synapse serverless SQL pool capabilities and use cases
  • Query files using a serverless SQL pool
  • Create external database objects

Module 5: Use Azure Synapse serverless SQL pools to transform data in a data lake

  • Transform data files with the CREATE EXTERNAL TABLE AS SELECT statement
  • Encapsulate data transformations in a stored procedure
  • Include a data transformation stored procedure in a pipeline

Module 6: Create a lake database in Azure Synapse Analytics

  • Understand lake database concepts
  • Explore database templates
  • Create a lake database
  • Use a lake database

Module 7: Analyze data with Apache Spark in Azure Synapse Analytics

  • Get to know Apache Spark
  • Use Spark in Azure Synapse Analytics
  • Analyze data with Spark
  • Visualize data with Spark

Module 8: Transform data with Spark in Azure Synapse Analytics

  • Modify and save dataframes
  • Partition data files
  • Transform data with SQL

Module 9: Use Delta Lake in Azure Synapse Analytics

  • Understand Delta Lake
  • Create Delta Lake tables
  • Create catalog tables
  • Use Delta Lake with streaming data
  • Use Delta Lake in a SQL pool

Module 10: Analyze data in a relational data warehouse

  • Design a data warehouse schema
  • Create data warehouse tables
  • Load data warehouse tables
  • Query a data warehouse

Module 11: Load data into a relational data warehouse

  • Load staging tables
  • Load dimension tables
  • Load time dimension tables
  • Load slowly changing dimensions
  • Load fact tables
  • Perform post load optimization

Module 12: Build a data pipeline in Azure Synapse Analytics

  • Understand pipelines in Azure Synapse Analytics
  • Create a pipeline in Azure Synapse Studio
  • Define data flows
  • Run a pipeline

Module 13: Use Spark Notebooks in an Azure Synapse Pipeline

  • Understand Synapse Notebooks and Pipelines
  • Use a Synapse notebook activity in a pipeline
  • Use parameters in a notebook

Module 14: Plan hybrid transactional and analytical processing using Azure Synapse Analytics

  • Understand hybrid transactional and analytical processing patterns
  • Describe Azure Synapse Link

Module 15: Implement Azure Synapse Link with Azure Cosmos DB

  • Enable Cosmos DB account to use Azure Synapse Link
  • Create an analytical store enabled container
  • Create a linked service for Cosmos DB
  • Query Cosmos DB data with Spark
  • Query Cosmos DB with Synapse SQL

Module 16: Implement Azure Synapse Link for SQL

  • What is Azure Synapse Link for SQL?
  • Configure Azure Synapse Link for Azure SQL Database
  • Configure Azure Synapse Link for SQL Server 2022

Module 17: Get started with Azure Stream Analytics

  • Understand data streams
  • Understand event processing
  • Understand window functions

Module 18: Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics

  • Stream ingestion scenarios
  • Configure inputs and outputs
  • Define a query to select, filter, and aggregate data
  • Run a job to ingest data

Module 19: Visualize real-time data with Azure Stream Analytics and Power BI

  • Use a Power BI output in Azure Stream Analytics
  • Create a query for real-time visualization
  • Create real-time data visualizations in Power BI

Module 20: Introduction to Microsoft Purview

  • What is Microsoft Purview?
  • How Microsoft Purview works
  • When to use Microsoft Purview

Module 21: Explore Azure Databricks

  • Get started with Azure Databricks
  • Identify Azure Databricks workloads
  • Understand key concepts

Module 22: Use Apache Spark in Azure Databricks

  • Get to know Spark
  • Create a Spark cluster
  • Use Spark in notebooks
  • Use Spark to work with data files
  • Visualize data

Module 23: Run Azure Databricks Notebooks with Azure Data Factory

  • Understand Azure Databricks notebooks and pipelines
  • Create a linked service for Azure Databricks
  • Use a Notebook activity in a pipeline
  • Use parameters in a notebook
  • One FREE attendance to the Microsoft Certified: Azure AI Fundamentals (AI900) training
  • One year access to the class recording
  • 180 days access to the lab environment after class
  • One voucher to take the exam
  • Up to date courseware with Microsoft Learn
  • Microsoft course achievement badge

Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions, or having completed the following trainings:

Certification information: 

Exam characteristics:

  • Exam code: DP-203
  • Cost: $0 (included in your training)
  • Skills measured
    • Design and implement data storage 
    • Design and develop data processing 
    • Design and implement data security 
    • Monitor and optimize data storage and data processing 
  • All details... 

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