Azure Data Engineering Training (DP-203)

Course 8595

  • Duration: 4 days
  • Language: English
  • Level: Intermediate

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.

Azure Data Engineering Training Delivery Methods

  • In-Person

  • Online

Azure Data Engineering Training Information

In this course, you will learn how to:

  • Explore compute and storage options for data engineering workloads in Azure.
  • Run interactive queries using serverless SQL pools.
  • Perform data Exploration and Transformation in Azure Databricks.
  • Explore, transform, and load data into the Data Warehouse using Apache Spark.
  • Ingest and load Data into the Data Warehouse.
  • Transform Data with Azure Data Factory or Azure Synapse Pipelines.
  • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines.
  • Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link.
  • Perform end-to-end security with Azure Synapse Analytics.
  • Perform real-time Stream Processing with Stream Analytics.
  • Create a Stream Processing Solution with Event Hubs and Azure Databricks.
  • Continue learning and face new challenges with after-course one-on-one instructor coaching.

Training Prerequisites

Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions. Specifically completing:

Certification Information

This class does prepare an individual to take the Microsoft Certified Exam DP-203.

Azure Data Engineering Training Outline

Learning objectives

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

Learning objectives

  • Describe the key features and benefits of Azure Data Lake Storage Gen2
  • Enable Azure Data Lake Storage Gen2 in an Azure Storage account
  • Compare Azure Data Lake Storage Gen2 and Azure Blob storage
  • Describe where Azure Data Lake Storage Gen2 fits in the stages of analytical processing
  • Describe how Azure data Lake Storage Gen2 is used in common analytical workloads

Learning objectives 

  • Identify the business problems that Azure Synapse Analytics addresses.
  • Describe core capabilities of Azure Synapse Analytics.
  • Determine when to use Azure Synapse Analytics.

Learning objectives

  • Identify capabilities and use cases for serverless SQL pools in Azure Synapse Analytics
  • Query CSV, JSON, and Parquet files using a serverless SQL pool
  • Create external database objects in a serverless SQL pool

Learning objectives

  • Use a CREATE EXTERNAL TABLE AS SELECT (CETAS) statement to transform data.
  • Encapsulate a CETAS statement in a stored procedure.
  • Include a data transformation stored procedure in a pipeline.

Learning objectives

  • Understand lake database concepts and components
  • Describe database templates in Azure Synapse Analytics
  • Create a lake database

Learning objectives

  • Identify core features and capabilities of Apache Spark.
  • Configure a Spark pool in Azure Synapse Analytics.
  • Run code to load, analyze, and visualize data in a Spark notebook.

Learning objectives

  • Use Apache Spark to modify and save dataframes
  • Partition data files for improved performance and scalability.
  • Transform data with SQL

Learning objectives

  • Describe core features and capabilities of Delta Lake.
  • Create and use Delta Lake tables in a Synapse Analytics Spark pool.
  • Create Spark catalog tables for Delta Lake data.
  • Use Delta Lake tables for streaming data.
  • Query Delta Lake tables from a Synapse Analytics SQL pool.

Learning objectives 

  • Design a schema for a relational data warehouse.
  • Create fact, dimension, and staging tables.
  • Use SQL to load data into data warehouse tables.
  • Use SQL to query relational data warehouse tables.

Learning objectives 

  • Load staging tables in a data warehouse
  • Load dimension tables in a data warehouse
  • Load time dimensions in a data warehouse
  • Load slowly-changing dimensions in a data warehouse
  • Load fact tables in a data warehouse
  • Perform post-load optimizations in a data warehouse

Learning objectives

  • Describe core concepts for Azure Synapse Analytics pipelines.
  • Create a pipeline in Azure Synapse Studio.
  • Implement a data flow activity in a pipeline.
  • Initiate and monitor pipeline runs.

Learning objectives

  • Describe notebook and pipeline integration.
  • Use a Synapse notebook activity in a pipeline.
  • Use parameters with a notebook activity.

Learning objectives

  • Describe Hybrid Transactional / Analytical Processing patterns.
  • Identify Azure Synapse Link services for HTAP.

Learning objectives

  • Configure an Azure Cosmos DB Account to use Azure Synapse Link.
  • Create an analytical store-enabled container.
  • Create a linked service for Azure Cosmos DB.
  • Analyze linked data using Spark.
  • Analyze linked data using Synapse SQL.

Learning objectives

  • Understand key concepts and capabilities of Azure Synapse Link for SQL.
  • Configure Azure Synapse Link for Azure SQL Database.
  • Configure Azure Synapse Link for Microsoft SQL Server.

Learning objectives 

  • Understand data streams.
  • Understand event processing.
  • Understand window functions.
  • Get started with Azure Stream Analytics.

Learning objectives

  • Describe common stream ingestion scenarios for Azure Synapse Analytics.
  • Configure inputs and outputs for an Azure Stream Analytics job.
  • Define a query to ingest real-time data into Azure Synapse Analytics.
  • Run a job to ingest real-time data, and consume that data in Azure Synapse Analytics.

Learning objectives 

  • Configure a Stream Analytics output for Power BI.
  • Use a Stream Analytics query to write data to Power BI.
  • Create a real-time data visualization in Power BI.

Learning objectives

  • Evaluate whether Microsoft Purview is appropriate for data discovery and governance needs.
  • Describe how the features of Microsoft Purview work to provide data discovery and governance.

Learning objectives

  • Catalog Azure Synapse Analytics database assets in Microsoft Purview.
  • Configure Microsoft Purview integration in Azure Synapse Analytics.
  • Search the Microsoft Purview catalog from Synapse Studio.
  • Track data lineage in Azure Synapse Analytics pipelines activities.

Learning objectives 

  • Provision an Azure Databricks workspace.
  • Identify core workloads and personas for Azure Databricks.
  • Describe key concepts of an Azure Databricks solution.

Learning objectives 

  • Describe key elements of the Apache Spark architecture.
  • Create and configure a Spark cluster.
  • Describe use cases for Spark.
  • Use Spark to process and analyze data stored in files.
  • Use Spark to visualize data.

Learning objectives

  • Describe how Azure Databricks notebooks can be run in a pipeline.
  • Create an Azure Data Factory linked service for Azure Databricks.
  • Use a Notebook activity in a pipeline.
  • Pass parameters to a notebook.

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Azure Data Engineering Training FAQs

Microsoft Azure Data Engineering Training (DP-203) teaches participants how to design and implement data solutions using Azure services.

This course is intended for data professionals who want to learn how to use Azure services to design and implement data solutions. It is recommended for those with experience in data processing and transformation.

The course covers a variety of topics related to data engineering, including designing data storage solutions, ingesting and processing data, implementing data security, and monitoring and optimizing data solutions.

The course covers a variety of topics related to data engineering, including designing data storage solutions, ingesting and processing data, implementing data security, and monitoring and optimizing data solutions.

Participants will gain skills in designing and implementing data solutions using Azure services, as well as in data processing and transformation, data security, and monitoring and optimization.

The course is live and instructor-led over four days.

The course is online and consists of modules that include videos, readings, and hands-on labs. Participants can work through the course at their own pace.

Yes, participants who complete the course can take the DP-203 exam to earn the Microsoft Certified: Azure Data Engineer Associate certification.

Participants should have experience working with data, including data processing and transformation. Some familiarity with Azure services is also helpful. Specifically, you should have completed both Learning Tree course 8566, Microsoft Azure Fundamentals Training (AZ-900T00) and Learning Tree course 8586, Microsoft Azure Data Fundamentals Training (DP-900).

Yes, Exam DP-203 replaced both Exam DP-200 and Exam DP-201, which retired on June 30, 2021.

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