Data Engineering on Microsoft Azure (DP-203)


Course Number: MOC-DP-203

Duration: 4 days (26 hours)

Format: Live, hands-on

Azure Training Overview

This Microsoft official DP-203 course, Data Engineering on Microsoft Azure, teaches attendees the patterns and practices of real-time analytical solutions using Azure. Participants learn how to interactively explore data stored in files in a data lake, transform data, monitor and analyze the performance of analytical systems, and much more.

Location and Pricing

This course is taught as a private course in-person or online for teams of 3 or more. To receive a quote for online corporate training, please contact us.

In addition, some Microsoft courses are available as live, online classes for individuals.

Objectives

  • 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

Prerequisites

All students must have knowledge of cloud computing, core data concepts, and professional experience with data solutions. Attendees should have taken AZ-900: Azure Fundamentals and DP-900: Microsoft Azure Data Fundamentals, or have equivalent experience. 

Outline

Expand All | Collapse All

Explore Compute and Storage Options for Data Engineering Workloads
  • Introduction to Azure Synapse Analytics
  • Describe Azure Databricks
  • Introduction to Azure Data Lake storage
  • Describe Delta Lake architecture
  • Work with data streams by using Azure Stream Analytics
Run Interactive Queries using Azure Synapse Analytics Serverless SQL Pools
  • Explore Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools
Data Exploration and Transformation in Azure Databricks
  • Describe Azure Databricks
  • Read and write data in Azure Databricks
  • Work with DataFrames in Azure Databricks
  • Work with DataFrames advanced methods in Azure Databricks
Explore, Transform, and Load Data into the Data Warehouse using Apache Spark
  • Understand big data engineering with Apache Spark in Azure Synapse Analytics
  • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics
Ingest and Load Data into the Data Warehouse
  • Use data loading best practices in Azure Synapse Analytics
  • Petabyte-scale ingestion with Azure Data Factory
Transform Data with Azure Data Factory or Azure Synapse Pipelines
  • Data integration with Azure Data Factory or Azure Synapse Pipelines
  • Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
Orchestrate Data Movement and Transformation in Azure Synapse Pipelines
  • Orchestrate data movement and transformation in Azure Data Factory
End-to-End Security with Azure Synapse Analytics
  • Secure a data warehouse in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for sensitive data
Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark pools
  • Query Azure Cosmos DB with serverless SQL pools
Real-time Stream Processing with Stream Analytics
  • Enable reliable messaging for Big Data applications using Azure Event Hubs
  • Work with data streams by using Azure Stream Analytics
  • Ingest data streams with Azure Stream Analytics
Create a Stream Processing Solution with Event Hubs and Azure Databricks
  • Process streaming data with Azure Databricks structured streaming
Conclusion

Training Materials:

All Microsoft training students receive Microsoft official courseware.

Software Requirements:

Attendees will not need to install any software on their computer for this class. The class will be conducted in a remote environment that Accelebrate will provide; students will only need a local computer with a web browser and a stable Internet connection. Any recent version of Microsoft Edge, Mozilla Firefox, or Google Chrome will be fine.



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