Course Summary

This Data Engineering on Microsoft Azure (DP-203) course introduces the ins and outs of working with relational, non-relational, and data warehouse solutions in the Azure platform with a focus on providing the knowledge needed to make the right decisions for implementation in an organization.

The goal of this course is to provide you with all the tools you need to prepare for the DP-203 Data engineering on Microsoft Azure exam — including text explanations, video demos, lab activities, self-assessment questions, and a practice exam— to increase your chances of passing the exam on your first try.


Methodology:

180-day access to:

  • Lessons
  • Video learning
  • Labs for Data Engineering on Microsoft Azure (DP-203).
  • MeasureUp Practice Test for Data Engineering on Microsoft Azure (DP-203). Practice Mode with remediation and Certification mode to simulate the test day experience.

Duration: 

Approximately 40 hours of primary course content. Each learner will progress at their own rate.

Audience: 

Data engineers, system engineers, IT managers developers, database administrators, and cloud architects who have very little or no knowledge about Azure offerings and architecture.

Prerequisites:
  • Six months or less of experience with Azure offerings and architecture.
  • Limited consolidated experience about on-premises environments and technologies.
Course Outline: 

Upon successful completion of this course, students should be able to:

  • Understand Azure data solutions.
  • Implement data-storage solutions.
  • Manage and develop data processing for Azure data solutions.
  • Monitor and optimize data solutions.

Lesson 1: Understand Azure data solutions (2 hours)

  • Data-storage concepts (36 minutes)
    • Types of data
    • Understand data storage
    • Data storage in Azure
  • Data-processing concepts (48 minutes)
    • Batch processing
    • Stream processing
    • Lambda and kappa architectures
    • Azure technologies used for data processing
  • Use cases (36 minutes)
    • Advanced analytics
    • Hybrid ETL with existing on-premises SSIS and Azure Data Factory
    • Internet of things architecture
  • Summary
  • Case Study
  • Quiz


Lesson 2: Implement data-storage solutions (2 hours and 36 minutes)

  • Implement non-relational data stores (1 hour and 24 minutes)
    • Implement a solution that uses Cosmos DB, Azure Data Lake Storage Gen2, or Blob storage
    • Implement partitions
    • Implement a consistency model in Cosmos DB
    • Provision a non-relational data store
    • Provision an Azure Synapse Analytics workspace
    • Provide access to data to meet security requirements
    • Implement for high availability, disaster recovery, and global distribution
  • Implement relational data stores (48 minutes)
    • Provide access to data to meet security requirements
    • Implement for high availability and disaster recovery
    • Implement data distribution and partitions for Azure Synapse Analytics
    • Implement PolyBase
  • Manage data security (24 minutes)
    • Implement dynamic data masking
    • Encrypt data at rest and in motion
  • Summary
  • Case Study
  • Quiz


Lesson 3: Manage and develop data processing for Azure Data Solutions (2 hours and 12 minutes)

  • Batch data processing (1 hour and 24 minutes)
    • Develop batch-processing solutions using Azure Data Factory and Azure Databricks
    • Implement the Integration Runtime for Azure Data Factory
    • Create pipelines, activities, linked services, and datasets
    • Create and schedule triggers
    • Implement Azure Databricks clusters, notebooks, jobs, and autoscaling
    • Ingest data into Azure Databricks
    • Ingest and process data using Azure Synapse Analytics
  • Streaming data (48 minutes)
    • Stream-transport and processing engines
    • Implement event processing using Stream Analytics
    • Configure input and output
    • Select the appropriate built-in functions
  • Summary
  • Case Study
  • Quiz


Lesson 4: Monitor and optimize data solutions (4 hours and 36 minutes)

  • Monitor data storage (1 hour and 36 minutes)
    • Monitor and Azure SQL Database
    • Monitor Azure SQL Database using DMV
    • Monitor Blob storage
    • Implement Azure Data Lake Storage monitoring
    • Implement Azure Synapse Analytics monitoring
    • Implement Cosmos DB monitoring
    • Configure Azure Monitor alerts
    • Audit with Azure Log Analytics
  • Monitor data processing (1 hour and 12 minutes)
    • Monitor Azure Data Factory pipelines
    • Monitor Azure Databricks
    • Monitor Azure Stream Analytics
    • Monitor Azure Synapse Analytics
    • Configure Azure
    • Monitor alerts Audit with Azure Log Analytics
  • Optimize Azure data solutions (1 hour and 48 minutes)
    • Troubleshoot data-partitioning bottlenecks
    • Partitioning considerations
    • Partition Azure SQL Database
    • Partition Azure Blob storage
    • Partition Cosmos DB
    • Optimize Azure Data Lake Storage Gen2
    • Optimize Azure Stream Analytics
    • Optimize Azure Synapse Analytics
    • Manage the data life cycle
  • Summary
  • Case Study
  • Quiz