Course Code: 5794

Azure Data Engineer Associate (DP-200 & DP-201)

Class Dates:
8/9/2021
Length:
5 Days
Cost:
$2895.00
Class Time:
Technology:
Virtualization
Delivery:
Instructor-Led Training, Virtual Instructor-Led Training

Overview

  • Course Overview
  • The students will design various data platform technologies into solutions that are in line with business and technical requirements. This can include on-premises, cloud, and hybrid data scenarios which incorporate relational, No-SQL or Data Warehouse data. They will also learn how to design process architectures using a range of technologies for both streaming and batch data.

    The students will also explore how to design data security including data access, data policies and standards. They will also design Azure data solutions which includes the optimization, availability and disaster recovery of big data, batch processing and streaming data solutions.
    EXAMS
    : DP-200: Implementing an Azure Data Solution
    DP-201: Designing an Azure Data Solution

Prerequisites

Course Details

  • PART 1, Module 1: Azure for the Data Engineer
  • Explain the evolving world of data
  • Survey the services in the Azure Data Platform
  • Identify the tasks that are performed by a Data Engineer
  • Describe the use cases for the cloud in a Case Study
  • Lab : Azure for the Data Engineer
  • Identify the evolving world of data
  • Determine the Azure Data Platform Services
  • Identify tasks to be performed by a Data Engineer
  • Finalize the data engineering deliverables
  • PART 1, Module 2: Working with Data Storage
  • Choose a data storage approach in Azure
  • Create an Azure Storage Account
  • Explain Azure Data Lake storage
  • Upload data into Azure Data Lake
  • Lab : Working with Data Storage
  • Choose a data storage approach in Azure
  • Create a Storage Account
  • Explain Data Lake Storage
  • Upload data into Data Lake Store
  • PART 1, Module 3: Enabling Team Based Data Science with Azure Databricks
  • Explain Azure Databricks and Machine Learning Platforms
  • Describe the Team Data Science Process
  • Provision Azure Databricks and workspaces
  • Perform data preparation tasks
  • Lab : Enabling Team Based Data Science with Azure Databricks
  • Explain Azure Databricks and Machine Learning Platforms
  • Describe the Team Data Science Process
  • Provision Azure Databricks and Workspaces
  • Perform Data Preparation Tasks
  • PART 1, Module 4: Building Globally Distributed Databases with Cosmos DB
  • Create an Azure Cosmos DB database built to scale
  • Insert and query data in your Azure Cosmos DB database
  • Provision a .NET Core app for Cosmos DB in Visual Studio Code
  • Distribute your data globally with Azure Cosmos DB
  • PART 1, Module 5: Working with Relational Data Stores in the Cloud
  • SQL Database and SQL Data Warehouse
  • Provision an Azure SQL database to store data
  • Provision and load data into Azure SQL Data Warehouse
  • Lab : Working with Relational Data Stores in the Cloud
  • PART 1, Module 6: Performing Real-Time Analytics with Stream Analytics
  • Explain data streams and event processing
  • Querying streaming data using Stream Analytics
  • How to process data with Azure Blob and Stream Analytics
  • How to process data with Event Hubs and Stream Analytics
  • Lab : Performing Real-Time Analytics with Stream Analytics
  • Explain data streams and event processing
  • Querying streaming data using Stream Analytics
  • Process data with Azure Blob and Stream Analytics
  • Process data with Event Hubs and Stream Analytics
  • PART 1, Module 7: Orchestrating Data Movement with Azure Data Factory
  • Explain how Azure Data Factory works
  • Create Linked Services and datasets
  • Create pipelines and activities
  • Azure Data Factory pipeline execution and triggers
  • Lab : Orchestrating Data Movement with Azure Data Factory
  • Explain how Data Factory Works
  • Create Linked Services and Datasets
  • Create Pipelines and Activities
  • Azure Data Factory Pipeline Execution and Triggers
  • PART 1, Module 8: Securing Azure Data Platforms
  • Configuring Network Security
  • Configuring Authentication
  • Configuring Authorization
  • Auditing Security
  • Lab : Securing Azure Data Platforms
  • Configure network security
  • Configure Authentication
  • Configure Authorization
  • Explore SQL Server Books Online
  • PART 1, Module 9: Monitoring and Troubleshooting Data Storage and Processing
  • Data Engineering troubleshooting approach
  • Azure Monitoring Capabilities
  • Troubleshoot common data issues
  • Troubleshoot common data processing issues
  • Lab : Monitoring and Troubleshooting Data Storage and Processing
  • Explain the Data Engineering troubleshooting approach
  • Explain the monitoring capabilities that are available
  • Troubleshoot common data storage issues
  • Troubleshoot common data processing issues
  • PART 1, Module 10: Integrating and Optimizing Data Platforms
  • Integrating data platforms
  • Optimizing data stores
  • Optimize streaming data
  • Manage disaster recovery
  • Lab : Integrating and Optimizing Data Platforms
  • Integrate Data Platforms
  • Optimize Data Stores
  • Optimize Streaming Data
  • Manage Disaster recovery
  • PART 2, Module 1: Data Platform Architecture Considerations
  • Core Principles of Creating Architectures
  • Design with Security in Mind
  • Performance and Scalability
  • Design for availability and recoverability
  • Design for efficiency and operations
  • Lab : Case Study
  • Core principles for creating architectures
  • Design with security in mind
  • Consider performance and scalability
  • Design for availability and recoverability
  • Design for efficiency and operations
  • PART 2, Module 2: Azure Batch Processing Reference Architectures
  • Lambda architectures from a Batch Mode Perspective
  • Design an Enterprise BI solution in Azure
  • Automate enterprise BI solutions in Azure
  • Architect an Enterprise-grade Conversational Bot in Azure
  • Lab : Architect an Enterprise-grade Conversational Bot in Azure
  • PART 2, Module 3: Azure Real-Time Reference Architectures
  • Lambda architectures for a Real-Time Perspective
  • Lambda architectures for a Real-Time Perspective
  • Design a stream processing pipeline with Azure Databricks
  • Create an Azure IoT reference architecture
  • Lab : Azure Real-Time Reference Architectures
  • Describe Lambda architectures for a Real-Time Mode Perspective
  • Architect a stream processing pipeline with Azure Stream Analytics
  • Design a stream processing pipeline with Azure Databricks
  • Create an Azure IoT reference architecture
  • PART 2, Module 4: Data Platform Security Design Considerations
  • Defense in Depth Security Approach
  • Network Level Protection
  • Identity Protection
  • Encryption Usage
  • Advanced Threat Protection
  • Lab : Data Platform Security Design Considerations
  • PART 2, Module 5: Designing for Resiliency and Scale
  • Design Backup and Restore strategies
  • Optimize Network Performance
  • Design for Optimized Storage and Database Performance
  • Incorporate Disaster Recovery into Architectures
  • Design Backup and Restore strategies
  • Lab : Designing for Resiliency and Scale
  • Adjust Workload Capacity by Scaling
  • Design a Highly Available Solution
  • PART 2, Module 6: Design for Efficiency and Operations
  • Maximizing the Efficiency of your Cloud Environment
  • Use Monitoring and Analytics to Gain Operational Insights
  • Use Automation to Reduce Effort and Error
  • Lab : Design for Efficiency and Operations