DP-050T00: Migrate SQL workloads to Azure

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Trukmė Kalba Miestas Kaina Data ir registracija kursui
2 dienos lietuvių k. - 1000 EUR Užklausti

In this course, the students will explore the objectives of data platform modernization and how it is suitable for given business requirements. They will also explore each stage of the data platform modernization process and define what tasks are involved at each stage, such as the assessment and planning phase. Students will also learn the available migration tools and how they are suitable for each stage of the data migration process. The student will learn how to migrate to the three target platforms for SQL based workloads; Azure Virtual Machines, Azure SQL Databases and Azure SQL Database Managed Instances. The student will learn the benefits and limitations of each target platform and how they can be used to fulfil both business and technical requirements for modern SQL workloads. The student will explore the changes that may need to be made to existing SQL based applications, so that they can make best use of modern data platforms in Azure.

Kursai skirti

The audience for this course is data professionals and data architects who want to learn about migrating data platform technologies that exist on Microsoft Azure and how existing SQL based workloads can be migrated and modernized. The secondary audience for this course is individuals who manage data platforms or develop applications that deliver content from the existing data platform technologies.

Kurso nauda

In this course students will learn:

  • Understand Data Platform Modernization
  • Choose the right tools for Data Migration
  • Migrate SQL Workloads to Azure Virtual Machines
  • Migrate SQL Workloads to Azure SQL Databases
  • Migrate SQL Workloads to Azure SQL Database Managed Instance


  1. Introducing Data Platform Modernization
  2. Choose the right tools for Data Migration
  3. Migrating SQL Workloads to Azure Virtual Machines
  4. Migrate SQL Workloads to Azure SQL Databases
  5. Migrate SQL Workloads to Azure SQL Database Managed Instance


Successful students start this role with a fundamental knowledge of cloud computing concepts and professional experience in implementing SQL solutions.


  • Working with and maintaining SQL workloads
  • Experience with Azure, such as deploying and managing resources

To gain these skills, take the following free online training before attending the course:

Module 1: Introduction to Azure Machine Learning
Getting Started with Azure Machine Learning
Azure Machine Learning Tools
Lab : Creating an Azure Machine Learning Workspace
Lab : Working with Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer
Training Models with Designer
Publishing Models with Designer
Lab : Creating a Training Pipeline with the Azure ML Designer
Lab : Deploying a Service with the Azure ML Designer
Module 3: Running Experiments and Training Models
Introduction to Experiments
Training and Registering Models
Lab : Running Experiments
Lab : Training and Registering Models
Module 4: Working with Data
Working with Datastores
Working with Datasets
Lab : Working with Datastores
Lab : Working with Datasets
Module 5: Compute Contexts
Working with Environments
Working with Compute Targets
Lab : Working with Environments
Lab : Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
Introduction to Pipelines
Publishing and Running Pipelines
Lab : Creating a Pipeline
Lab : Publishing a Pipeline
Module 7: Deploying and Consuming Models
Real-time Inferencing
Batch Inferencing
Lab : Creating a Real-time Inferencing Service
Lab : Creating a Batch Inferencing Service
Module 8: Training Optimal Models
Hyperparameter Tuning
Automated Machine Learning
Lab : Tuning Hyperparameters
Lab : Using Automated Machine Learning
Module 9: Interpreting Models
Introduction to Model Interpretation
using Model Explainers
Lab : Reviewing Automated Machine Learning Explanations
Lab : Interpreting Models
Module 10: Monitoring Models
Monitoring Models with Application Insights
Monitoring Data Drift
Lab : Monitoring a Model with Application Insights
Lab : Monitoring Data Drift