Providing Technology Training and Mentoring For Modern Technology Adoption
552241 is a two-day instructor-led course is intended for data professionals who want to expand their knowledge about creating big data analytic solutions on Microsoft Azure. Students will learn how to design solutions for batch and real-time data processing. Different methods of using Azure will be discussed and practiced in lab exercises, such Azure CLI, Azure PowerShell and Azure Portal. 552241 labs and exercises cover the first two objectives of exam 70-475 (Designing Big Data batch, interactive & real-time solutions). The other two objectives (Designing Machine Learning and cloud analytics solutions) are covered in 552242.
After completing this course, students will be able to:
The primary audience for this course is data engineers (IT professionals, developers, and information workers) who plan to implement big data engineering workflows on Azure.
In addition to their professional experience, students who attend this training should already have the following technical knowledge:
Module 1: Architectures for Big Data Engineering with AzureThis module describes common architectures for processing big data using Azure tools and services. Lessons
Module 2: Processing Event Streams using Azure Stream AnalyticsThis module describes how to use Azure Stream Analytics to design and implement stream processing over large-scale data.Lessons
Module 3: Performing custom processing in Azure Stream AnalyticsThis module describes how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job. Lessons
Module 4: Managing Big Data in Azure Data Lake StoreThis module describes how to use Azure Data Lake Store as a large-scale repository of data files.Lessons
Module 5: Processing Big Data using Azure Data Lake AnalyticsThis module describes how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.Lessons
Module 6: Implementing custom operations and monitoring performance in Azure Data Lake AnalyticsThis module describes how to create and deploy custom functions and operations, integrate with Python and R, and protect and optimize jobs.Lessons
Module 7: Implementing Azure SQL Data WarehouseThis module describes how to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest.Lessons
Module 8: Performing Analytics with Azure SQL Data WarehouseThis module describes how to import data in Azure SQL Data Warehouse, and how to protect this data.Lessons
Module 9: Automating the Data Flow with Azure Data FactoryThis module describes how to use Azure Data Factory to import, transform, and transfer data between repositories and services.Lessons