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Implementing an Azure Data Solution

30 Hours
Online Instructor-led Training
USD 1399 (USD 2800)
Save 50% Offer ends on 31-Jul-2024
Implementing an Azure Data Solution course and certification
318 Learners

About this Course
Implementing an Azure Data Solution training course will give you a detailed overview of ingesting, egressing, and transforming data from multiple sources using various services and tools. This training will prepare you for designing and implementing the management, monitoring, security, and privacy of data using the full stack of Azure services. This course is the part of Microsoft Certified: Azure Data Engineer Associate certification.

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Who should do Implementing an Azure Data Solution training?

  • Azure Data Engineers
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This is the second exam for Azure Data Engineer and this exam helps engineers to design data storage, data processing, and data security and compliance solutions for Azure services. Candidates must have the ability to design Azure SQL Databases, Azure Cosmos DB, Azure Data Lake Storage, Azure Stream Analytics, and Blob storage services.

Areas Covered Design relational and non-relational cloud data stores Design real-time and batch processing solutions and provision compute resources Design security solutions as encryption, auditing, and privacy of the data Design data retention policies and plan archiving strategies

 

Implementing an Azure Data Solution

Course Details & Curriculum
200T01-A: Implementing an Azure Data Solution

Course Outline

Module 1: Azure for the Data Engineer This module explores how the world of data has evolved and how cloud data platform technologies are providing new opportunities for business to explore their data in different ways. The student will gain an overview of the various data platform technologies that are available, and how a Data Engineers role and responsibilities has evolved to work in this new world to an organization benefit Lessons  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 After completing this module, students will be able to:  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

Module 2: Working with Data Storage This module teaches the variety of ways to store data in Azure. The Student will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for the data you want to store in the cloud. They will also understand how data lake storage can be created to support a wide variety of big data analytics solutions with minimal effort. Lessons  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 After completing this module, students will be able to:  Choose a data storage approach in Azure  Create an Azure Storage Account  Explain Azure Data Lake Storage  Upload data into Azure Data Lake

Module 3: Enabling Team Based Data Science with Azure Databricks This module introduces students to Azure Databricks and how a Data Engineer works with it to enable an organization to perform Team Data Science projects. They will learn the fundamentals of Azure Databricks and Apache Spark notebooks; how to provision the service and workspaces and learn how to perform data preparation task that can contribute to the data science project. Lessons  Explain Azure Databricks  Work with Azure Databricks  Read data with Azure Databricks  Perform transformations with Azure Databricks Lab : Enabling Team Based Data Science with Azure Databricks  Explain Azure Databricks  Work with Azure Databricks  Read data with Azure Databricks  Perform transformations with Azure Databricks After completing this module, students will be able to:  Explain Azure Databricks  Work with Azure Databricks  Read data with Azure Databricks  Perform transformations with Azure Databricks

Module 4: Building Globally Distributed Databases with Cosmos DB In this module, students will learn how to work with NoSQL data using Azure Cosmos DB. They will learn how to provision the service, and how they can load and interrogate data in the service using Visual Studio Code extensions, and the Azure Cosmos DB .NET Core SDK. They will also learn how to configure the availability options so that users are able to access the data from anywhere in the world. Lessons  Create an Azure Cosmos DB database built to scale  Insert and query data in your Azure Cosmos DB database  Build a .NET Core app for Cosmos DB in Visual Studio Code  Distribute your data globally with Azure Cosmos DB Lab : Building Globally Distributed Databases with Cosmos DB  Create an Azure Cosmos DB  Insert and query data in Azure Cosmos DB  Build a .Net Core App for Azure Cosmos DB using VS Code  Distribute data globally with Azure Cosmos DB After completing this module, students will be able to:  Create an Azure Cosmos DB database built to scale  Insert and query data in your Azure Cosmos DB database  Build a .NET Core app for Azure Cosmos DB in Visual Studio Code  Distribute your data globally with Azure Cosmos DB

Module 5: Working with Relational Data Stores in the Cloud In this module, students will explore the Azure relational data platform options including SQL Database and SQL Data Warehouse. The student will be able explain why they would choose one service over another, and how to provision, connect and manage each of the services. Lessons  Use Azure SQL Database  Describe Azure SQL Data Warehouse  Creating and Querying an Azure SQL Data Warehouse  Use PolyBase to Load Data into Azure SQL Data Warehouse Lab : Working with Relational Data Stores in the Cloud  Use Azure SQL Database  Describe Azure SQL Data Warehouse  Creating and Querying an Azure SQL Data Warehouse  Use PolyBase to Load Data into Azure SQL Data Warehouse After completing this module, students will be able to:  Use Azure SQL Database  Describe Azure Data Warehouse  Creating and Querying an Azure SQL Data Warehouse  Using PolyBase to Load Data into Azure SQL Data Warehouse

Module 6: Performing Real-Time Analytics with Stream Analytics In this module, students will learn the concepts of event processing and streaming data and how this applies to Events Hubs and Azure Stream Analytics. The students will then set up a stream analytics job to stream data and learn how to query the incoming data to perform analysis of the data. Finally, you will learn how to manage and monitor running jobs. Lessons  Explain data streams and event processing  Data Ingestion with Event Hubs  Processing Data with Stream Analytics Jobs Lab : Performing Real-Time Analytics with Stream Analytics  Explain data streams and event processing  Data Ingestion with Event Hubs  Processing Data with Stream Analytics Jobs After completing this module, students will be able to:  Explain data streams and event processing  Data Ingestion with Event Hubs  Processing Data with Stream Analytics Jobs

Module 7: Orchestrating Data Movement with Azure Data Factory In this module, students will learn how Azure Data factory can be used to orchestrate the data movement and transformation from a wide range of data platform technologies. They will be able to explain the capabilities of the technology and be able to set up an end to end data pipeline that ingests and transforms data. Lessons  Explain how Azure Data Factory works  Azure Data Factory Components  Azure Data Factory and Databricks Lab : Orchestrating Data Movement with Azure Data Factory  Explain how Data Factory Works  Azure Data Factory Components  Azure Data Factory and Databricks After completing this module, students will be able to:  Azure Data Factory and Databricks  Azure Data Factory Components  Explain how Azure Data Factory works

Module 8: Securing Azure Data Platforms In this module, students will learn how Azure provides a multi-layered security model to protect your data. The students will explore how security can range from setting up secure networks and access keys, to defining permission through to monitoring across a range of data stores. Lessons  An introduction to security  Key security components  Securing Storage Accounts and Data Lake Storage  Securing Data Stores  Securing Streaming Data Lab : Securing Azure Data Platforms  An introduction to security  Key security components  Securing Storage Accounts and Data Lake Storage  Securing Data Stores  Securing Streaming Data After completing this module, students will be able to:  An introduction to security  Key security components  Securing Storage Accounts and Data Lake Storage  Securing Data Stores  Securing Streaming Data

Module 9: Monitoring and Troubleshooting Data Storage and Processing In this module, the student will get an overview of the range of monitoring capabilities that are available to provide operational support should there be issue with a data platform architecture. They will explore the common data storage and data processing issues. Finally, disaster recovery options are revealed to ensure business continuity. Lessons  Explain the monitoring capabilities that are available  Troubleshoot common data storage issues  Troubleshoot common data processing issues  Manage disaster recovery Lab : Monitoring and Troubleshooting Data Storage and Processing  Explain the monitoring capabilities that are available  Troubleshoot common data storage issues  Troubleshoot common data processing issues  Manage disaster recovery After completing this module, students will be able to:  Explain the monitoring capabilities that are available  Troubleshoot common data storage issues  Troubleshoot common data processing issues  Manage disaster recovery
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