COURSE OVERVIEW
In this course, the students will implement various data platform technologies into solutions that are in line with business and technical requirements including on-premises, cloud, and hybrid data scenarios incorporating both relational and No-SQL data. They will also learn how to process data using a range of technologies and languages for both streaming and batch data.
The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions.
DELIVERY FORMAT
- Delivery Format: Virtual Training
- Date: 3 – 5 August, 2020
- Time: 9:00 AM – 5:00 PM GMT
- Price: USD 1,500/ attendee
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COURSE CONTENT
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
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
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 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
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
Provision 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
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
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
Explain SQL Database and SQL Data Warehouse
Create an Azure SQL Database to store data
Provision and 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
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
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
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
Module 8: Securing Azure Data Platforms.
In this module, students will learn how Azure Storage 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 with Advanced Threat Detection.
Lessons
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
Module 9: Monitoring and Troubleshooting Data Storage and Processing.
In this module, the student will look at the wide range of monitoring capabilities that are available to provide operational support should there be issue with a data platform architecture. They will explore the data engineering troubleshooting approach and be able to apply this to common data storage and data processing issues.
Lessons
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
Module 10: Integrating and Optimizing Data Platforms.
In this module, the student will explore the various ways in which data platforms can be integrated based upon different business requirements. They will also explore the various ways in which data platforms can be optimized from a storage and data processing perspective to improve data loads. Finally, disaster recovery options are revealed to ensure business continuity.
Lessons
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
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