Syllabus
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 businesses to explore their data in different ways. The students will gain an overview of the various data platform technologies that are available and how a Data Engineer's role and responsibilities has evolved to work in this new world to an organization's 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
Working with Data Storage
This module teaches the variety of ways to store data in Azure. The students 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 want to be stored 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
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 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
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, 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 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 understand the following:
- 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 data globally with Azure Cosmos DB
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 students 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
- Create and Query an Azure SQL Data Warehouse
- Use PolyBase to Load Data into Azure SQL Data Warehouse
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, they 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
- Understand Data Ingestion with Event Hubs
- Understand Processing Data with Stream Analytics Jobs
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:
- Understand Azure Data Factory and Databricks
- Understand Azure Data Factory Components
- Be able to explain how Azure Data Factory works
Securing Azure Data Platforms
In this module, students will learn how Azure provides a multi-layered security model to protect data. The students will explore how security can range from setting up secure networks and access keys, to defining permission, 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:
- Have an introduction to security
- Understand key security components
- Understand securing Storage Accounts and Data Lake Storage
- Understand securing Data Stores
- Understand securing Streaming Data
Monitoring and Troubleshooting Data Storage and Processing
In this module, the students 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:
- Explain the monitoring capabilities that are available
- Troubleshoot common data storage issues
- Troubleshoot common data processing issues
- Manage disaster recovery
- Understand securing Streaming Data
Certification
Executive Program in Implementing an Azure Data Solution (DP-200) Certified by Microsoft.
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Platforms Covered
Azure Data Lake
Azure Data Lake is a scalable data storage and analytics service. The service is hosted in Azure, Microsoft's public cloud.
Azure Databricks
Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. For a big data pipeline, the data (raw or structured) is ingested into Azure through Azure Data Factory in batches, or streamed near real-time using Kafka, Event Hub, or IoT Hub.
Azure Storage
Azure Storage is a Microsoft-managed cloud storage service, that provides highly available, durable, scalable and redundant storage, at a fraction of the cost, if you were to manage it manually.
Azure Cosmos DB
Azure Cosmos DB is Microsoft's proprietary globally-distributed, multi-model database service "for managing data at planet-scale" launched in May 2017. It is schema-agnostic, horizontally scalable and generally classified as a NoSQL database.
Azure Stream Analytics
Microsoft Azure Stream Analytics is a serverless scalable complex event processing engine by Microsoft that enables users to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, web sites, social media, and other applications.
Azure SQL Data Warehouse
Azure SQL Data Warehouse (SQL DW) is a petabyte-scale MPP analytical data warehouse built on the foundation of SQL Server and run as part of the Microsoft Azure Cloud Computing Platform.
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Instructors and Experts
Learn from India's Best leading faculty and industry leaders
Sanjeev Singh
EXP 18+
Sameer
EXP 15+
Satwik Muthappa
EXP 15+
Mujaheed
EXP 12+Program Fee
Implementing an Azure Data Solution (DP-200)
INR. 28,990*
Inclusive of all Taxes
No Cost EMI Available
Frequently Asked Questions
What is Azure Data Solution?
Azure Data Engineers design and implement the management, monitoring, security, and privacy of data using the full stack of Azure data services to satisfy business needs.
What is Azure Data Solution Used for?
Azure data engineers are responsible for data-related implementation tasks that include provisioning data storage services, ingesting streaming and batch data, transforming data, implementing security requirements, implementing data retention policies, identifying performance bottlenecks, and accessing external data sources.
What is a learning path?
Learning paths are collections of modules that are organized around specific roles (like developer, architect, or system admin) or technologies (like Azure Web Apps, Power BI, or Xamarin.Forms). When you finish a learning path, you've gained a new understanding of different aspects of the technology or role you're studying. You also get a trophy!
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