Azure for Data Engineer

• Experience with elementary data analysis (e.g., Excel)
• A general understanding of cloud concepts (e.g., AWS)

Audience

• Database engineers
• Developers

Data analysis refers to the process of obtaining, cleaning, and analyzing data, often times in a visual manner, to derive insights for better decision making. More and more data is being stored in databases in the cloud so it is important to have a strategy for understanding, managing and accessing such data. One popular tool for accessing and visualizing data is Power Bi.

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to gain a practical understanding of available cloud solutions, the data analysis processes needed to work with data in the cloud, and the hands-on practice to apply tools such as Power BI to analyze data.

By the end of this training, participants will be able to:

• Install and configure Power BI.
• Evaluate the various data solutions offered by cloud providers such as Azure.
• Gain an understanding of the different structures, modeling approaches, and data warehouse designs used to store, manage and access Big Data.
• Apply tools and techniques to clean data in preparation for analysis.
• Build reporting and analytics solutions based on on-premise as well as cloud data.
• Integrate data analytics solutions with a data warehouse.
• Mitigate data security risks and ensure data privacy.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

1.        Azure for the Data Engineer

• 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
• 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

2.       Working with Data Storage

• 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

3.       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
• 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

4.       Building Globally Distributed Databases with Cosmos DB

• 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

5.       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
• 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

6.       Performing Real-Time Analytics with Stream Analytics

• 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

7.       Orchestrating Data Movement with Azure Data Factory

• 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

8.       Securing Azure Data Platforms

• 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

9.       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
• 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