This course teaches developers how to create application using the NoSQL API and SDK for Azure Cosmos DB. Students will learn how to write efficient queries, create indexing policies, manage and provisioned resources, and perform common operations with the SDK.

Module 1: Get started with Azure Cosmos DB for NoSQL

Module 2: Plan and implement Azure Cosmos DB for NoSQL

Module 3: Connect to Azure Cosmos DB for NoSQL with the SDK

Module 4: Access and manage data with the Azure Cosmos DB for NoSQL SDKs

Module 5: Execute queries and build a Generative AI application with Azure Cosmos DB

Module 6: Define and implement an indexing strategy for Azure Cosmos DB for NoSQL

Module 7: Integrate Azure Cosmos DB for NoSQL with Azure services

Module 8: Implement a data modeling and partitioning strategy for Azure Cosmos DB for NoSQL

Module 9: Design and implement a replication strategy for Azure Cosmos DB for NoSQL

Module 10: Optimise query and operation performance in Azure Cosmos DB for NoSQL

Module 11: Monitor and troubleshoot an Azure Cosmos DB for NoSQL solution

Module 12: Manage an Azure Cosmos DB for NoSQL solution using DevOps practices

Module 13: Create server-side programming constructs in Azure Cosmos DB for NoSQL

By the end of the course, you will: 

• Learn about the Azure Cosmos DB for NoSQL and how to get started with your first account, database, and container.
• Use the .NET SDK for Azure Cosmos DB for NoSQL to perform common operations on databases, containers, and items.
• Create SQL queries for Azure Cosmos DB for NoSQL using the Data Explorer and SDKs for .NET, Python, and Node.js. Apply these skills to retrieve and process data efficiently, then use them to build a Generative AI application that makes the most of Cosmos DB’s capabilities.
• Access and manage data with the Azure Cosmos DB for NoSQL SDKs
• Integrate Azure Cosmos DB for NoSQL with Azure Cognitive Search, Azure Functions, and your own solutions.
• In this learning path, you’ll learn how Azure Cosmos DB uses partitioning to scale containers and how spending some time thinking about your data model helps to meet the performance needs of your applications.
• Optimise the performance of your queries and operations using Azure Cosmos DB for NoSQL.
• Use JavaScript to author server-side stored procedures, user-defined functions, and triggers.

Prerequisites for this course are:

• Familiarity with Azure and the Azure portal.
• Experience programming with C#. If you have no previous programming experience, we recommend you complete the Take your first steps with C# learning path before starting this one.
• Before starting this learning path, you should have experience of building cloud applications with Microsoft C# or a similar programming language.

This course provides students with the knowledge and skills to administer a SQL Server database infrastructure for cloud, on-premises and hybrid relational databases and who work with the Microsoft PaaS relational database offerings. Additionally, it will be of use to individuals who develop applications that deliver content from SQL-based relational databases.

Module 1: Plan and implement data platform resources

Module 2: Implement a secure environment for a database service

Module 3: Monitor and optimise operational resources in Azure SQL

Module 4: Optimise query performance in Azure SQL

Module 5: Automate database tasks for Azure SQL

Module 6: Plan and implement a high availability and disaster recovery environment

By the end of the course, you will be able to:

The audience for this course is data professionals managing data and databases who want to learn about administering the data platform technologies that are available on Microsoft Azure. This course is also valuable for data architects and application developers who need to understand what technologies are available for the data platform with Azure and how to work with those technologies through applications.

Successful Azure Database Administrators start this role with professional experience in database management and technical knowledge of cloud technologies.

Specifically:

• Working with, maintaining, and developing with SQL Server
• Experience with Azure, such as deploying and managing resources

At a minimum, you should know the information in the following online training before attending the course:

• AZ-900 Azure Fundamentals
• DP-900 Azure Data Fundamentals

This course covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric. Students will learn how to use Fabric dataflows, pipelines, and notebooks to develop analytics assets such as semantic models, data warehouses, and lakehouses.  This course is designed for experienced data professionals skilled at data preparation, modeling, analysis, and visualisation, such as the PL-300: Power BI Data Analyst certification.

The primary audience for this course is data professionals with experience in data modeling and analytics. DP-600 is designed for professionals who want to use Microsoft Fabric to create and deploy enterprise-scale data analytics solutions. Learners should have prior experience with one of the following programming languages: Structured Query Language (SQL), Kusto Query Language (KQL), or Data Analysis Expressions (DAX).

Module 1: Get started with Microsoft Fabric

Module 2: Implement a data warehouse with Microsoft Fabric

Module 3: Work with semantic models in Microsoft Fabric

Module 4: Administer and govern Microsoft Fabric

By the end of the course, you should be able to:

• Explore how to implement data analytics solutions on a single platform with Microsoft Fabric. Integrate, transform, and store data to train AI models and create insightful reports.
• Explore the data warehousing process and learn how to load, monitor, secure, and query a warehouse in Microsoft Fabric.
• Designing reports for enterprise scale requires more than just connecting to data. Understanding semantic models and strategies for scalability and lifecycle management are key to a successful enterprise implementation. This learning path helps you prepare for the Fabric Analytics Engineer Certification.

Prerequisites include:

• You should be familiar with basic data concepts and terminology, including data engineering, modeling, and analysis.
• You should be familiar with basic data concepts and terminology.
• Familiarity with Microsoft Fabric
• Work with semantic models in Microsoft Fabric

This course covers methods and practices to implement data engineering solutions by using Microsoft Fabric. Explore how Microsoft Fabric enables you to ingest and orchestrate data from various sources (such as files, databases, or web services) through dataflows, notebooks, and pipelines.

This learning path introduces the foundational components of implementing a data lakehouse with Microsoft Fabric:

• Ingest, transform, and analyse streaming data with Microsoft Fabric.
• Explore the data warehousing process and learn how to load, monitor, secure, and query a warehouse in Microsoft Fabric.
• Microsoft Fabric is a Software-as-a-Service platform for data analytics. Learn how to manage your environment through Continuous Integration/Continuous Deployment (CI/CD), monitoring, and security.

Module 1: Ingest data with Microsoft Fabric

Module 2: Implement a Lakehouse with Microsoft Fabric

Module 3: Implement Real-Time Intelligence with Microsoft Fabric

Module 4: Manage a Microsoft Fabric environment

Students will learn how to design and develop effective data loading patterns, data architectures, and orchestration processes. Objectives for this course include ingesting and transforming data and securing, managing, and monitoring data engineering solutions. This course is designed for experienced data professionals skilled at data integration and orchestration.

This audience for this course is data professionals with experience in data extraction, transformation, and loading. DP-700 is designed for professionals who need to create and deploy data engineering solutions using Microsoft Fabric for enterprise-scale data analytics. Learners should also have experience at manipulating and transforming data with one of the following programming languages: Structured Query Language (SQL), PySpark, or Kusto Query Language (KQL).

Prerequisites include:

• Familiarity with Microsoft Fabric
• You should be familiar with basic data concepts and terminology.
• The student should be familiar with Microsoft Fabric and SQL, basic knowledge of KQL is helpful.
• Before starting this learning path, you should have experience with Microsoft Fabric or Power BI. You should also have a basic understanding of data engineering and management concepts.