This course explores how to use Databricks and Apache Spark on Azure to take data projects from exploration to production. You’ll learn how to ingest, transform, and analyze large-scale datasets with Spark DataFrames, Spark SQL, and PySpark, while also building confidence in managing distributed data processing. Along the way, you’ll get hands-on with the Databricks workspace—navigating clusters and creating and optimizing Delta tables.   You’ll also dive into data engineering practices, including designing ETL pipelines, handling schema evolution, and enforcing data quality. The course then moves into orchestration, showing you how to automate and manage workloads with Lakeflow Jobs and pipelines. To round things out, you’ll explore governance and security capabilities such as Unity Catalog and Purview integration, ensuring you can work with data in a secure, well-managed, and production-ready environment.

Azure Databricks is a fully managed, cloud-based data analytics platform, which empowers developers to accelerate AI and innovation by simplifying the process of building enterprise-grade data applications. Built as a joint effort by Microsoft and the team that started Apache Spark, Azure Databricks provides data science, engineering, and analytical teams with a single platform for big data processing and machine learning. In this course, you’ll learn how to use Azure Databricks to train and deploy machine learning models.

This course teaches developers to utilize Azure Cosmos DB for NoSQL API and SDK. Students will learn query execution, resource configuration, SDK operations, and design strategies for non-relational data modeling and data partitioning.

This learning path prepares you for the task of developing data-driven applications by using Microsoft Azure SQL Database.

You’ll learn how to create and configure an Azure SQL Database, build and deploy database projects using GitHub Actions and Azure Pipelines, and automate the publishing process. Additionally, you’ll explore how to use Data API builder for Azure SQL Database and develop a data API with Azure Web Apps and Static Web App.

Furthermore, you’ll gain skills in importing data via an external REST endpoint, exporting data using an Azure Function, and securing an Azure SQL Database. These essential skills will empower you to effectively develop and manage applications using Azure SQL Database.

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 visualization, such as the PL-300: Power BI Data Analyst certification. 

• Source streaming data sources into Microsoft Fabric.
• Use real time Eventstream in Microsoft Fabric.
• Query data in a KQL database in Microsoft Fabric.
• Create real time dashboards in Microsoft Fabric.

This course is designed to build practical skills in implementing data science and machine learning solutions using Microsoft Fabric. The course explores the complete end-to-end data science process, from understanding and exploring data to preparing and transforming datasets for analysis. Students will learn how to train, evaluate, and track machine learning models, as well as how to deploy those models and generate predictions using Microsoft Fabric tools and capabilities.