This course covers data engineering patterns for batch and real-time analytical solutions using Microsoft Azure. Students learn to use core compute and storage technologies, design serving layers, and handle data ingestion, transformation, and security. It includes hands-on labs for practical experience.

Learning Outcomes

• Explore compute and storage options for data engineering workloads in Azure
• Design and Implement the serving layer
• Understand data engineering considerations
• Run interactive queries using serverless SQL pools
• Explore, transform, and load data into the Data Warehouse using Apache Spark
• Perform data Exploration and Transformation in Azure Databricks
• Ingest and load Data into the Data Warehouse
• Transform Data with Azure Data Factory or Azure Synapse Pipelines
• Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
• Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
• Analyze and Optimize Data Warehouse Storage
• Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
• Perform end-to-end security with Azure Synapse Analytics
• Perform real-time Stream Processing with Stream Analytics
• Create a Stream Processing Solution with Event Hubs and Azure Databricks
• Build reports using Power BI integration with Azure Synapse Analytics
• Perform Integrated Machine Learning Processes in Azure Synapse Analytics

Prerequisites

Knowledge of cloud computing and core data concepts, along with professional experience in data solutions.

Prerequisite Courses

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