This course takes you from understanding the foundations of cloud-native data engineering to building and operating complete data platforms with Azure Databricks, introducing practical techniques for scalable ingestion, transformation, processing, governance, and monitoring.

You'll begin by building a clear foundation in cloud-native data engineering, learning how modern data engineering differs from traditional approaches, how the data engineering lifecycle works, and how data lakes, warehouses, and lakehouses support different analytical requirements. You'll also explore cloud-native storage architectures and establish an Azure environment using Azure Databricks and Azure Data Lake Storage Gen2. From there, the course explores how production data pipelines are built using Apache Spark and Azure Databricks. You'll learn to transform data with Spark DataFrames, manage reliable datasets with Delta Lake, and organise information through Bronze, Silver, and Gold data layers. These capabilities provide the foundation for creating structured and maintainable pipelines that can process growing volumes of business data. The course then advances into incremental and real-time data processing. You'll build incremental ingestion pipelines with Auto Loader, process streaming data using Azure Event Hubs, and orchestrate pipeline execution with Databricks Workflows. This allows you to move beyond isolated transformations and develop automated data pipelines that respond efficiently to changing data sources. Finally, you'll focus on operating production-ready data platforms. You'll apply data governance using Unity Catalog, implement data quality and pipeline validation, monitor Databricks jobs and workloads, and optimise pipeline performance. You'll bring these capabilities together by building an end-to-end cloud-native data platform that moves data through ingestion, transformation, validation, governance, and analytical layers. By the end of this course, you will be able to: - Explain cloud-native data engineering concepts, architectures, and the modern data engineering lifecycle. - Configure Azure Databricks and integrate it with Azure Data Lake Storage Gen2. - Build scalable batch data pipelines using Apache Spark, Delta Lake, and medallion architecture. - Develop incremental and streaming pipelines using Auto Loader and Azure Event Hubs. - Orchestrate automated data pipelines using Databricks Workflows. - Govern, validate, monitor, and optimise production-ready data platforms using Azure Databricks. Designed for aspiring data engineers, cloud professionals, developers, analysts, and technical professionals seeking practical experience with modern cloud data platforms, this course provides a structured path from foundational concepts to production-oriented implementation. To be successful here, you should have a basic understanding of data concepts and familiarity with programming fundamentals. Prior experience with Azure Databricks or advanced cloud data engineering is not required, as the course introduces the environment and technologies through guided practical activities. Build the skills to transform raw data into reliable, governed, and analytics-ready information, and develop cloud-native data platforms designed for modern data engineering workloads.