Azure Data Engineering

  Understanding Mapping Data Flows in Azure Data Factory (ADF) Mapping Data Flows in Azure Data Factory (ADF) is are powerful visual tools that enable data engineers to design, build, and operationalize data transformation logic at scale—without writing code. For professionals aiming to advance in the Azure Data Engineer Course Online , mastering these data flows is essential. They allow users to transform data from various sources, apply complex business rules, and load it efficiently into destination systems, making them a core element in modern data engineering pipelines. Understanding Mapping Data Flows in Azure Data Factory (ADF) 1. What Are Mapping Data Flows? Mapping Data Flows are data transformation activities within Azure Data Factory that use a no-code, drag-and-drop interface . Unlike traditional ETL processes, which rely heavily on custom scripts, Mapping Data Flows provide a visual environment for designing complex transformations. The actual execution happens o...

Understanding Azure Data Factory Integration Runtimes 1. Introduction to Integration Runtimes Azure Data Factory (ADF) serves as a powerful cloud-based ETL and data integration service. One of its core features is integration runtimes , which act as the compute infrastructure enabling data movement, transformation, and pipeline orchestration. For professionals looking to excel in the Azure Data Engineer Course Online , understanding integration runtimes is fundamental, as it allows them to design robust and scalable data pipelines. Integration runtimes essentially handle how data is copied from source to destination, ensuring security, performance, and flexibility across hybrid and cloud environments. Understanding Azure Data Factory Integration Runtimes 2. Types of Integration Runtimes in ADF ADF provides three main types of integration runtimes: ·          Azure Integration Runtime (Azure IR): Runs data movement and transformation in th...