Azure Data Engineering

  Azure Data Factory vs SSIS: Understanding the Key Differences Azure Data Factory (ADF) is a modern, cloud-based data integration service that enables organizations to efficiently manage, transform, and move data across various systems. In contrast, SQL Server Integration Services (SSIS) is a traditional on-premises ETL tool designed for batch processing and data migration. Both are powerful data integration tools offered by Microsoft, but they serve different purposes, environments, and capabilities. In this article, we’ll delve into the key differences between Azure Data Factory and SSIS, helping you understand when and why to choose one over the other. Microsoft Azure Data Engineer Azure Data Factory vs SSIS: Understanding the Key Differences 1. Overview SQL Server Integration Services (SSIS) SSIS is a traditional on-premises ETL (Extract, Transform, Load) tool that is part of Microsoft SQL Server. It allows users to create workflows for data integration, transformatio...

  Key Differences Between ETL and ELT Processes in Azure Azure data engineering offers two common approaches for processing data: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). These methods are essential for moving and processing data from source systems into data warehouses or data lakes for analysis. While both serve similar purposes, they differ in their workflows, tools, and technologies, particularly when implemented within Azure's cloud ecosystem. This article will explore the key distinctions between ETL and ELT in the context of Azure data services, helping organizations make informed decisions about their data processing strategies. Azure Data Engineer Training Key Differences Between ETL and ELT Processes in Azure 1. Process Flow: Extraction, Transformation, and Loading The most fundamental difference between ETL and ELT is the sequence in which data is processed: Microsoft Azure Data Engineer ·        ...