5. Azure DataBricks – DLT Workflow, Incremental Auto Load, Parametrization

📌 Welcome to Data Sight! In this power-packed tutorial, we dive deep into Azure Databricks and demonstrate a real-time hands-on project focused on: 🔹 DLT (Delta Live Tables) Workflow 🔹 Incremental Auto Load with Change Data Capture (CDC) 🔹 Parametrization for dynamic and reusable pipelines Whether you're a beginner or intermediate learner, this session will help you understand and implement modern data engineering workflows using Azure Databricks. 💡 What You’ll Learn: Setting up and managing a DLT pipeline in Azure Databricks Implementing Incremental Load using Auto Loader with schema evolution Applying parameters to make your pipelines reusable and dynamic Real-world tips to scale your data workflows 👨‍💻 Perfect For: Data Engineers, Data Scientists, Azure Developers, and anyone interested in building scalable data pipelines on Azure. 🛠️ Tools Used: Azure Databricks Delta Lake Auto Loader Parameterization techniques Notebooks + DLT Pipelines ✅ Don’t forget to LIKE, SHARE, and SUBSCRIBE to Data Sight for more hands-on tutorials, project implementations, and career guidance in the Azure Data Engineering world! 📌 Stay updated with the latest in Data Engineering & Azure Projects! 🔖 Hashtags: #AzureDatabricks #DeltaLiveTables #AutoLoader #DataEngineering #DLTWorkflow #IncrementalLoad #AzureDataEngineering #DatabricksTutorial #RealTimeDataPipeline #DataSight #DatabricksForBeginners #BigData #ETL #DataPipeline #CDC #AzureProjects