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Key Differences Between ETL Key Differences Between ETL and ELT Processes in Azure and ELT Processes in Azure Subtitle: A Guide for Azure Data Engineers www.visualpath.in +91-7032290546
Introduction to Azure Data Engineering Introduction to Azure Data Engineering Azure provides a scalable cloud ecosystem for building modern data pipelines. ETL and ELT are two primary approaches to ingest, process, and store data. Understanding their differences is crucial for building efficient solutions. www.visualpath.in +91-7032290546
What is ETL? (Extract, Transform, Load) What is ETL? (Extract, Transform, Load) Data is extracted from source systems. Transformed in a staging area (on- prem or cloud). Loaded into a destination like Azure SQL Database or Synapse Analytics. Best for complex transformations before loading data. www.visualpath.in +91-7032290546
What is ELT? (Extract, Load, Transform) What is ELT? (Extract, Load, Transform) Data is extracted from source systems. Loaded directly into Azure Data Lake or Synapse Analytics. Transformed inside the target system using native compute power (e.g., T-SQL, Spark). Ideal for handling large volumes of raw data. www.visualpath.in +91-7032290546
Tooling in Azure for ETL Tooling in Azure for ETL Azure Data Factory (ADF) for orchestration. Azure Integration Runtime or SSIS for transformation. Data loaded into Azure Synapse or SQL DB post-transformation. www.visualpath.in +91-7032290546
Tooling in Azure for ELT Tooling in Azure for ELT ADF used primarily for orchestration and data movement. Data stored in Azure Data Lake or Synapse. Transformations performed using Synapse SQL, Databricks, or Spark pools. www.visualpath.in +91-7032290546
Key Differences: ETL Key Differences: ETL vs vs ELT ELT Feature Feature ETL ETL ELT ELT Transformation Before loading After loading Utilizes cloud-native compute Performance Limited by external engine Use Case Legacy systems Big data & real-time streams www.visualpath.in +91-7032290546
When to Use ETL or ELT in Azure When to Use ETL or ELT in Azure Use ETL when: Source data needs cleaning before loading Regulatory compliance requires transformation before storage Use ELT when: Working with large volumes of semi- structured or raw data Utilizing Synapse or Databricks for scalable compute www.visualpath.in +91-7032290546
Conclusion Conclusion ETL and ELT both play key roles in Azure data pipelines. Choose based on data volume, transformation complexity, and performance needs. Azure supports both approaches using robust, integrated services. www.visualpath.in +91-7032290546
For More Information About Azure DATA Engineering Address:- Flat no: 205, 2nd Floor, Nilagiri Block, Aditya Enclave, Ameerpet, Hyderabad-16 Ph. No: +91-998997107 www.visualpath.in online@visualpath.in www.visualpath.in +91-7032290546
Thank You www.visualpath.in www.visualpath.in +91-7032290546
