Azure Data Engineer | Azure Data Engineer Course in Chennai

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Key Differences Between ETL and ELT Processes in
Azure

• SubtitleA Guide for Azure Data Engineers

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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.

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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.

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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.

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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.

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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.

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Key Differences ETL vs ELT
Feature ETL ELT
Transformation Before loading After loading
Performance Limited by external engine Utilizes cloud-native compute
Use Case Legacy systems Big data real-time streams
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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

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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.

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For More Information About Azure DATA
Engineering Address- Flat no 205, 2nd Floor,
Nilagiri Block, Aditya Enclave, Ameerpet,
Hyderabad-16 Ph. No 91-998997107
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Thank You
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