Top Data Build Tool Training – DBT Training in Hyderabad
                     How DBT Differs from Traditional ETL Tools
www.visualpath.in  Modern Data Transformation in the Analytics Stack
The Traditional ETL Model
Title: Traditional ETL: Extract → Transform → Load
 Data is extracted from source systems
 Transformed using an external ETL engine
 Loaded into a data warehouse
 Common tools: Informatica, Talend, SSIS, DataStage
Challenges:
 Complex to manage and maintain
 Slow iteration cycles
• Limited transparency into transformations
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Enter DBT – The Modern Way
Title: dbt: Transform After Load (ELT)
 ELT process: Extract and load data first, then 
transform
 dbt focuses solely on the transformation layer
 Leverages the compute power of modern data 
warehouses
 Uses SQL and Jinja for modular, testable models
www.visualpath.in • Embraces version control and CI/CD workflows
Title: Traditional ETL vs. dbt ELT Architecture
Key Architectural Difference
Feature Traditional ETL dbt
Transformation Engine External tool Data warehouse (SQL)
Language GUI/Scripting SQL + Jinja
Version Control Limited or proprietary Git-native
Testing Manual or external Built-in data tests
Deployment Often manual Automated via CI/CD
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Developer Experience
Title: Built for Analysts and Engineers
 Traditional ETL tools often require complex scripting 
or proprietary GUIs
 dbt enables analytics engineers to write 
transformations using SQL
 Modular and reusable logic with clear dependencies
 Easy to test, debug, and document
www.visualpath.in • Encourages fast development cycles and collaboration
Collaboration & Governance
Title: Software Engineering Best Practices in dbt
 Version-controlled codebase using Git
 Automated documentation of models and lineage
 Built-in data quality tests for confidence in outputs
 Supports peer review and CI/CD pipelines
• Brings discipline and accountability to data transformation
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Performance & Scale
Title: Leverages the Power of Cloud Data Warehouses
 dbt compiles SQL and runs transformations directly 
in platforms like Snowflake, Big Query, and 
Redshift
 No need for a separate compute engine
 Scales with your warehouse’s performance
• Simplified architecture with fewer moving parts
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Title: Choosing Between dbt and Traditional ETL
When to Use What?
Scenario Use dbt Use Traditional ETL
Data modeling in warehouse Yes No
Extracting data from APIs No Yes
File ingestion and complex 
No Yes
workflows
Business logic in SQL Yes No
Complex non-SQL 
No Yes
transformations
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Summary & Takeaways
Title: dbt vs. ETL: A Paradigm Shift in Data Transformation
 dbt focuses on transformation after data is loaded
 Encourages clean, testable, version-controlled SQL-based 
pipelines
 Promotes better collaboration between data engineers and 
analysts
 Ideal for modern data stacks leveraging cloud warehouses
• Not a full ETL tool — but the gold standard for in-
www.visualpath.in warehouse transformation.
For More Information About 
DBT (Data Build Tool)
Address:- Flat no: 205, 2nd Floor,
   Nilagiri Block, Aditya Enclave,  Ameerpet, Hyderabad-16
   Ph. No: +91-998997107
     Visit: www.visualpath.in
      E-Mail: [email protected]
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Thank You
Visit: www.visualpath.in 
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