⚙️ Building Data Transformation with dbt
LinkedIn Post — March 20, 2026
🔄 I completed two projects focused on transforming data using dbt to deepen my understanding of modern data transformation practices. Through these projects, I learned the difference between dbt Core and dbt Cloud — dbt Core is open-source and runs locally via CLI, while dbt Cloud provides a managed environment with scheduling, a user interface, and collaboration features. I also explored how dbt handles data inputs: sources are used to reference existing tables in the data warehouse, while seeds are CSV files stored in the project and loaded into tables, typically used for static or reference data.
🧪 I also practiced testing in dbt to ensure data quality and reliability. There are two main approaches:
• Singular tests → custom SQL queries to validate specific logic
• Generic tests → reusable tests such as not null, unique, and relationships to enforce data integrity across models
🔗 In addition, I used dbt docs to visualize data lineage and better understand the relationships between models. These projects helped me build a more structured, testable, and maintainable transformation layer using dbt.
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#DataEngineering #dbt #DataTransformation #AnalyticsEngineering #SQL #ETL #ELT #DataPipeline #DataModeling #DataQuality #ModernDataStack #CloudData #DataLineage #DataTesting
