Welcome to my DBT BigQuery Project. In this initiative, I will showcase my ability to leverage data transformation tools to model and analyze datasets effectively within Google BigQuery. This project serves as a demonstration of my expertise in utilizing modern data engineering techniques, aiming to derive meaningful insights, facilitate efficient data collection, and ensure a seamless transition in the realm of data analytics. Join me as we navigate the complexities of data modeling and analytics through the lens of DBT and BigQuery.
DBT (data build tool) is a command-line tool that enables data analysts and engineers to transform data in their warehouse more effectively. By building models and executing transformations directly in a SQL-based environment, DBT allows for enhanced data accessibility and analysis.
In this project, I focus on creating models related to customer data and order transactions. The end goal is to facilitate data quality assurance, improve accessibility, and create a robust framework for ongoing data analysis. This project highlights the potential of DBT to manage transformations efficiently while ensuring data integrity through various testing methodologies.
This architectural framework ensures a streamlined, automated, and scalable approach to handling data, from its extraction to transformation and analysis.
- DBT: For data modeling and transformation.
- Google BigQuery: Cloud-based data warehouse for storing and querying large datasets.
- Git: Version control system to manage code changes and collaborate.
- CI/CD Tools: To automate the deployment of changes and ensure quality in the pipeline.
- Clone the repository:
git clone <repository_url> cd <project_directory>
The result of this project will be a set of well-defined models that provide insights into customer and order data, enabling better decision-making and analysis.
Requirements DBT
Access to Google BigQuery
Git