Project Title: Comprehensive Data Transformation and Analysis of German Credit Data Using dbt

Project Objectives:

• To enhance my expertise in dbt by implementing a variety of advanced data transformation techniques on the German Credit Data.
• To design and build a robust data transformation pipeline that efficiently processes raw data into insightful, actionable reporting tables.
• To utilize dbt’s powerful features such as models, snapshots, tests, and macros to ensure data quality, traceability, and maintainability.
• To document every step of the transformation process for future reference and ensure that the final models are optimized for performance and scalability.

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High Level Overview

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Project Outline:

1. Initial Setup

• Project Initialization: I have initialized a dbt project named german_credit_analysis and established a connection to my PostgreSQL database.
• Schema Organization: I've organized the project by creating necessary folders for staging, intermediate models, aggregates, snapshots, and marts within the models directory.

2. Staging Model

• Creating the Staging Model:
  • I developed the stg_german_credit_data model in the staging folder. This model is responsible for cleaning and standardizing the raw credit data, ensuring that all categorical values are properly mapped, null values are handled, and data types are appropriately cast.

3. Intermediate Transformations

• Credit Scoring Model:
  • I created the int_credit_scores model to calculate a custom credit score based on the features provided in the dataset. This model applies specific scoring logic to assess the creditworthiness of each customer.
• Customer Segmentation:
  • I developed the int_customer_segmentation model to segment customers based on their credit score, age, and other demographic factors. This segmentation is crucial for identifying different risk profiles among customers.
• Default Risk Prediction:
  • I implemented the int_default_risk model, which predicts the likelihood of a customer defaulting on their credit. This model integrates the risk segmentation with other variables to provide a comprehensive risk assessment.

4. Advanced Transformations

• Historical Snapshot:
  • I set up a snap_credit_scores model to track changes in customer credit scores over time. Using dbt’s snapshot feature, I can monitor trends and shifts in creditworthiness across different periods.
• Aggregations and Summarization:
  • I created the agg_credit_summary and agg_segmented_default_rates models to summarize the credit data. These models calculate averages, sums, and default rates by customer segment and age group, providing valuable insights for further analysis.

5. Data Quality Testing

• Implementing Column Testing:
  • I have incorporated dbt tests in the schema.yml file to ensure data quality across the models. Tests include checks for uniqueness, non-null values, and accepted values for categorical columns.
• Creating Custom Tests:
  • I also developed custom tests to detect logical inconsistencies, such as customers with zero income but high credit amounts, ensuring the integrity of the data.

6. Documentation and Deployment

• Documenting the Project:
  • I have thoroughly documented all models using dbt’s documentation features. Each model, column, and test is described in detail within the schema.yml file. I can generate and view the documentation using dbt docs generate and dbt docs serve.
• Deploying the Models:
  • I ran the dbt models using dbt run to deploy them to the database. The final reporting tables are now ready for analysis and visualization.

7. Enhancing with Macros

• Creating Reusable Macros:
  • I have written reusable macros that simplify repetitive tasks within the project. These macros are applied across multiple models to ensure consistency and efficiency in the transformations.

8. Final Review and Optimization

• Optimizing Model Performance:
  • I reviewed and optimized the SQL queries in each model to ensure they run efficiently. I utilized materializations like incremental and table to improve performance where necessary.
• Integrating CI/CD:
  • If applicable, I would integrate this project with a CI/CD pipeline to automate testing and deployment, ensuring that future changes are seamless and well-managed.

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Deliverables:

• Complete dbt Project: A well-structured dbt project with all necessary models, snapshots, tests, macros, and documentation.
• Transformed Data Tables: Finalized tables in the PostgreSQL database that are ready for analysis and reporting.
• Comprehensive Documentation: Detailed dbt documentation covering all transformations and processes involved in the project.

This project has not only deepened my understanding of dbt but also provided a comprehensive approach to transforming and analyzing credit data in a scalable, maintainable way.