Data Transformation and Structure Creation
Posted: Wed Jun 18, 2025 3:58 am
* **Data Cleaning Techniques:** The extracted data underwent rigorous cleaning to resolve inconsistencies and errors. This included handling missing values (using imputation methods), standardizing formats (e.g., converting dates to a consistent format), and correcting typos. Regular checks and validations were implemented throughout this process.
* **Data Validation:** We implemented robust data validation rules to ensure the integrity of the cleaned data. This included checks for data type consistency, range validation, and uniqueness constraints. Validation was crucial for maintaining data quality throughout the project.
This critical phase involved converting the cleaned data into the desired relational format.
* **Data Modeling:** A comprehensive data model was created to define the tables, columns brother cell phone list and relationships within the target database. This model was meticulously designed to support the identified use cases.
* **Transformation Logic:** Custom scripts were developed using Python and SQL to transform the extracted data. These scripts performed tasks such as splitting fields (e.g., combining full names into separate first and last name columns), converting values to appropriate data types (e.g., converting strings to dates), and creating new calculated fields.
* **Data Validation:** We implemented robust data validation rules to ensure the integrity of the cleaned data. This included checks for data type consistency, range validation, and uniqueness constraints. Validation was crucial for maintaining data quality throughout the project.
This critical phase involved converting the cleaned data into the desired relational format.
* **Data Modeling:** A comprehensive data model was created to define the tables, columns brother cell phone list and relationships within the target database. This model was meticulously designed to support the identified use cases.
* **Transformation Logic:** Custom scripts were developed using Python and SQL to transform the extracted data. These scripts performed tasks such as splitting fields (e.g., combining full names into separate first and last name columns), converting values to appropriate data types (e.g., converting strings to dates), and creating new calculated fields.