Write data transformation steps

1. Analyze Source and Target Formats

1. Understand the Source Format:
   • Structure: Identify the data schema, including fields, data types, and relationships (e.g., CSV, JSON, XML, SQL tables).
   • Encoding: Verify the character encoding (e.g., UTF-8, ASCII).
   • Data Integrity: Assess completeness and consistency in the source data.
2. Define the Target Format:
   • Structure: Specify the schema or format requirements (e.g., JSON, SQL, or Excel).
   • Constraints: Note any requirements such as field lengths, data types, or relationships.

2. Data Extraction

• Extract Data:
  • Use appropriate tools to read the source data:
    • For flat files (e.g., CSV): Use libraries such as pandas in Python or tools like Excel.
    • For structured formats (e.g., SQL, XML): Query or parse the data using SQL commands or XML parsers.
    • For unstructured formats (e.g., logs, JSON): Use specialized parsers to extract meaningful information.

3. Data Cleaning and Preprocessing

1. Handle Missing Values:
   • Impute missing values or remove incomplete records based on the requirements.
2. Standardize Data:
   • Ensure consistent formats (e.g., date formats, numerical precision).
   • Apply standard naming conventions if necessary.
3. Remove Duplicates:
   • Identify and remove duplicate records to prevent redundancy.
4. Validate Data Types:
   • Ensure that all fields match the expected data types required by the target format.

4. Data Transformation

• Mapping Schema:
  • Define a mapping between the source and target fields (e.g., source_column_A -> target_field_X).
• Apply Transformations:
  • Convert data types (e.g., string to integer, or date to timestamp).
  • Normalize or denormalize datasets as needed.
  • Format data according to the target format (e.g., hierarchical structure for JSON, tabular for CSV).

5. Data Loading

1. Export to Target Format:
   • For JSON: Use libraries like json in Python to serialize the data.
   • For SQL: Use INSERT statements or bulk loading commands.
   • For Excel: Save using libraries like openpyxl or pandas.
2. Verify Output:
   • Compare the transformed data with the target schema to ensure compliance.
   • Perform sample checks for accuracy.

6. Validation and Testing

1. Data Integrity Checks:
   • Validate record counts to ensure no data loss during the transformation.
   • Cross-check key fields for consistency.
2. Schema Compliance:
   • Verify that all fields in the target format adhere to the required schema.
3. Performance Testing:
   • Evaluate the speed and efficiency of the transformation process for large datasets.

7. Documentation and Automation

1. Document the Process:
   • Maintain a log of transformation steps, including tools, scripts, and configurations used.
2. Automate Repetitive Tasks:
   • Use scripts or workflows (e.g., Python, ETL tools) for repeated transformations.