Create data pipeline documentation

Pipeline Stages

1. Data Extraction

• Source: MySQL Database containing transactional data (e.g., sales records).
• Extraction Method: Use an ETL tool such as Apache Airflow or a custom Python script with the mysql-connector library to connect to the source database.
• Frequency: Data is extracted incrementally every 24 hours using a timestamp field to capture only new or updated records.

2. Data Transformation

• Transformation Tool: Data transformation is performed using AWS Glue to clean and standardize the data.
• Processes:
  • Convert date formats to ISO 8601 for consistency.
  • Remove duplicate records and resolve inconsistencies.
  • Aggregate sales data to prepare for warehouse schema requirements.
• Validation: Perform validation checks for missing values, data type mismatches, and referential integrity.

3. Data Loading

• Target: Amazon Redshift, optimized for analytical queries.
• Loading Method: Use the COPY command in Amazon Redshift, which ingests data directly from S3 storage buckets.
• Process:
  • Transformed data is written to Amazon S3 in CSV or Parquet format.
  • Redshift pulls data from S3 using secure credentials.

4. Monitoring and Logging

• Implement monitoring with Amazon CloudWatch to track pipeline performance and detect errors.
• Log failures and retry attempts in Apache Airflow or a custom logging system.