What's included

• Data architecture diagrams and documentation

  Clearly defined architecture outlining data storage, processing, and flow. This includes data warehouse design, data lake architecture, and integration points.

• ETL scripts, workflows

  Implementing ETL processes to move data from source to destination, applying necessary transformations along the way. This involves scripting or configuring tools for data extraction and transformation.

• Data Quality Framework

  Establish and document rules and standards for data quality. Implement monitoring systems to ensure data quality is maintained over time.

• Monitoring dashboards, logging configurations

  Set up systems for real-time monitoring of data pipelines, including performance metrics, error tracking, and alerts. Configure logging for troubleshooting and auditing.

• Test scripts, testing documentation

  Develop and implement automated testing frameworks to validate the accuracy and reliability of data pipelines, ensuring that changes to the infrastructure do not introduce errors.