DataFlow Architect-Airflow Pipeline Code Gen

Example

Designing a pipeline for aggregating daily sales data from multiple sources, transforming the data, and loading it into a data warehouse.

Scenario

In a scenario where a retail company needs to automate their sales data collection, DataFlow Architect would provide the Airflow code needed for each step of the ETL process, alongside explanations for why each task is performed in a certain way. This helps the team understand best practices for data extraction, transformations, and loading procedures.

Example

Annotating a complex data transformation task that involves joining multiple data sources and applying business logic.

Scenario

For a financial services firm looking to enrich transaction data with customer information from disparate systems, DataFlow Architect would offer detailed Python code with inline comments. These annotations explain each step of the process, from data extraction through transformation logic, helping the data team understand how to implement and maintain this pipeline efficiently.

Example

Guidance on setting up Airflow DAGs for optimal execution and maintainability.

Scenario

In a scenario where an organization is scaling its data processing capabilities, DataFlow Architect provides insights into structuring Airflow DAGs (Directed Acyclic Graphs) for better performance and easier future modifications. This includes advice on task dependencies, scheduling frequencies, and error handling, ensuring the data engineering team adopts industry-standard practices.