1. Scalability: Airflow is a workflow management platform while PostgreSQL is a relational database management system. Airflow is designed for orchestrating complex workflows, handling dependencies, and managing scheduling, making it more suitable for scaling up workflow operations. On the other hand, PostgreSQL excels in storing and managing structured data, providing advanced queries, and ensuring data integrity in a traditional relational database setup.

2. Functionality: Airflow focuses on coordinating workflows through Directed Acyclic Graphs (DAGs) where tasks are organized based on their dependencies and relationships. It enables the creation of dynamic workflows with conditional logic and branching. Conversely, PostgreSQL primarily deals with data storage and retrieval, offering features such as indexes, triggers, and stored procedures for managing and querying data efficiently.

3. Data Storage: While Airflow stores metadata related to workflows, task instances, and job scheduling in its backend database (e.g., SQLite, MySQL), PostgreSQL is a full-fledged database system that can store large volumes of structured data. Airflow uses its metadata database for operational purposes, whereas PostgreSQL is a robust solution for persistent data storage and retrieval.

4. Querying Capabilities: PostgreSQL provides a powerful SQL engine with support for advanced querying, indexing, and optimization techniques. Users can execute complex queries, perform aggregations, joins, and subqueries efficiently. Airflow, on the other hand, does not offer direct querying capabilities on its metadata database, focusing more on orchestrating workflows and task execution rather than data manipulation through SQL.

5. Community and Ecosystem: Airflow has a vibrant open-source community with a wide range of plugins, integrations, and extensions available for extending its functionality. Users can leverage the contributions from the community to integrate with various systems and tools. In contrast, PostgreSQL also has a strong community support but is more dedicated to database-related features and enhancements, with a focus on data management and storage technologies.

6. Use Cases: Airflow is commonly used for managing ETL pipelines, data processing workflows, and orchestration of tasks in a distributed environment. It is ideal for scenarios where workflow automation and scheduling are crucial. PostgreSQL, on the other hand, is well-suited for traditional database applications, such as transaction processing, reporting, data warehousing, and online analytical processing (OLAP) tasks that require robust data storage and retrieval capabilities.

In Summary, Airflow and PostgreSQL serve distinct purposes in the realm of data management, with Airflow specializing in workflow orchestration and scheduling, while PostgreSQL excels in data storage, retrieval, and advanced SQL querying functionalities.