1. Scalability: One key difference between Airflow and Apache Flink is their scalability. Airflow primarily focuses on task scheduling and orchestration, while Apache Flink is designed to handle large-scale data processing with real-time streaming capabilities. Apache Flink is built to scale horizontally and vertically, making it suitable for handling massive amounts of data and supporting high-throughput workloads.

2. Data Processing Model: Another significant difference is the data processing model used by Airflow and Apache Flink. Airflow uses a batch processing model, where tasks are executed at scheduled intervals. On the other hand, Apache Flink follows a stream processing model, meaning it can process data in real-time as it arrives, enabling near-instantaneous analysis and response to streaming data.

3. Fault Tolerance: When it comes to fault tolerance, Apache Flink provides strong guarantees for exactly-once processing semantics. It ensures data integrity by transparently handling failures and providing mechanisms to recover from failures, ensuring each event is processed exactly once. In contrast, Airflow focuses on fault recovery but does not offer the same level of support for exactly-once processing semantics.

4. State Management: Apache Flink includes a built-in state management feature that allows for storing and managing both key-value and stream state. This makes it possible to maintain the application's state across failures, ensuring continuity even in the event of unexpected incidents. Airflow, on the other hand, does not provide built-in state management capabilities, as it primarily focuses on task scheduling and does not require state persistence.

5. Use Cases: While Airflow is well-suited for workflow management and task scheduling, Apache Flink is often preferred for data-intensive and real-time streaming applications. Apache Flink is frequently used in scenarios where data processing needs to be done in near real-time, such as fraud detection, real-time analytics, and continuous data processing. Airflow, on the other hand, is commonly used for ETL (Extract, Transform, Load) workflows and data pipeline orchestration.

6. Community Size and Maturity: Airflow has been around for a longer time and has a larger community compared to Apache Flink. Airflow has an extensive ecosystem, including numerous plugins and integrations, and a mature community that actively contributes to its development. However, Apache Flink has gained significant traction in recent years and has a rapidly growing community that continues to enhance its capabilities.

In Summary, Airflow primarily focuses on task scheduling and workflow management, while Apache Flink is a powerful data processing system with real-time streaming capabilities. Apache Flink offers scalability, a stream processing model, strong fault tolerance, built-in state management, and is often used for real-time data-intensive applications. Airflow, on the other hand, is well-suited for workflow management, ETL, and data pipeline orchestration.