Apache Airflow offers several advantages over traditional scheduling tools for data workflows. It provides dynamic workflow scheduling, allowing for the definition and execution of complex workflows with dependencies. Built-in monitoring and logging capabilities facilitate better visibility and debugging of workflows. Airflow is highly scalable, capable of handling large-scale data processing tasks efficiently. Its dependency management features ensure that tasks are executed in the correct order, improving workflow reliability and efficiency.

Implementing retry mechanisms in data processing systems is significant for ensuring fault tolerance. Retry mechanisms automatically retry failed tasks, helping systems recover from transient failures without human intervention. This enhances system resilience and reliability, reducing the impact of temporary disruptions on data processing workflows and ensuring consistent data delivery and processing.

Breaking down a large table into smaller tables in normalization helps reduce data redundancy by organizing data into logical groups, thereby improving data integrity and making the database easier to manage.

Apache Airflow allows users to define workflows using Python code. Python provides a powerful and flexible language for defining tasks, dependencies, and other workflow components in Airflow. By leveraging Python, users can express complex workflows in a concise and readable manner, enabling easier development, maintenance, and extensibility of Airflow workflows.

Best practices for managing metadata in a Data Lake include maintaining a centralized metadata repository with a data catalog that offers search capabilities. This enables efficient discovery and understanding of available data assets, facilitating data exploration and analytics initiatives.

The primary goal of distributed computing is scalability, which involves efficiently handling increased workloads by distributing tasks across multiple interconnected nodes or computers. This approach allows for better resource utilization, improved fault tolerance, and enhanced performance compared to traditional centralized systems.

Apache Spark supports multiple programming languages including Scala, Java, and Python, making it accessible to a wide range of developers and allowing them to work with Spark using their preferred language.

Enabling job checkpointing in Apache Flink is essential for ensuring high availability and fault tolerance. Checkpoints allow Flink to persist the state of the streaming application periodically, enabling recovery from failures by restoring the state to a consistent point in time. This ensures that processing can resume without data loss or duplication.

Data Wrangling is the process of cleaning, structuring, and enriching raw data to make it suitable for analysis. It involves tasks such as cleaning inconsistent data, handling missing values, and transforming data into a format usable for analysis.

Data Integration is the process of combining data from various sources into a unified view, ensuring consistency and coherence in Dimensional Modeling. This step is crucial for building a comprehensive data model.