In a data governance framework, the process of defining policies, procedures, and standards for data management falls under the domain of Data Governance. Data governance encompasses the establishment of overarching principles and guidelines for managing data effectively across the organization. It involves defining rules and best practices to ensure data is managed, accessed, and used appropriately to support organizational objectives while maintaining compliance and mitigating risks.

Web Scraping is a data extraction technique used to extract data from semi-structured or unstructured sources like emails, documents, or social media platforms, enabling analysis and processing of the data.

GDPR stands for General Data Protection Regulation, a comprehensive European Union (EU) legislation designed to protect the privacy and personal data of EU citizens and residents. It imposes strict requirements on organizations handling personal data, including consent mechanisms, data breach notification, data subject rights, and hefty fines for non-compliance, aiming to harmonize data protection laws across the EU and empower individuals with greater control over their personal information.

In an ERD, a one-to-one relationship indicates that each instance of one entity can be associated with only one instance of another entity, and vice versa. It's represented by a straight line between the entities.

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.

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.

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.

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.

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.

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.

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.

In Apache Flink, Stream Processing allows for processing large volumes of data in a fault-tolerant and low-latency manner. Flink's stream processing capabilities enable real-time data processing by dividing data into continuous streams and processing them incrementally. This approach ensures fast processing with low latency and fault tolerance, making it suitable for various real-time analytics and event-driven applications.