Materialized view is a caching strategy commonly used in database systems to store frequently accessed query results in memory. It precomputes and stores the results of queries as tables, allowing for faster retrieval and reduced load on the underlying database by avoiding repeated execution of expensive queries. This helps in improving query performance and overall system scalability.

The BASE (Basically Available, Soft state, Eventually consistent) model in NoSQL databases prioritizes availability and partition tolerance over immediate consistency. It allows for eventual consistency, meaning that updates are propagated to all nodes eventually but may not be immediately reflected across the system.

Data Splitting is a transformation technique used to split a single data field into multiple fields based on a specified delimiter, such as a comma or space. It's commonly used in data preprocessing tasks.

Apache Kafka is a distributed messaging system known for its high throughput, fault-tolerance, and scalability. It is commonly used in real-time data processing scenarios for building data pipelines and streaming applications, where it facilitates the ingestion, processing, and delivery of large volumes of data with low latency and high reliability.

Replicating the database across multiple regions helps distribute the workload geographically and improves fault tolerance and disaster recovery capabilities. It enhances data availability by allowing users to access data from the nearest replica, reducing latency. Additionally, it helps maintain consistency through mechanisms like synchronous replication and conflict resolution strategies.

In the event of a data breach, it's crucial to take immediate action to mitigate its impact and comply with regulations. This includes notifying affected customers promptly to mitigate potential harm, conducting a thorough investigation to understand the breach's scope and root cause, enhancing security measures to prevent future incidents, and reporting the breach to relevant authorities as required by law. Transparency, accountability, and proactive remediation are essential to rebuilding trust and minimizing regulatory penalties.

Decomposition is the process in normalization where a large table is broken down into smaller tables to reduce redundancy and improve data integrity by eliminating anomalies.

GDPR (General Data Protection Regulation) is a comprehensive framework designed to protect the privacy and personal data of individuals within the European Union (EU) and the European Economic Area (EEA). It establishes guidelines for organizations on data processing, storage, and transfer, as well as requirements for obtaining consent, notifying data breaches, and appointing data protection officers. Compliance with GDPR is essential for organizations handling EU/EEA citizens' data to avoid hefty fines and maintain trust with customers.

The conceptual data model is represented using graphical notations such as entity-relationship diagrams (ERDs), which depict high-level relationships and concepts within the data model.

Monitoring tools facilitate optimizing data pipeline performance by identifying performance bottlenecks and inefficiencies. These tools continuously track and analyze various metrics such as data latency, throughput, resource utilization, and error rates, enabling data engineers to pinpoint areas for improvement, streamline workflows, and enhance overall pipeline efficiency and scalability. By proactively monitoring and addressing performance issues, organizations can ensure optimal data processing and delivery, meeting business requirements and objectives effectively.