Change Data Capture (CDC) is a data extraction technique that involves reading data from a source system's transaction log to capture changes since the last extraction, enabling incremental updates to the data warehouse.

The metadata repository acts as a centralized storage and access point for all metadata related to an organization's data assets, including data lineage information. It serves as a repository or database where metadata is collected, managed, and made accessible to users and systems across the organization. By centralizing metadata in a repository, organizations can ensure consistency, accessibility, and integrity of metadata, facilitating effective data management and governance practices.

NoSQL databases typically offer a flexible schema, allowing for the storage of various types of data without the need to adhere to a rigid structure like in traditional relational databases.

Sharding involves partitioning data across multiple database instances, allowing for horizontal scaling and distributing the workload evenly. It enables the system to handle increased traffic by spreading data and queries across multiple servers. This approach enhances scalability and performance by reducing the load on individual database servers.

One of the primary features that distinguish NoSQL databases from traditional relational databases is horizontal scalability, which allows them to efficiently handle large volumes of data by adding more nodes to the database cluster.

Data Accuracy measures the extent to which data is free from errors, inaccuracies, or mistakes. It evaluates the correctness of data values in relation to the real-world entities they represent. High data accuracy ensures that the data reflects the true state of the system and supports informed decision-making and analysis.

Anomaly detection in data pipeline monitoring involves identifying and analyzing deviations from the expected behavior of the pipeline. This process often employs statistical techniques, machine learning algorithms, or predefined rules to detect unusual patterns or outliers in the data flow, which may indicate errors, bottlenecks, or data quality issues within the pipeline.

In response to a security breach in a Data Lake, an organization should enact its incident response plan, implement data encryption to protect sensitive data, enforce access control policies to limit unauthorized access, and conduct security auditing to identify vulnerabilities. Preventative measures may include regular data backups, disaster recovery plans, and data masking techniques to obfuscate sensitive information.

Least connections routing is a load balancing technique that distributes incoming requests across multiple servers based on the current number of active connections. Servers with fewer connections receive more requests, helping to evenly distribute the workload and prevent any single server from becoming overwhelmed. This approach promotes efficient resource utilization and enhances system reliability by preventing overload on individual servers.

Choosing between batch loading and real-time loading involves weighing factors such as data freshness versus processing overhead. Batch loading may offer higher throughput but lower data freshness compared to real-time loading.

The Map phase in Hadoop MapReduce takes input data and processes it to generate key-value pairs, which are then passed to the Reduce phase for further processing. It involves tasks like data parsing and filtering.

Data validation is crucial in ensuring that the loaded data meets quality standards and integrity constraints. It helps prevent errors and inconsistencies, ensuring the reliability of downstream processes.