In data loading, the process of transforming data from its source format into a format suitable for the target system is commonly referred to as ETL (Extract, Transform, Load).

A relationship line between two entities in an ERD indicates an association between them, specifying how instances of one entity are related to instances of another entity within the database model.

Data lineage provides a detailed trace of data flow from its source to destination, allowing users to understand how data moves through various systems, transformations, and processes. It helps ensure data quality, compliance, and understanding of data dependencies within an organization.

A leader election algorithm in distributed systems is responsible for selecting a process or node to act as the leader, which assumes the responsibility of coordinating activities and making decisions on behalf of the distributed system. The leader plays a crucial role in ensuring orderly execution of distributed algorithms, managing resource allocation, and maintaining system stability. Leader election algorithms help prevent conflicts, establish a single point of authority, and enable efficient coordination among distributed nodes.

A Full Outer join returns all rows from both tables, joining records where available and inserting NULL values for missing matches. It combines the results of Left and Right Outer joins.

Ensuring data consistency and accuracy during data integration involves steps such as data profiling and cleansing to identify and rectify inconsistencies, implementing validation rules, and establishing documentation processes to maintain data lineage and traceability.

Change Data Capture (CDC) is a data extraction technique that involves extracting data from a source system's log files in real-time, enabling near real-time analysis and processing of the captured data.

NoSQL databases typically adopt the eventual consistency model, where updates to the data propagate asynchronously, providing a higher level of availability and partition tolerance at the expense of consistency.

Metadata management tools often use auditing mechanisms to track changes in data lineage over time. Auditing helps monitor modifications, access, and usage of metadata, ensuring accountability, compliance, and data governance. It provides a historical record of metadata changes, facilitating troubleshooting and maintaining data lineage accuracy.

Apache Airflow would be recommended due to its DAG-based architecture, which enables the definition of complex workflows with dependencies between tasks. It provides a flexible and scalable solution for orchestrating data pipelines, allowing for easy scheduling, monitoring, and management of workflows. Additionally, Airflow offers a rich set of features such as task retries, logging, and extensibility through custom operators and hooks.

Data profiling analysis involves examining the quality and characteristics of data within a dataset. It assesses various aspects such as consistency, correctness, completeness, and uniqueness of data values. Through data profiling, data engineers can identify anomalies, errors, or inconsistencies in the dataset, which is crucial for ensuring data quality and reliability in subsequent processes like cleansing and integration.

In Kafka, Zookeeper is responsible for storing the committed offsets of the consumers. Zookeeper manages various aspects of Kafka's distributed system, including coordination and metadata management.