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.

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.

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.

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.

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.

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.

A wide-column store NoSQL database is designed for efficiently storing and querying large volumes of data, typically organized in column families, making it optimal for analytical and big data workloads.

The process described involves converting the logical representation of data into a physical implementation, considering various factors such as storage mechanisms, indexing strategies, and partitioning schemes.

Data lineage in metadata management primarily serves the purpose of tracking the origin, transformation, and movement of data throughout its lifecycle. It provides insights into how data is sourced, processed, and utilized across various systems and processes, facilitating data governance, compliance, and decision-making. Understanding data lineage helps organizations ensure data quality, lineage, and regulatory compliance.

The logical data model focuses on defining entities, attributes, and relationships between entities, providing a structured representation of the data independent of any specific database technology or implementation.