Assignment Question: You are tasked with designing a distributed database system for a multinational company that needs to store, retrieve, and manage data in real-time from multiple locations across the globe. Discuss the fundamental principles of designing such a system, the challenges faced, and potential solutions. Also, implement a basic algorithm to handle data consistency across different nodes.

Solution:

Fundamental Principles:

1) Data Distribution: Distribute data across various nodes (servers) to ensure quick access regardless of location. Data can be distributed using techniques like sharding or partitioning.

2) Replication: Replicate data across nodes to ensure fault tolerance and data availability.

3) Consistency: Ensure that all nodes have the most recent and consistent version of the data.

4) Scalability: Design the system to handle growing amounts of data by adding more nodes.

5) Latency: Optimize for minimal latency when accessing data from any global location.

Challenges:

1) Network Issues: Different parts of the world might experience varying network speeds and connectivity issues.

2) Data Consistency: Ensuring real-time data consistency across distributed nodes can be complex.

3) Hardware Failures: Servers or nodes can go offline due to hardware issues.

4) Security Concerns: Protecting data integrity and privacy across various global data centers.

5) Scalability: Efficiently adding more nodes as the data grows can be challenging.

Potential Solutions:

1) Distributed Hash Tables (DHT): Efficiently locate data across nodes.

2) Gossip Protocols: Use these for nodes to communicate and sync data.

3) Data Versioning: Assign version numbers to data entries to manage updates and maintain consistency.

4) Redundancy: Keep multiple copies of data to ensure system reliability.

5) Geographically Distributed Data Centers: Place data centers in strategic locations to reduce access latency.

Basic Algorithm for Data Consistency:

function ensureDataConsistency(node, data):

    latest_version = getLatestVersion(data)

    if node.version(data) < latest_version:

        updated_data = getUpdatedDataFromOtherNodes(data, latest_version)

        node.update(data, updated_data, latest_version)

    end if

    for each neighboring_node in node.neighbors:

        if neighboring_node.version(data) < latest_version:

            sendUpdateToNode(neighboring_node, data, updated_data, latest_version)

        end if

    end for

end function