To provide some real-world colour to our theoretical discussion, let's examine how CRDTs have been used in industry applications.

Collaborative Editing Applications such as Google Docs and Microsoft Office Live use CRDTs to handle concurrent updates from multiple users. Without CRDTs, these applications would struggle to accurately merge changes or conflicts. In the same vein as choosing the right film for a movie marathon, picking the right CRDTs allows these tools to track changes in a flexible and efficient manner.

Just like the complexities of trading strategies in finance, Distributed Databases often employ CRDTs to maintain consistency while coping with network partitions. For example, Riak, a distributed NoSQL database uses CRDTs to allow concurrent writes that will eventually converge.

Distributed File Systems such as IPFS (InterPlanetary File System) use CRDTs to maintain replicas of files. It's akin to maintaining a backup of your favourite movie collection – making sure your data is always available, come what may.

Now, let's illustrate with some Python code a basic CRDT simulation. Assuming we're building a basic travel planning app - making use of CRDT to handle collaborative changes.

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if __name__ == '__main__':

​

    class CRDT:

        def __init__(self):

            self.locations = set()

​

        def add(self, location):

            self.locations.add(location)

​

        def remove(self, location):

            self.locations.discard(location)

​

        def query(self):

            return self.locations

​

    crdt = CRDT()

    crdt.add('Paris')

    crdt.add('Rome')

    print(crdt.query())

    crdt.remove('Paris')

    print(crdt.query())