Feature parity is a challenging yet worthwhile goal while developing datastores. To reach this, we need to expand the functionalities of basic structures by wrapping them with utilities.

Consider a simple Python class BasicDataStore which is serving as our basic data structure. This basic data store comprises fundamental put and get operations for storing and retrieving data, drawing parallels with elementary fetch and write operations of data storage systems in finance or AI-driven software. For instance, storing and retrieving stock price data for IBM can be viewed as a microscopic model for larger, more complex data transactions.

To expand this structure and move towards achieving feature parity with sophisticated databases like Redis or MongoDB, you can start wrapping these basic structures with utilities. These utilities could be added functionalities for advanced data manipulations, data security, or performance optimization.

This process of expansion becomes an iterative loop, constantly refining our datastore and adding new utilities as needed, each time inching closer towards achieving feature parity.

We'll explore the details and specifics of these utilities in the forthcoming sections of this tutorial.

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if __name__ == "__main__":

    class BasicDataStore:

​

        def __init__(self):

            self.store = {}

​

        def put(self, key, value):

            self.store[key] = value

​

        def get(self, key):

            return self.store[key]

​

    ds = BasicDataStore()

    ds.put('IBM', 143.72)

    print(ds.get('IBM'))