The database is like a library building ... every book (information) that enters must be properly placed (processed systematically), when a number of books come then the officer should be recorded like a book code, ISBN, publisher, author, book title, and others. If a bookcase is full then it must increase the amount, if one floor is full of bookcases then it should increase the number of floors, if one building is full of bookcases then it should increase the number of buildings.
In this case, Google is the admin that will direct you to the right bookcase based on your search, because there are so many bookcases, and many floors, with different buildings.
When you have the flu (cold sickness) and you are looking for a book on how to treat it, google will direct you to a bookcase about health, or modern and traditional medicine.
Of course, because of your limitations, you may not check the books one by one (for example in a health bookcase there has 5000 books).
From here, then the machine learning function is needed.
Suppose you find a way of treating cold sickness, then machine learning will give an answer:
"According to the books in this building that 90% recommend you take paracetamol, with the possibility to heal is 99.99%."
So with machine learning will become easier, faster (does not take much time), meaning you do not need to check the books one by one to find the answer.
This is called making decisions (not deterministic)
From here you begin to understand what is the difference between making a decision and what it is deterministic.
So if there is a question whether can make a company like google? then I answer it is very possible to do even better.
Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed https://www.datasciencecentral.com/profiles/blogs/big-data-and-machine-learning-google-case