##Overview:

In this Project, we have dealt with "Big Dataset" and through efficient use of Data Structure, we have manipulated the dataset and obtained the best time complexity for it. To Explain, we have used "B+ Trees" for the dataset that amounts for 1.8Gb in size and 2 millions+ in records. Since the B+ tree is a m-ary tree, we have efficiently increased the height to the optimal level through which time complexity reduced to less than O(logn).

Details:

we have integrated a secure login component in our project, in which only authorized user (Admin) can manipulate the data. The component is password-protected.

-TOP PRODUCTS -WORST PRODUCTS -BEST PRICE PRODUCTS -SEARCH PRODUCTS -DISPLAY PRODUCTS

Here we have two categories: Movies&Tv and Music. Both of these sums up to the size of 1.2Gb. Both of them have the features like:

-TOP PRODUCTS -WORST PRODUCTS -BEST PRICE PRODUCTS -SEARCH PRODUCTS -DISPLAY PRODUCTS

Here we have two categories: Software and Video Games . In the dataset, we have unsorted software's id, prices and ratings and reviews. Both of them have the features like:

With manipulating the data, Admin can see the best products, worst products, best price product ( in terms of price and rating) and can perform searching for a provided "Id". Top , worst and best product are found based on the users, reviews and rating.

The best time complexity for big data can only be achieved through trees especially BST. What about more dynamic (in terms of its branching) and self-balancing BST? Here, B+Trees comes into the picture to serve our needs. For 2M+ Data, B+ tree still gives us the best searching time complexity of O(logn) unlike Hashing and other searching data structure. Same is the case for data insertion in B+ trees.