Big Data and Web Development
We live in an era of Big Data, which involves sets of information so large, normal database applications are thought to be unable to bear the load. While some attempt to approach the problem with a hardware solution, searching for online components that will aid them in building the next electronic device designed for massive data crunching procedures, such efforts may not always accommodate web based applications for wrestling our way through truly enormous big data structures.
For the web to truly be useful in manipulating such enormous data structures, how we think about data and develop software to manage it may require a huge paradigm shift–especially in cases where there just is not enough raw processing power to go around. In such cases, more symbolic methods of data manipulation may be key to web development efforts, dealing with data by inference and dynamic topological structuring than by a more direct approach to retrieving information we desire to have in hand at a moments notice.
The Power of Substitution
Programmers recognize that to constantly toss around huge amounts of data, every time you are making a software request, isn’t necessarily the most efficient way to handle data retrieval procedures. In fact, tossing around addresses where that data is located may be far more useful than tossing around the data itself. In this fashion, we only need to actually access the data in question, if we are in need of manipulating that data in some significant way. But, if our goal is to only acknowledge that it exists, or to grab and read through a set of parameters associated with such an address, rather than sifting through the entire block of data, then data retrieval ends up being done on a basis of how deep we need to probe through the data to satisfy our initial request. Hence, increasing the length of the address that serves as a substitute for the portion of data being accessed identifies the limits on the depth of our data retrieval process.
Data Frequency Factors
Another way to cut down on the time it takes to search through massive data structures is to weight data relationships. If each portion of the data you are searching through is weighted with respect to other information it is frequently used with, two such structures can share a relational frequency address, causing these portions of the data structure to tend to gravitate towards each other, the more often they are pulled up in searches, increasing the value of their relevant relation to each other. Hence, web developers can use this weighting as a basis to assume that if a piece of information is being pulled up, anything that has a strong relational frequency rating to that piece of information will likely be needed as well. Thus preventing the need to go digging through the bulk of the data unnecessarily, unless the need arises. Hence, frequent relations become a basis for constructing relevant tables of information based on its regularity of use by the web based software. In this fashion, data will tend to topologically colonize where it is relevant, excusing the need to go hunting very far for that data. Therefore, the more people who access this data, the easier it becomes for the software to determine what portions of the data set tends to share relevance to the majority of users of that data structure.