Which of the following best defines big data? There are as many answers to this as there are researchers studying its myriad ramifications. The short answer is: anything that can be analyzed using computers can be called big data. It encompasses everything from weather maps to customer demographic information. Data mining is the process of sifting through huge databases, both publicly and privately, to find specific patterns, trends or anomalies that may hold the key to profitable investing.
The short answer to the aforementioned question is a (n) yes. Anything that can be measured can be called big data. And in particular, anything that can be measured in a database, is a database approach. A good example is the detailed historical sales data compiled by independent websites and online retailers for the last 30 years or so.
Now then, we all know that there is no database approach which defines Big Data. However, there are certain characteristics that all good databases have in common. They contain data type descriptions, which are easily understood by any computer program capable of storing and retrieving such information. Additionally, they are organized in a way which makes it easy for programmers to construct different types of queries, which are again easily understandable by programmers using an equivalent software program.
Also, every well-designed database will include a primary key that is randomly generated. This primary key represents the “key” that a user accesses to gain access to all or part of the database. When the user accesses the data, a key search is performed on that specific data type. If a match is found, then the user is granted permission to proceed with the requested action.
So, now we know what a primary key is and what it does. We also know what a database consists of. What we still need to determine is what sort of entity our new system should be. And we also need to determine what sort of entity our new database should be – an order-of-magnitude binary search engine, or a data warehouse?
Well, as it turns out, when I was in the process of developing my first distributed database system, I discovered that my company’s mission was to improve customer service and make purchasing more efficient. Therefore, one of the first things I considered was how to best define a data type so that I would be able to better describe the ordering process to my system development team. In addition, one of the primary keys to be determined was whether to use a secondary key that was just a counter or a primary key. This led me to conduct much research into the appropriate data types for a Big Data system.
As it turns out, the question which of the following best describes a data type is this: is it possible to map the key components of a key using only the values that are already stored in the data warehouse? And in order to map the key components of the key components, it’s necessary to determine what each component looks like, in order to properly align them with the data warehouse. Fortunately, in this case, the answer was an unequivocal “yes!” because all three components of the key needed to have identical (or at very similar) values.
Now that I got that out of the way, I would like to share another answer to the previous question. Which of the following is not the appropriate system design for a business? A (n) is clearly not appropriate because most users will not be able to quickly tell the difference between a primary key and a secondary key. B is clearly not appropriate because many users will not be able to quickly tell the difference between a reference data element and an instance element. C is clearly not appropriate because any application designing a data access layer need to take into account both of the previous question (which of the two is best for what a business needs to do).