Look around at the technology we have today, and it's easy to come to the conclusion that it's all about data. Organizations are flooded with data. Not only that, but in an era of incredibly cheap storage where everyone and everything are interconnected, the nature of the data we’re collecting is also changing. For many businesses and software development companies, their critical data used to be limited to their transactional databases and data warehouses.

As consumers, we have an increasing appetite for rich media, both in terms of the movies we watch and the pictures and videos we create and upload. We also, often without thinking, leave a trail of data across the Web as we perform the actions of our daily lives.

Not only is the amount of data being generated increasing, but the rate of increase is also accelerating. Not only software development firms, but from emails to Facebook posts, from purchase histories to web links, there are large data sets growing everywhere. The challenge is in Database designing   and extracting from this data the most valuable aspects; sometimes this means particular data elements, and at other times, the focus is instead on identifying trends and relationships between pieces of data.

There's a subtle change occurring behind the scenes that is all about using data in more and more meaningful ways. Large companies have realized the value in data for some time and have been using it to improve the services they provide to their customers, that is, us. Consider how Google displays advertisements relevant to our web surfing, or how Amazon or Netflix recommend new products or titles that often match well to our tastes and interests.

These corporations wouldn't invest in large-scale data processing if it didn't provide a meaningful return on the investment or a competitive advantage. There are several main aspects to big data that should be appreciated in a remarkable manner.

Some questions only give value when asked of sufficiently large data sets. Recommending a movie based on the preferences of another person is, in the absence of other factors, unlikely to be very accurate. Increase the number of people to a hundred and the chances increase slightly. Use the viewing history of ten million other people and the chances of detecting patterns that can be used to give relevant recommendations improve dramatically.

Big data tools often enable the processing of data on a larger scale and at a lower cost than previous solutions. As a consequence, it is often possible to perform data processing tasks that were previously prohibitively expensive.

In web development projects, the cost of large-scale data processing isn't just about financial expense; latency is also a critical factor. A system may be able to process as much data as is thrown at it, but if the average processing time is measured in weeks, it is likely not useful. Big data tools allow data volumes to be increased while keeping processing time under control, usually by matching the increased data volume with additional hardware.