Which of These Sources is Likely to Produce Big Data?

Today’s IT organisations are under increasing pressure to optimise their data infrastructure and how they collect, process and utilise it. This has become especially critical as companies realise the potential to achieve significant cost savings by optimising their data centres. Companies that implement big data solutions are also seeing a return on their investment in terms of increased productivity, improved customer service and reduced IT staff costs. However one of the key areas for concern is how to identify the fastest and most cost-effective source for analytics – particularly when facing a budget constrains.

which of the following sources is likely to produce big data the fastest

The two main sources of analytics are either processing power or storing and analyzing massive amounts of data. While both of these technologies have the potential to drive significant business value through better understanding of customer behavior, the time required to optimise these technologies is quickly becoming a critical factor in company competitiveness. In order to gain an upper hand in today’s competitive environment it is vital to establish the fastest and most cost-effective analytics platform. Today two technologies are proving to be the most viable answers to this question. They are Infra-red analytics and streaming analytics.

Infra-red analytics refers to the collection, processing and utilising of information derived from radio-frequency in-sockets. Most large databases utilise such technology as they are capable of reliably delivering large amounts of processed data in real time. Such databases typically comprise multiple nodes that each operate separate applications with associated databases. One instance of this technology is a live, streaming grid computing application. When large amounts of analytics are needed to provide insight into a specific piece of information, a live grid is used.

Streaming analytics on the other hand is based on the idea of ingesting real-time data, processing it and then providing information that can be instantly analyzed. The concept is similar to what is done with web feeds – data scientists collect information, process it and make available to the right users. However, unlike web feeds, which are often updated, data warehouses are maintained permanently. They may be automatically updated whenever new or relevant pieces of data are added or removed from the system. For example, data warehouses may constantly store customer demographics and their patterns of purchasing. This is a valuable data set which can be used by data scientists and actuators.

Perpetual analytics is another concept which deals with how certain systems and algorithms function. Such systems may be based on algorithms which search through large databases, but due to their nature, are unable to answer questions that cannot be posed at the initial stage of the search. As the search continues, more answers are provided to the user, until eventually, the right answer is found. However, it may take a very long time, and as such this falls under the category of a temporary measure which is not really a reliable solution, but rather a good medium between short-term and long-term solutions.

Mapreduce is another way of asking the question: which of the following sources is likely to produce big data the fastest. Mapreduce is a framework, which was originally developed at Facebook, where engineers were able to identify areas of data which were low in reference memory but were still potentially useful. The main use of Mapreduce at Facebook was to build out large graphs from these reference memory regions, but it was later adapted by Twitter and Netflix to make it possible to also analyze big data from these areas. The framework is designed around mathematical algorithms that can be easily adapted for use in data analysis. In fact, Mapreduce even works well with small and relatively stationary domains, where the distribution of values is predictable.

Occam’s law states that the probability of a hasty data recovery from a finite set of parameters is proportional to the square of the input error. As we are learning, computing and monitoring have changed dramatically, and in order to apply Occam’s law to data recovery from Hadoop, Mapreduce may be a better fit. Hadoop is based on the Map algorithm, which has been adapted for use in streaming analytics and web analytics. It operates on highly parallel nodes using TensorFlow and graph algorithms, which are easy to extend and modify for various purposes.

Mapreduce has the ability to analyze and process large amounts of data, thanks to the parallel distributed processing engines it uses. It is designed to scale up from small tasks, which it processes on a single computer node, up to even thousands of them. It achieves this by collecting and analyzing data across multiple machines. It can be run on the mainframe on its own or as part of a cluster which contains many machines.