Big data has become a buzzword in the last few years, with the development of large-scale and fast growing databases and processing power. What exactly is it? It is an umbrella term for several technologies and tools that facilitate the extraction of large amounts of data from a distributed system. This data may include structured or unstructured data, such as in the case of big data warehouses used by e-commerce companies. In addition, it may involve time-series data, such as that obtained from customer service applications.
One tool that helps extract big data and apply it to various analytics problems is a tool called a big data analyzer. Such tools extract key features of the data, and in turn can make the data much more presentable to users. Such tools may analyze different types of data, such as streaming data, grid data, or historical data. They can even provide reports on different dimensions of big data, such as demand and supply chain, quality, and profitability, among others. Such tools may even help business executives improve their overall strategic thinking, and give them insights into how they should adapt their current business strategies.
However, big data has its downsides. One of the biggest is that it eats up valuable man hours. Since big data analysis and extraction is difficult, it will necessarily consume a lot of time. Likewise, the results of such analysis or extraction may not be immediate, or are not reliable for certain applications. Lastly, big data warehouses may consume a significant amount of cloud storage space, and thus may affect the company’s ability to respond quickly to changing market conditions or competitors’ activities.
Thus, although big data has great potential, it does have some pitfalls that need to be addressed. One is the limitation of big data analytics to internal reporting, which may not accurately reflect external conditions. Another is that the tools may only provide very coarse or very detailed information, which may affect the accuracy of forecasts and other business projections. Yet another is that big data visualization is limited to either a desktop or a web browser. This limits its analytical power.
So how can one exploit big data? How do you extract the maximum value out of it? As mentioned earlier, one of the key benefits of big data warehousing is its ability to provide insights that cannot be provided by traditional analysis and research methods. For example, retail sales trends can be predicted in great detail using retail sales tracking data, and vice-versa. However, it takes a trained expert to predict the pattern in the data, and that expert is not available to every retailer. A big data warehouse can provide this expert knowledge, since it contains terabytes and petabytes of data, which can be accessed via a web browser.
Big data also allows businesses to leverage computing power to its fullest potential. This means that companies who have traditionally had IT professionals as their only employees-or at least, the ones who could afford them-may now access highly sophisticated technology, specifically servers, to run applications. For e-commerce, this means that a business owner who has traditionally relied on expensive IT solutions may now access server-based software and start automating his e-commerce process. The biggest advantage of big data automation is its efficiency. It is capable of handling tasks that were once handled by human workers, and by combining various small tasks into a large overall process, the overall efficiency increases, making it more cost-effective for the business.
In the last few years, there has been an increasing focus on big data warehouses. These are very much related to Hadoop, but they are distinct from one another. What is the difference between a big data warehouse and Hadoop? The former creates applications on the nodes of a cluster, while Hadoop uses the entire network to function efficiently.
So what is it? To answer the question above, one must be able to explain what big data warehouse is and what it can do for the enterprise. According to experts in the field, it is an advanced form of Hadoop that makes use of a central data repository containing terabytes of data across thousands of devices. This enables the system to achieve a higher degree of parallelism, delivering more value to its users and enabling new business models based on big data.