Apache’s Hadoop is an open source data solution that allows for the distributed processing and storage of very large data sets. But whether it can replace your company’s data warehousing solution depends on your data warehousing and data processing needs. Hadoop is a specialized program that can facilitate some times of data warehousing but is not an all-in-one data warehousing solution.
The Advantages of Apache’s Hadoop
Apache’s Hadoop is able to detect faults on a software layer; it does not rely upon commercial grade technology. By being able to detect and correct faults, Hadoop offers better reliability and accuracy. Apache’s Hadoop is also intensely scalable, able to scale from a handful of machines to thousands of machines. Thus, it can very easily be used for projects that anticipate scaling upwards significantly but do not want to allocate those resources just yet. As an open source solution, Hadoop can also be modified and improved as the developer sees fit.
Hadoop is able to utilize its resources for very deep data analysis. This data analysis could be applied to any industry, to minimize risk and detect fraud. The product can be used with SQL through SQL-on-Hadoop, though it has no inherent database foundation. This lack of a database foundation does mean that the product is slower than traditional data warehousing.
The Disadvantages of Apache’s Hadoop
For many, open source solutions have served as a double-edged sword. While they are more extensible and easily customized, they also come with an inherent lack of support and often have to be modified from their out-of-the-box state. Hadoop is no different; there is no single resource for Hadoop support, instead there are specialists within the solution.
Hadoop is not designed for general reporting and performance management. Instead, Hadoop is mostly designed for complicated analytic services. For some comipanies, these analytic services may be enough. But for others, performance management and reporting must be key. This is why Hadoop is often seen as an addition to a standard data warehousing solution rather than a replacement.
Through the use of big data sets, Hadoop can be used for risk analytics and general data mining. But it may not be usable for strict data warehousing, especially for larger sets of data that must be analyzed on a more general level. Though the temptation may be there, data warehouses and Hadoop simply serve different purposes altogether. Hadoop is designed for the broad analysis of large data sets, whereas data warehouses are designed for the fast delivery and optimization of these data sets.
For the very unique case scenarios in which Apache’s Hadoop can replace a warehousing solution, it is likely to be the optimal choice. Hadoop allows for quick scaling and fault tolerance, in addition to the ability to run a complex distributed processing and storage system on less than optimal hardware technology. Thus, if Hadoop can replace your data warehousing solution, it may be a good idea, but that really only implies that you didn’t need a data warehousing solution to begin with. For most use cases, Apache’s Hadoop will instead work with a data warehousing solution for optimal performance and in-depth analysis.