Today, many organizations are facing the Big Data problem. Managing and processing Big Data can incur a lot of challenges for traditional data processing platforms such as relational database systems. Hadoop was designed to be a distributed and scalable system for dealing with Big Data problems.
The design, implementation, and deployment of a Big Data platform require a clear definition of the Big Data problem by system architects and administrators. A Hadoop-based Big Data platform uses Hadoop as the data storage and processing engine. It deals with the problem by transforming the Big Data input into the expected output. On one hand, the Big Data problem determines how the Big Data platform should be designed, for example, which modules or subsystems should be integrated into the platform and so on. On the other hand, the architectural design of the platform can determine complexity and efficiency of the platform.
Different Big Data problems have different properties. A Hadoop-based Big Data platform is capable of dealing with most of the Big Data problems, but might not be good fit for others. Because of these and many other reasons, we need to choose from Hadoop alternatives.