With the availability of fast growing and cheap storage technology, corporate organizations are storing fifty percent more data each year. This has increased the corporate data storage tremendously. To manage and analyse this big data in order to extract useful information for meeting the requirements of corporate business challenges is now the most crucial problem for organizations. Thus, researchers and data analysts are facing a big open research question how to find the computationally efficient and intelligent approaches for discovering the useful patterns, finding the characteristics of inherent pattern, identifying incoming patterns by using the knowledge of big data, and discovering the associations between extremely large numbers of variables. These are also essential not only for big data analytics but also for big data security since detection of security vulnerability of the data communication, storage and usage of big data also depends on the existing computationally intelligent data mining techniques. This paper discusses about the current capabilities of existing data mining techniques for big data challenges, their performance in terms of space and computational complexities. Then the paper also suggests some possible approaches of integration and optimization of existing techniques. Finally, it also discusses how those can be implemented by using current distributed computing facilities.