Keyword search in relational databases provides a simple and interactive query interface for retrieving data from databases. In recent year's keyword search over structured or unstructured data received significant attention. KWS performs on enterprise applications based on various forms which can take some values, these values might be express verifiable, for example user identification. A large portion of this assortment of effort ensures a critical confinement in the connection of enterprise information meanwhile it neglects the application code that has regularly been precisely intended to present information in a significant manner to employers. There is a lot of work done in this domain and many solutions have been developed, but there are still significant limitations in perspective of enterprise data and also performance issues are still not maintainable when updates occur. Furthermore the existing work done around there depended on making keyword lists. When these indices require modifications, the maintenance issues are raised. The most recent work in this area is in light of transformed SQL inquiries from the SQL queries in the structure. We studied existing keyword search techniques and compare their strength and weaknesses. After that we discussed the flaws and imperfections in the existing work and made some suggestions for improvement in future development. In contrast, we propose techniques based on a hybrid approach. Retrieval of information from big data can be made faster by using KWS and Agrios, minimizes information development between the two segments of the hybrid, utilizing systems repurposed from social database inquiry streamlining. Therefore, information retrieval can be enhanced by our proposed technique.