Deep Learning is the trending area of research in Machine Learning and Pattern Recognition. Deep Learning focuses on Machine Learning tools and techniques, and applies them in resolving complications which lacks human or artificial thoughts. Deep Learning is achieved by learning over a cascade of many layers. Deep Learning handles many real world complications, such as Machine translation, Object recognition and Localization, Speech recognition, Image caption generation, Distributed representation for text, Natural Language Processing, Image Classification, etc., with its datadriven representation learning. The traditional computing is facing challenges in dealing with high-dimensional and streaming data, semantic indexing, and scalability of models. The analysis of streaming and fast-moving input data is constructive in tracking tasks such as fraud detection. In view of the above challenges, extensive advanced research work is essential for adaptation of Deep Learning algorithms to deal with issues associated with Big Data. Handling streaming data is essentially adapted by Deep Learning algorithms so as to deal with the increasing huge amounts of uninterrupted input data. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, which makes it a worthy tool for Big Data Analytics, where the input data is mostly unlabeled and un-categorized. This paper provides a detailed survey on semantic indexing techniques which will enable fast search and information retrieval.