Deep Learning and Big Data Analytics are the major focus in current rapidly growing technical environment. The use of large data has become crucial to different organizations as they are collecting huge amount of domain specific data, which contains critical information about cyber security, theft detection, national resources, business economics, marketing and medical information. The assessment of this huge amount of data needs advanced analytical techniques for surveying and predicting future course of action by creating advanced decision-making strategies. Deep learning algorithms utilize the collected training data, to create a representation model. This model uses the computer for predictions or decision making about new data without the need to train the machine explicitly to perform user task. These techniques and algorithms infer high-level complex abstractions as the data are represented through hierarchical process. A major benefit of deep learning is processing and learning from the huge amounts of unsupervised data, analyzing patterns from the data which can be used for big data analytics in which the raw data is largely unlabeled and not categorized. In this paper, deep learning techniques for addressing data of various types or formats is analyzed, enabling fast and full processing of data by integrating large amounts of different information i.e. data transformation is also addressed. It also deals with the quality of data as machine performance improves with the quality of data. Further exploration on the deep learning techniques to assist big data is done by focusing on two key topics on how Deep Learning assist some of the specific problems like Data Variety and Data Quality in Big Data Analytics, and how these techniques can aid in processing the Big Data.