A primary outcome of Bigdata is to derive useful and actionable insights from large or challenges data collections. The goal is to run the transformations from data, to information, to knowledge, and finally to insights. This includes calculating simple analytics like Mean, Max, and Median, to derive overall understanding about data by building models, and finally to derive predictions from data. Some cases we can afford to wait to collect and processes them, while in other cases we need to know the outputs right away. MapReduce has been the defacto standard for data processing, and we will start our discussion from there. However, that is only one side of the problem. There are other technologies like Apache Spark and Apache Drill graining ground, and also realtime processing technologies like Stream Processing and Complex Event Processing. Finally there are lot of work on porting decision technologies like Machine learning into big data landscape. This talk discusses big data processing in general and look at each of those different technologies comparing and contrasting them.