The paradigm shift of data from static to fast flowing data is an important move in the industry, to accommodate growing size of data. The velocity and volume of data are continuing to expand which has started to make its impact in business and other applications of Big Data. The paper describes the paradigm shift of data from static data to streaming data for data analytics beyond Hadoop. It describes how the first generation of Hadoop applications were largely built for batch-oriented paradigm . Streaming data is essentially different from traditional data handling patterns and comes with its own set of challenges and requirements. New applications such as Storm, Flume, Kafka, and other technologies are evolving to bring in an era of real-time analytics Data is generated incessantly from thousands of sources simultaneously and it can be of various type such as log files, mobile and web data, transaction etc. The sections of my paper are Introduction followed by Streaming data, Hadoop, Streaming data analytics, Apache Spark, Comparing Streaming models and Streaming analytics use cases followed by conclusion. The information presented is from secondary source i.e. journal, conference proceedings and websites.