Big data is often created by data that is generated at incredible speeds, such as click-stream data, financial ticker data, log aggregation, or sensor data. Often these events occur thousands to tens of thousands of times per second. No wonder this type of data is commonly referred to as a fire-hose.
Fast Data is real-time big data not yet stored. Fast Data processing sits in front of the big data fire hose, sifting through the massive amounts of incoming information to identify actionable business opportunities or threats. While this data is in motion, it offers opportunities to respond immediately based on insights derived from deep, real-time analytics.
The benefits of big data are lost if fresh, fast-moving data from the fire-hose is dumped into HDFS, an analytic RDBMS, or even flat files, because the ability to act or alert right now, as things are happening, is lost. The fire hose represents active data, immediate status, or data with ongoing purpose. The data warehouse, by contrast, is a way of looking though historical data to understand the past and predict the future.