If you are an internet buff probability is that you have already heard about big data or for that matter real time big data. In the last few days, no information technology has been so radical in its approach as real time big data processing. Earlier when data was limited in scope and size things were different and companies took their own time to collect them and then analyze them. The process could take day or weeks before any action could be taken based on the data analysis. But no more now with the explosion of information, thanks to internet and internet of things. Today the business environment has completely changed. The companies now need actionable data as fast as they can get.

It is no more enough to just collect data rapidly but the technology should also provide the entrepreneur to take action based on the real time big data analysis; for instance the twitter handles are analyzed for assessing the public reaction to launch a product or Facebook posts are analyzed to assess the people’s reaction to a government policy. All this becomes stale very fast. To be proactive the companies need this information in real time so that they can adjust to the consumer feedback and reaction. Apart from this, it is also necessary for trading, fraud detection and management, system monitoring, and many other verticals we deal with in digital economy we are moving into.

Big data is one of the most used buzzwords at the moment in our industry. You can best define it by thinking of four Vs: Big data is not just about Volume, but also about Velocity, Variety, and Veracity. Volume is defined as a lot of data, Velocity is streams of data, Variety is speech, image, text and video and Veracity is the accuracy of data. These can be used to gather real time knowledge needed to run a business in the real world.

Big data architecture contains several parts. Often, masses of structured and semi-structured historical data are stored in Hadoop. On the other side, stream processing is used for fast data requirements taking care of velocity, variety, and veracity. Both areas complement each other very well.

Apache streaming processing is the ideal platform to process data streams or sensor data which is usually a high ratio of event throughput versus numbers of queries, whereas complex event processing utilizes event-by-event processing and aggregation; for example on potentially out-of-order events from a variety of sources – often with large numbers of rules or business logic. CEP engines are optimized to process discreet business events for example, to compare out-of-order or out-of-stream events, applying decisions and reactions to event patterns, and so on. For this reason, multiple types of event processing have evolved, described as queries, rules and procedural approaches to event pattern detection.

Stream processing is also designed to analyze and act on real-time apache streaming data, using continuous queries like SQL-type queries that operate over time and buffer windows. Essential to stream processing is Apache streaming Analytics, or the ability to continuously calculate mathematical or statistical analytics on the fly within the stream. Stream processing solutions are designed to handle high volume in real time with a scalable, highly available and fault tolerant and distributed architecture. This enables analysis of data in motion which is far more important than analyzing data in rest. Data in rest loses its value rapidly over time, thus making it not as effective as data in motion which requires real time processing.

In contrast to the traditional database model where data is first stored and indexed and then subsequently processed by queries, stream processing takes the inbound data while it is in flight, as it streams through the server. Stream processing also connects to external data sources, enabling applications to incorporate selected data into the application flow or to update an external database with processed information.

Stream processing found its first uses in the finance industry, as stock exchanges moved from floor-based trading to electronic trading. Today, it makes sense in almost every industry - anywhere where you generate stream data through human activities, machine data or sensory data. As the Internet of Everything (IOE) increase in volume, variety, velocity and veracity of data, leading to a dramatic increase in the applications for stream processing technologies. Some use cases where stream processing can solve business problems include Network monitoring and Risk management, fraud detection and management including many other areas of interest in business companies worldwide.