on 17 May 18
To get a grasp of today’s database management, it is essential to understand stream analytics. The database management today is far more advanced in terms of speed and flexibility than in the early years. It would not be wrong to say that stream analytics has become the backbone of any enterprise that wants to take decisions in real time and provide better services to its clients. Stream analytics thus provide a framework which is not only convenient to data scientists but also fault-tolerant and efficient as it can combine different data streams into a single entity.
However, to understand stream analytics one must be aware of the stream processing which analyzes and acts on the real-time data by accepting queries on the continuous basis. Streaming analytics communicates to external data sources and thereby enable applications to integrate data in the application flow. It can also update an external database with the new information in the real-time. Thus stream processing is nothing but complex event processing or CEP in short. CEP and streaming analytics technologies thus enable the user to take action on the basis of analysis of events that have just taken place.
The basis of stream processing is indeed streaming analytics which provides it with the functionality of constantly calculating various statistical parameters while moving in the stream of data. The scope and utility of streaming analytics is thus limitless as it not only provides for management and monitoring of the data even before it actually enters the database. It thus empowers data scientists to effect changes in the running data. They can spot and eliminate spurious data and wrongfully inserted entries. This can prevent fraudsters and hackers from corrupting the data in the database.
Stream analytics is best experienced with Spark streaming which is a free framework from Apache. In fact, Spark Streaming is an extension of the core Spark API that is scalable, fast and fault-tolerant stream processing of live data streams. It has revolutionized the data management and it is going to be the technology that would have sway in time to come.
Streaming Analytics taps into streams of data and continuously aggregates that data, and merges it in real time with other parameters.
Let us understand stream analytics with a real life example of GPS-enabled vehicles that are being tracked in the real-time. Stream analytics works well in the given paradigm. It can manage GPS data from several vehicles in real-time and merge them so that a person sitting in a police control room or in a company running taxi network can take immediate decisions based on the data analyzed by the streaming analytics. Stream analytics can continuously collect the GPS data from a vehicle and merge it with the actual location of the policemen or the customers.
It can thus track each and every move the driver makes in real time and also calculate which vehicle is closest to a particular spot based on the user preferences. The speed at which it is being done is tremendous and so tracking is instantaneous. The decisions from the user can be taken and transmitted in real-time. This is a great achievement for data management as the time lapse is zero and in real life situations such as medical emergency services or police actions, it can make a world of difference.
Streaming analytics thus involves two parts knowing what is happening and then acting upon what is happening. Since Streaming analytics provides data immediately the onus then falls on the users, the companies that handle the decision making. If the decision making is slow then, unfortunately, there is nothing stream analytics can do about it and the advantage gained through the use of stream analytics is lost. And that is perhaps the only bad thing that can happen in stream analytics.
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