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Real-time analytics is the use of all available enterprise data and resources, when they are needed. It consists of dynamic analysis and reporting, based on the data entered into a system, it takes less than one minute before the actual time of use. Real-time analytics is also known as real-time data analytics, real-time data integration, and real-time intelligence.
The need for real-time analytics has been growing with time. It’s importance in various domains has proved that the application brings quicker solutions. Whether it is banking, retail or telecommunication, real-time analytics has its way around.
In banking, we hear and experience various types of frauds. Fraud transactions, are one of them occurring on a daily basis. For example, the credit card may have had transactions, twice in two different parts of the country. Real-time analytics enables to detect the location and longitude. If the locations of both the transactions do not match, then there is definitely a grave issue.
Another simple example is the social networking sites. Twitter users would be aware of the trending topics in the twitter page. Here, real time analytics comes in the picture, since it thrives on the user data. Based on a user’s tweets, they source the most trending and talked about topics, and post it on the page about what’s trending. This immediately drives revenue and traffic. Storm plays a role here too.
Brands like twitter, flipboard, OOYALA, Loggly, wego have been the adopters of storm extensively for trending topics, custom magazine feeds, real-time video analytics, and compare and display real-time prices.
To throw some light on Apache Storm, it could be defined as a free and open-source distributed real-time computation system. It is simple and can be used with any programming language.
A storm cluster has 3 sets of nodes- The master here is the Nimbus, which runs in the node or machine. It is responsible for submitting jobs to the cluster. Zookeeper is a distributed code initiation service, it has to be installed with storm separately. It has the responsibility to keep it in the running stage. Nimbus submits it, but zookeeper runs it, if there is a failure the supervisor takes care of it.
· Uploads computation for execution
· Distributes codes across the cluster
· Launches workers across the cluster
· Monitors computation and relocates workers as needed.
· Coordinates the storm cluster
· Communicates with nimbus through zookeeper, starts and stops workers according to signals from Nimbus.
Storm is considered ideal for real-time processing as it is fast in processing 1 million , 100 byte messages per second per node. It is scalable with parallel calculations that run across a cluster of machines. Storm guarantees that each unit of data will be processed at least once. Messages are replayed when there are failures. It has standard configurations that are suitable for production on day one. Once deployed, it is easy to operate.
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