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In Dezyre's Hadoop hands-on training course, we perform two different projects that require us to stream data from twitter in real time. Most of these hadoop projects are a production scenario which will then involve analyzing the project in a batch mode and representing to end users.
But what if the decision that needs the streamed data is time sensitive? This means that we must stream that data and analyze it in motion. After analysis, the result must be presented as the streaming is taking place.
An example of a use of such system is to analyze public response to any event in real time like a political speech, a sports game, an economic news and much more. People with the access to quality real-time data can then position themselves for profit in such circumstance.
In this Hackerday, we will go through the basis of statistics and see how Spark enables us to perform statistical operations like descriptive and inferential statistics over the very large dataset.
In this spark streaming project, we are going to build the backend of a IT job ad website by streaming data from twitter for analysis in spark.
In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.