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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.
The goal of this apache kafka project is to process log entries from applications in real-time using Kafka for the streaming architecture in a microservice sense.
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.
Spark Project - Discuss real-time monitoring of taxis in a city. The real-time data streaming will be simulated using Flume. The ingestion will be done using Spark Streaming.