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Monitoring Real-Time Uber Data Using Spark Machine Learning, Streaming, and the Kafka API (Part 1) | MapR

According to Gartner, by 2020, a quarter of a billion connected cars will form a major element of the Internet of Things. Connected vehicles are projected to generate 25GB of data per hour, which can be analyzed to provide real-time monitoring and apps, and will lead to new concepts of mobility and vehicle usage. One of the 10 major areas in which big data is currently being used to excellent advantage is in improving cities. For example, the analysis of GPS car data can allow cities to optimize traffic flows based on real-time traffic information.

Uber is using big data to perfect its processes, from calculating Uber’s pricing, to finding the optimal positioning of cars to maximize profits. In this series of blog posts, we are going to use public Uber trip data to discuss building a real-time example for analysis and monitoring of car GPS data. There are typically two phases in machine learning with real-time data:

  • Data Discovery: The first phase involves analysis on historical data to build the machine learning model.
  • Analytics Using the Model: The second phase uses the model in production on live events. (Note that Spark does provide some streaming machine learning algorithms, but you still often need to do an analysis of historical data.)

building the model

Source: Monitoring Real-Time Uber Data Using Spark Machine Learning, Streaming, and the Kafka API (Part 1) | MapR

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