From the post:
KillrWeather is a reference application (which we are constantly improving) showing how to easily leverage and integrate Apache Spark, Apache Cassandra, and Apache Kafka for fast, streaming computations in asynchronous Akka event-driven environments. This application focuses on the use case of time series data.
The site doesn’t give enough emphasis to the importance of time series data. Yes, weather is an easy example of time series data, but consider another incomplete listing of the uses of time series data:
A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.
Mastering KillrWeather will put you on the road to many other uses of time series data.
I first saw this in a tweet by Chandra Gundlapalli.