Working with small time-series data is fun. You can easily load daily Microsoft stock prices into memory and find the most successful year it its history. Or you can download average daily temperatures in your city over the last 10 years and try to spot a trend in a chart. But what if you have prices at millisecond frequency for thousands of stocks or high-resolution temperatures for the entire globe? With the right tools, working with massive time-series can feel the same as crunching through hundreds of observations in memory.
In this talk, I will show what's available if you are using R, the .NET platform and Azure. We'll use Deedle, a scalable .NET data analytics library, R type provider that makes thousands of R packages available to .NET developers and MBrace, a cloud computing framework that can easily scale your data analytics over an Azure compute cluster.