Talk I gave at a Spark Meetup on 01/16/2014
One of the most difficult aspects of deploying spark streaming as part of your technology stack is maintaining all the job associated with stream processing jobs. In this talk I will discuss the the tools and techniques that Sharethrough has found most useful for maintaining a large number of spark streaming jobs. We will look in detail at the way Monoids and Twitter's Algebrid library can be used to create generic aggregations. As well as the way we can create generic interfaces for writing the results of streaming jobs to multiple data stores. Finally we will look at the way dependency injection can be used to tie all the pieces together, enabling raping development of new streaming jobs.