It is twofold.

First, I want to advocate for the general practical software design principles on which our solution rests - for example, the importance of looking for a perfect, "holy grail" type solution, rather than settling for solutions addressing individual common cases; the importance of optimizing the worst-case behavior; the challenges of optimizing dynamic system behavior in an unpredictable environment.

Second, I would like to emphasize how important is the problem of stragglers and how enormous is the impact of having a good solution for it. I really would like to see authors of other data processing systems realize this and address the problem in their systems as well.

To get an idea of what I plan to discuss, a good place to start is a blog post we wrote. It’s called No Shard Left Behind. The basic idea sounds really simple in principle. We repartition data as it’s processed (Dynamic Work Rebalancing). It’s really that simple. This talk dives into why it’s also challenging and worth the effort. It’s also important to note that the talk also explores other approaches that just don’t work in all use cases.

I hope they will walk away with an inspiration to keep looking for "holy grail"-type solutions to architectural problems they face, and an appreciation for systems that implement such solutions, such as Cloud Dataflow.