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OneNet—a distributed functional programming platform from Microsoft
OneNet (now called Prajna) is a distributed functional programming platform being developed at Microsoft. As such, it has a lot of similarities to Apache Spark.
Both platforms are built using functional languages—F#, in the case of OneNet, and Scala in Spark—which are also the primary languages for developers using the platforms.
OneNet will have support for specializied computing devices, such as GPUs, from the outset which should provide more options when performing computationally expensive analyses.
Happy 5th birthday, Spark
The Apache Spark project was first open-sourced on 31 March 2010.
While much has been made of how quickly interest in Spark has grown, it’s worth pausing to remember that it’s been around for a whole five years. The project has had time to mature and expand into areas where there are the practical requirements (e.g. data frames, ML pipelines).
Looking forward to what the team comes up with in the next five years.
Birth of a Theorem
I’ve been listening to extracts from “Birth of a Theorem: A Mathematical Adventure” on BBC Radio 4’s Book of the Week. It’s Cédric Villani’s account of the years leading up to his award of the Fields Medal—the most coveted prize in mathematics.
We rarely get a chance to see the creative process at work. All we get to see is the final result—wrapped up in a neat little bow. We don’t see the blind alleys that were navigated to get there. Villani takes us on a journey through his unproductive lows, his breakthroughs and everything in between.
It’s the same with business decisions. We see the rationalised, justified final decision. Not the messy process of negotiation and estimation that resulted in the decision. If we’re to get better at making decisions, understanding the process is key.
Big data and narrative
As the amount of data we collect continues to explode, attention needs to shift to making sense of it. Tools like Hadoop and Spark allow us to analyse these huge datasets, but they don’t really make sense of it. Senior managers want insights. They want to have their business illuminated by the data. At present, this is done by data scientists analyzing the data and weaving it into a report.
However, this isn’t scalable. Data scientists become the bottleneck. It’s hard to get real-time insights when someone has to plough though the data and then write it up.
Unsurprisingly, there are people working on this problem. Narrative Science have a tool called Quill that they call an “automated narrative generation platform”.
If we are going to start harnessing the power of big data as part of our day-to-day business, tools of this kind will be essential. I presume there is still a way to go before they get even close to the insights a data scientist can provide, but we need to start somewhere.