I will show the Blockchain analysis in Jupyter interactive notebook using the external Spark cluster running in Kubernetes, everything dockerized.
The talk will briefly describe how Blockchain transactions work, but most of the time would be the demo. In the demo I will show how we can run various queries on the publicly available Blockchain data, graph algorithms such as PageRank for identifying significant BTC addresses and more.
Intended audience: intermediate, analysts, Bitcoin/Altcoin enthusiasts
In this presentation I showed a simple way of leveraging Spark's GraphX and GraphFrames for analyzing the transaction graph of Bitcoin transactions. Real data was used.
Have you ever wondered how to implement your own operator pattern for you service X in Kubernetes? You can learn this in this session and see an example of open-source project that does spawn Apache Spark clusters on Kubernetes and OpenShift following the pattern. You will leave this talk with a better understanding of how spark-on-k8s native scheduling mechanism can be leveraged and how you can wrap your own service into operator pattern not only in Go lang but also in Java. Let's make the data science more scalable in a cloud native fashion.
In this presentation I showed a simple way of leveraging Spark's GraphX and GraphFrames for analyzing the transaction graph of Bitcoin transactions. Real data was used.