Architecting Kafka Streams

Bill Bejeck walks through a scenario in which one might use Kafka Streams:

Now, you’ve defined your source and we can start creating processors that’ll do the work on the data. The first goal is to mask the credit card numbers recorded in the incoming purchase records. The first processor is used to convert credit card numbers from 1234-5678-9123-2233 to xxxx-xxxx-xxxx-2233. The Stream.mapValues method performs the masking. The KStream.mapValues method returns a new KStream instance that changes the values, as specified by the given ValueMapper, as records flow through the stream. This particular KStream instance is the parent processor for any other processors you define. Our new parent processor provides the masked credit card numbers to any downstream processors with Purchase objects.

Unfortunately, this article seems like a mixture of high-level and low-level information that appeals more to people who already know how Kafka Streams works, but it is nevertheless interesting.

Related Posts

Neural Nets On Spark

Nisha Muktewar and Seth Hendrickson show how to use Deeplearning4j to build deep learning models on Hadoop and Spark: Modern convolutional networks can have several hundred million parameters. One of the top-performing neural networks in the Large Scale Visual Recognition Challenge (also known as “ImageNet”), has 140 million parameters to train! These networks not only […]

Read More

Running H2O In R On Azure HDInsight

Daisy Deng shows how to configure HDInsight to be able to run the H2O package in R rather than Python or Scala: We provide a few script actions for installing rsparkling on Azure HDInsight. When creating the HDInsight cluster, you can run the following script action for header node: And run the following action […]

Read More


April 2017
« Mar May »