Maki Nage is a framework designed to work on streaming data. A Maki Nage application takes a stream of events as input, applies some transformations on these events, and returns another stream of events:

import rx
import rxsci as rs

source = [1, 2, 3, 4, 5, 6, 7]

      rs.data.roll(window=3, stride=3, pipeline=rx.pipe(

Maki Nage leverages two other projects as a foundation:

The structure of Maki Nage is the following one:


All transformation functions in Maki Nage are ReactiveX operators. RxSCi is a ReactiveX extension library, dedicated to data manipulation. The combination of ReactiveX and RxSci makes it very easy to deal with streams of events.

Kafka is used for multi-core and multi-machine scalability. Thanks to its usage, it is easy to start working on a relatively small development machine, then scale on a bigger machine or a small cluster, and finally scale on a cloud platform. All these steps with the same code base.

The key advantages of Maki Nage are:

  • Ease of use, thanks to declarative, extensible python APIs.

  • Code reuse from development up to deployment.

  • Scalability.

  • A unified paradigm for streaming and batch processing.