Streamz helps you build pipelines to manage continuous streams of data. It is simple to use in simple cases, but also supports complex pipelines that involve branching, joining, flow control, feedback, back pressure, and so on.

Optionally, Streamz can also work with both Pandas and cuDF dataframes, to provide sensible streaming operations on continuous tabular data.

To learn more about how to use streams, visit Core documentation.


Continuous data streams arise in many applications like the following:

  1. Log processing from web servers

  2. Scientific instrument data like telemetry or image processing pipelines

  3. Financial time series

  4. Machine learning pipelines for real-time and on-line learning

Sometimes these pipelines are very simple, with a linear sequence of processing steps:

a simple streamz pipeline

And sometimes these pipelines are more complex, involving branching, look-back periods, feedback into earlier stages, and more.

a more complex streamz pipeline

Streamz endeavors to be simple in simple cases, while also being powerful enough to let you define custom and powerful pipelines for your application.

Why not Python generator expressions?

Python users often manage continuous sequences of data with iterators or generator expressions.

def fib():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

sequence = (f(n) for n in fib())

However iterators become challenging when you want to fork them or control the flow of data. Typically people rely on tools like itertools.tee, and zip.

x1, x2 = itertools.tee(x, 2)
y1 = map(f, x1)
y2 = map(g, x2)

However this quickly become cumbersome, especially when building complex pipelines.


To install either use:

  • conda-forge: conda install streamz -c conda-forge

  • pip: pip install streamz

  • dev: git clone followed by pip install -e streamz/


The streamz project offers a Docker image for the convenience of quickly trying out streamz and its features. The purpose of the Dockerfile at this time is not to be used in a production environment but rather for experimentation, learning, or new feature development.

Its most common use would be to interact with the streamz example jupyter notebooks. Lets walk through the steps needed for this.

  • Build the Docker container

    $ docker/
  • Run the Docker container

    $ docker/
  • Interact with Jupyter Lab on the container in your browser at http://localhost:8888/.