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 Pandas dataframes to provide sensible streaming operations on continuous tabular data.
To learn more about how to use streams, visit :doc:`Core documentation <core>`.
Continuous data streams arise in many applications like the following:
- Log processing from web servers
- Scientific instrument data like telemetry or image processing pipelines
- Financial time series
- Machine learning pipelines for real-time and on-line learning
Sometimes these pipelines are very simple, with a linear sequence of processing steps:
And sometimes these pipelines are more complex, involving branching, look-back periods, feedback into earlier stages, and more.
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
x1, x2 = itertools.tee(x, 2) y1 = map(f, x1) y2 = map(g, x2)
However this quickly become cumbersome, especially when building complex pipelines.
.. toctree:: :maxdepth: 2 :hidden: :caption: Contents core.rst dataframes.rst dask.rst collections.rst api.rst collections-api.rst dataframe-aggregations.rst async.rst