preprocessing millions of records - how to speed up the processing

This page summarizes the projects mentioned and recommended in the original post on /r/datascience
Python Machine Learning Concurrency and Parallelism Dataframe Ray

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  • vaex

    Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀

    • Try vaex, vaex, using lazy evaluation and parallel calculations, you should be fine.

  • Ray

    Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

    • Dask, Ray(ray.io), or pyspark(if you have a cluster)

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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