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DASK and Apache Spark

DASK and Apache Spark

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For a Python driven Data Science team, DASK presents a very obvious logical next step for distributed analysis. However, today the de-facto standard choice for exact same purpose is Apache Spark. DASK is a pure Python framework, which does more of same i.e. it allows one to run the same Pandas or NumPy code either locally or on a cluster. Whereas, Apache Spark brings about a learning curve involving a new API and execution model although with a Python wrapper. Given the above statement, do we even need to compare and contrast to make a choice? Shouldn’t DASK be the default choice? Well, that’s what this session is about. It goes in detail explaining the various viewpoints and dimensions that need to be considered to pick one over other.
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