![]() The Python packaging for Spark is not intended to replace all of the other use cases. Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). This README file only contains basic information related to pip installed PySpark. ![]() Guide, on the project web page Python Packaging You can find the latest Spark documentation, including a programming Pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing,Īnd Structured Streaming for stream processing. Rich set of higher-level tools including Spark SQL for SQL and DataFrames, Supports general computation graphs for data analysis. High-level APIs in Scala, Java, Python, and R, and an optimized engine that NET Framework 4.6.1 Readme File.Spark is a unified analytics engine for large-scale data processing. NET Framework 4.6.įor important information about this release, see the. NET Framework 3.5 SP1 and earlier versions, but performs an in-place update for the. NET Framework runs side-by-side with the. ![]()
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