![]() ![]() ![]() These can all be for different purposes to be used. The core of Spark already Provides common ways to import the data and to transform and evaluate or to analyze.īut that's not all: Spark therefore comes with the Following built-in libraries: SQL and Data frames, Spark streaming, MLlib and GraphX. Neither must the data be in a specific format, yet thesis must, by necessity in a Certain Way to be processed to Spark to be able to use. Since we already have the oneness of Spark towards it, not for a specific purpose, but gene rally for almost developed data processing. But there are far more reasons why Spark for us to be relevant and therefore interesting.Īs a manufacturer-independent company we are moving in a variety of industries, are not one or only a few use cases is limited, but work across all sectors to applications and applications. That alone would be enough, so we as a company in our field in "Search and Big Data" So with this project apart. Apache Spark is currently using the Apache top level project in the Big Data environment is, the most active is being developed. ![]()
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