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Apache Spark with Python: Why use PySpark?

Predictions concerning weather, house expenses, and gold costs have in large part been accurate in past years due to a scarcity of right facts. However, today, with rampant digitization clouding every sphere of human lifestyles, the tale is special. Your Facebook feeds, smartwatches, Instagram stories, Tesla automobiles, and all other gadgets connected to the network are a source of statistics for engineers and scientists. Nonetheless, storing and processing these statistics to help us make sense of where in the sector goes as a whole is a special ballgame altogether. If you’re a developer, you will have probably grimaced and chuckled at the sheer scale of this activity.

What is Apache Spark?

Developed at the AMPLab at the University of California, Berkeley, Spark donated to the Apache Foundation as an open-source distributed cluster computing framework. In 2014, after Spark’s first release, it received recognition amongst Windows, Mac OS, and Linux users. Written in Scala, Apache Spark is one of the most popular computation engines that process huge batches of data in sets, and in a parallel style these days. Apache Spark Implementation with Java, Scala, R, SQL, and our all-time favorite: Python!

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