Updated on Nov, 2023
Language - English
Duration -2 hours
Pyspark is an Apache Spark and Python partnership for Big Data computations. Apache Spark is an open-source cluster-computing framework for large-scale data processing written in Scala and built at UC Berkeley's AMP Lab, while Python is a high-level programming language. the park was originally written in Scala, and its Framework PySpark was later ported to Python through Py4J due to industry adaptation. It is a Java library built into PySpark that helps Python interact with JVM objects dynamically; therefore, to run PySpark, you must also have Java enabled in addition to Python and Apache Spark.
Beginning steps for PySpark
- Connecting to a cluster is the first step in Spark (a group of nodes at a remote location where the master node splits the data among the worker nodes, and all the worker nodes report the results of the computations on data to the master node). It is as easy as building an object/instance of the class Spark Context to bind to the cluster.
- You may use the SparkContext class to generate a SparkSession object that acts as an intercept with the cluster relation. Creating several SparkSessions will lead to problems.
- pyspark.sql — module from which the SparkSession object can be imported.
- SparkSession.builder.getOrCreate() — function restores a current SparkSession if one exists, or produces a new one if one does not exist.
Check out the detailed breakdown of what’s inside the course
- Introduction to PySpark 09:10 09:10
Basics of Pyspark and Python
Programming With RDDS
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