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Data Manipulation Pandas-PySpark Conversion Guide
Everything you need to do Exploratory Data Analysis(EDA) with PySpark on Databricks
When working with big data, Jupyter notebook will fail. This is one reason why some companies are using ML platforms such as Databricks, SageMaker, Alteryx, etc. A good ML platform supports the entire machine learning lifecycle from data ingestion to modeling and monitoring, which increases the team’s productivity and efficiency. In this simple tutorial, I’ll share my notes on converting Pandas script to Pyspark, so that you can seamlessly convert these two languages as well!
Introduction
What is Spark?
Spark is an open-source cloud computing framework. It’s a scalable, massively parallel, and in-memory execution environment for running analytics applications. Spark is a fast and powerful engine for processing Hadoop data. It runs in Hadoop clusters through Hadoop YARN or Spark’s standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both general data processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. You can read more about Mapreduce here…
