Spark is an excellent language for developing machine learning pipelines, conducting exploratory data analysis at scale, and producing ETLs for data platforms. Spark is essential for anybody working with large amounts of data. massive-scale data-intensive operations may be managed and business insights can be obtained by processing massive volumes of data in a distributed manner using PySpark, a Python API for Spark, without compromising developer efficiency. To put it succinctly, PySpark is a strong and quick framework for doing massively distributed processing over robust data sets.

The data for this case study is publicly available on Yahoo Finance, and it includes information about a company’s daily stock values from 2010 through 2020.

1. Load the data into Apache Spark as a DataFrame
2. Analyze the data by computing various statistics such as mean, standard deviation, and correlation
3. Visualize the data by plotting the daily closing prices over the years. Stock Price Analysis

Spark which is one of the most used tools when it comes to working with Big Data, but whereas Spark used to be heavily reliant on RDD manipulations, Spark has now provided a DataFrame API for us to work with. So in this notebook, We will learn standard Spark functionalities needed to work with DataFrames, and finally some tips to handle the inevitable errors you will face. COVID-19 Dataset Analysis

This notebook covers a classification problem in Machine Learning and go through a comprehensive guide to succesfully develop an End-to-End ML class prediction model using PySpark. Tabular Data Classification