PySpark for Data Science – Intermediate

This module on PySpark Tutorials aims to explain the intermediate concepts such as those like the use of Spark session in case of later versions and the use of Spark Config and Spark Context in case of earlier versions. This will also help you in understanding how the Spark–related environment is set up, concepts of Broadcasting and accumulator, other optimization techniques include those like parallelism, tungsten, and catalyst optimizer. You will also be taught about the various compression techniques such as Snappy and Zlib. We will also understand and talk about the various Big data ecosystem related concepts such as HDFS and block storage, various components of Spark such as Spark Core, Mila, GraphX, R, Streaming, SQL, etc. and will also study the basics of Python language which is related and relevant to be used along with Apache Spark thereby making it Pyspark. We will learn the following in this course:

Regression

Linear Regression

Output Column

Test Data

Prediction

Generalized Linear Regression

Forest Regression

Classification

Binomial Logistic Regression

Multinomial Logistic Regression

Decision Tree

Random Forest

Clustering

K–Means Model

Pyspark is a big data solution that is applicable for real–time streaming using Python programming language and provides a better and efficient way to do all kinds of calculations and computations. It is also probably the best solution in the market as it is interoperable i.e. Pyspark can easily be managed along with other technologies and other components of the entire pipeline. The earlier big data and Hadoop techniques included batch time processing techniques.