Big Data Fundamentals with PySpark

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Course Description

There's been a lot of buzz about Big Data over the past few years, and it's finally become mainstream for many companies. But what is this Big Data? This course covers the fundamentals of Big Data via PySpark. Spark is “lightning fast cluster computing" framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk, than Hadoop. You’ll use PySpark, a Python package for spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc., to interact with works of William Shakespeare, analyze Fifa football 2018 data and perform clustering of genomic datasets. At the end of this course, you will gain an in-depth understanding of PySpark and it’s application to general Big Data analysis.

This chapter introduces the exciting world of Big Data, as well as the various concepts and different frameworks for processing Big Data. You will understand why Apache Spark is considered the best framework for BigData.

In this chapter, you'll learn about Spark SQL which is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as a distributed SQL query engine. This chapter shows how Spark SQL allows you to use DataFrames in Python.

The main abstraction Spark provides is a resilient distributed dataset (RDD), which is the fundamental and backbone data type of this engine. This chapter introduces RDDs and shows how RDDs can be created and executed using RDD Transformations and Actions.

PySpark MLlib is the Apache Spark scalable machine learning library in Python consisting of common learning algorithms and utilities. Throughout this last chapter, you'll learn important Machine Learning algorithms. You will build a movie recommendation engine and a spam filter, and use k-means clustering.

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Introduction to Big Data analysis with Spark
Free

This chapter introduces the exciting world of Big Data, as well as the various concepts and different frameworks for processing Big Data. You will understand why Apache Spark is considered the best framework for BigData.
View Chapter Details Play Chapter Now
Programming in PySpark RDD’s

The main abstraction Spark provides is a resilient distributed dataset (RDD), which is the fundamental and backbone data type of this engine. This chapter introduces RDDs and shows how RDDs can be created and executed using RDD Transformations and Actions.
View Chapter Details Play Chapter Now
PySpark SQL & DataFrames

In this chapter, you'll learn about Spark SQL which is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as a distributed SQL query engine. This chapter shows how Spark SQL allows you to use DataFrames in Python.
View Chapter Details Play Chapter Now
Machine Learning with PySpark MLlib

PySpark MLlib is the Apache Spark scalable machine learning library in Python consisting of common learning algorithms and utilities. Throughout this last chapter, you'll learn important Machine Learning algorithms. You will build a movie recommendation engine and a spam filter, and use k-means clustering.
View Chapter Details Play Chapter Now

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Upendra Kumar Devisetty

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As a Data Scientist, I employ Data Science and Machine Learning for the integrative analysis of Big Data using high-throughput methods on advanced distributed computing systems. I enjoy answering compelling questions to enable discovery and novel insights from Big Data.

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Big Data Fundamentals with PySpark

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Loved by learners at thousands of top companies:

Course Description

Introduction to Big Data analysis with Spark

What is Big Data?

The 3 V's of Big Data

PySpark: Spark with Python

Understanding SparkContext

Interactive Use of PySpark

Loading data in PySpark shell

Review of functional programming in Python

Use of lambda() with map()

Use of lambda() with filter()

PySpark SQL & DataFrames

Abstracting Data with DataFrames

RDD to DataFrame

Loading CSV into DataFrame

Operating on DataFrames in PySpark

Inspecting data in PySpark DataFrame

PySpark DataFrame subsetting and cleaning

Filtering your DataFrame

Interacting with DataFrames using PySpark SQL

Running SQL Queries Programmatically

SQL queries for filtering Table

Data Visualization in PySpark using DataFrames

PySpark DataFrame visualization

Part 1: Create a DataFrame from CSV file

Part 2: SQL Queries on DataFrame

Part 3: Data visualization

Programming in PySpark RDD’s

Abstracting Data with RDDs

RDDs from Parallelized collections

RDDs from External Datasets

Partitions in your data

Basic RDD Transformations and Actions

Map and Collect

Filter and Count

Pair RDDs in PySpark

ReduceBykey and Collect

SortByKey and Collect

Advanced RDD Actions

CountingBykeys

Create a base RDD and transform it

Remove stop words and reduce the dataset

Print word frequencies

Machine Learning with PySpark MLlib

Overview of PySpark MLlib

PySpark MF libraries

PySpark MLlib algorithms

Collaborative filtering

Loading Movie Lens dataset into RDDs

Model training and predictions

Model evaluation using MSE

Classification

Loading spam and non-spam data

Feature hashing and LabelPoint

Logistic Regression model training

Clustering

Loading and parsing the 5000 points data

K-means training

Visualizing clusters

Congratulations!