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.

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()

Introduction to Big Data analysis with Spark

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.

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

Programming in PySpark RDD’s

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.

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

PySpark SQL & DataFrames

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.

Tinjauan PySpark MLlib

Pustaka ML di PySpark

Algoritme PySpark MLlib

Collaborative filtering

Memuat himpunan data MovieLens ke dalam RDD

Pelatihan model dan prediksi

Evaluasi model menggunakan MSE

Klasifikasi

Memuat data spam dan non-spam

Feature hashing dan LabelPoint

Pelatihan model Logistic Regression

Pengelompokan

Memuat dan mengurai data 5000 points

Pelatihan K-means

Memvisualisasikan klaster

Selamat!

Machine Learning with PySpark MLlib

Complete Shakespeare

Movie ratings

5000 points

FIFA 2018

People

Spam

Selama beberapa tahun terakhir, Big Data banyak diperbincangkan dan kini telah menjadi arus utama bagi banyak perusahaan. Namun, apa sebenarnya Big Data itu? Kursus ini membahas dasar-dasar Big Data melalui PySpark. Spark adalah kerangka kerja komputasi klaster yang "secepat kilat" untuk Big Data. Spark menyediakan mesin platform pemrosesan data umum dan memungkinkan Anda menjalankan program hingga 100x lebih cepat di memori, atau 10x lebih cepat di disk, dibandingkan Hadoop. Anda akan menggunakan PySpark, paket Python untuk pemrograman Spark beserta pustaka tingkat tingginya yang andal seperti SparkSQL, MLlib (untuk Machine Learning), dan lain-lain. Anda akan mengeksplorasi karya William Shakespeare, menganalisis data Fifa 2018, dan melakukan pengelompokan pada himpunan data genomik. Di akhir kursus, Anda akan memiliki pemahaman mendalam tentang PySpark dan penerapannya untuk analisis Big Data secara umum.

Introduction to Python

Pelajari analisis Big Data dengan PySpark, SparkSQL, dan MLlib untuk pemrograman Spark.

Fundamental Big Data dengan PySpark

Big Data dengan PySpark

Pustaka ML di PySpark

Fundamental Big Data dengan PySpark

Latihan interaktif praktis