Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By : Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei
Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By: Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei

Overview of this book

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: • Mastering Apache Spark 2.x by Romeo Kienzler • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook
Table of Contents (23 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Downloading and understanding the famous Iris data for unsupervised classification


In this recipe, we download and inspect the well-known Iris dataset in preparation for the upcoming streaming KMeans recipe, which lets you see classification/clustering in real-time.

The data is housed on the UCI machine learning repository, which is a great source of data to prototype algorithms on. You will notice that R bloggers tend to love this dataset.

How to do it...

  1. You can start by downloading the dataset using either two of the following commands:
wget https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

You can also use the following command:

curl https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data -o iris.data

You can also use the following command:

https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
  1. Now we begin our first step of data exploration by examining how the data in iris.data is formatted:
head -5 iris.data
5.1,3.5,1.4,0.2,Iris-setosa...