Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (12 chapters)

Creating a DataFrame


With Spark session object, applications can create DataFrames from an existing RDD, a Hive table, or a number of data sources we mentioned earlier in Chapter 3, ELT with Spark. We have looked at creating DataFrames in our previous chapter especially from TextFiles and JSON documents. We are going to use a Call Detail Records (CDR) dataset for some basic data manipulation with DataFrames. The dataset is available from this book's website if you want to use the same dataset for your practice.

A sample of the data set looks like the following screenshot:

Figure 4.8: Sample CDRs data set

Manipulating a DataFrame

We are going to perform the following actions on this data set:

  1. Load the dataset as a DataFrame.
  2. Print the top 20 records from the data frame.
  3. Display Schema.
  4. Count total number of calls originating from London.
  5. Count total revenue with calls originating from revenue and terminating in Manchester.
  6. Register the dataset as a table to be operated on using SQL.

Scala DataFrame...