Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Scientific Computing and Big Data Analysis with Python and Hadoop

Handling event time and late date

Event time is the time inside the data. Spark Streaming used to define the time as the received time for DStream purposes, but for many applications that need the event time, this is not enough. For example, if you require the number of times that a hashtag appears in a tweet every minute, then you will need the time when the data was generated, not the time when Spark received the event. 

The following is an extension of the previous example of Structured Streaming, listening on server port 9999. The Timestamp is now enabled as a part of the input data, so now, we can perform window operations on the unbounded table:

import java.sql.Timestamp
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
// Creating DataFrame that represent the stream of input lines from connection
to host:port
val inputLines = spark.readStream
.option("host", "localhost")
.option("port", 9999)
.option("includeTimestamp", true)