Book Image

Big Data Analytics

By : Venkat Ankam
Book Image

Big Data Analytics

By: Venkat Ankam

Overview of this book

Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.
Table of Contents (18 chapters)
Big Data Analytics
About the Author
About the Reviewers

Monitoring applications

Spark Streaming jobs produce useful information for understanding the current state of the application. Broadly, there are two ways to monitor Spark Streaming jobs: using the UI and using external tools.

The Spark UI HTTP address is http://driver-host-name:4040/. When multiple SparkContexts run at the same time, they are bound to successive ports like 4041, 4042, and so on. The Spark UI provides useful information like event timeline and DAG visualizations as explained in Chapter 3, Deep Dive into Apache Spark. When a Spark Streaming application is running, a streaming tab appears on the UI, which provides information such as the number of batches completed, number of records processed, batch window time, total time of Spark Streaming application, input rate, scheduling delay, processing time, and total delay. The UI also shows the Kafka topic name, partition numbers, and offsets processed in a batch when using the Kafka direct API. This is really helpful and easy...