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

Spark Streaming with Kafka and HBase

Apache Kafka is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Kafka plays an important role in any streaming application. Let's see what happens without having Kafka in a streaming application. If the streaming application processing the streams is down for 1 minute for some reason, what will happen to the stream of data for that 1 minute? We will end up losing 1 minute's worth of data. Having Kafka as one more layer buffers incoming stream data and prevents any data loss. Also, if something goes wrong within the Spark Streaming application or target database, messages can be replayed from Kafka. Once the streaming application pulls a message from Kafka, acknowledgement is sent to Kafka only when data is replicated in the streaming application. This makes Kafka a reliable receiver.

There are two approaches to receive data from Kafka.

Receiver-based approach

Using the Kafka consumer API, receivers in a...