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
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
Index

Introducing Apache Spark


Hadoop and MR have been around for 10 years and have proven to be the best solution to process massive data with high performance. However, MR lacked performance in iterative computing where the output between multiple MR jobs had to be written to HDFS. In a single MR job, it lacked performance because of the drawbacks of the MR framework.

Let's take a look at the history of computing trends to understand how computing paradigms have changed over the last two decades.

The trend has been to Reference the URI when the network was cheaper (in 1990), Replicate when storage became cheaper (in 2000), and Recompute when memory became cheaper (in 2010), as shown in Figure 2.5:

Figure 2.5: Trends of computing

Note

So, what really changed over a period of time?

Tape is dead, disk has become tape, and SSD has almost become the disk. Now, caching data in RAM is the current trend.

Let's understand why memory-based computing is important and how it provides significant performance...