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

Building machine learning pipelines

Spark ML is an API built on top of the DataFrames API of Spark SQL to construct machine learning pipelines. Spark ML is inspired by the scikit-learn project, which makes it easier to combine multiple algorithms into a single pipeline. The following are the concepts used in ML pipelines:

  • DataFrame: A DataFrame is used to create rows and columns of data just like an RDBMS table. A DataFrame can contain text, feature vectors, true labels, and predictions in columns.

  • Transformer: A Transformer is an algorithm to transform a DataFrame into another DataFrame. The ML model is an example of a Transformer that transforms a DataFrame with features into a DataFrame with predictions.

  • Estimator: This is an algorithm to produce a Transformer by fitting on a DataFrame. Generating a model is an example of an Estimator.

  • Pipeline: As the name indicates, a pipeline creates a workflow by chaining multiple Transformers and Estimators together.

  • Parameter: This is an API to...