Let's learn how to create and use DataFrames for Big Data Analytics. For easy understanding and a quick example, the pyspark
shell is to be used for the code in this chapter. The data needed for exercises used in this chapter can be found at https://github.com/apache/spark/tree/master/examples/src/main/resources. You can always create multiple data formats by reading one type of data file. For example, once you read .json
file, you can write data in parquet, ORC, or other formats.
Big Data Analytics
By :
Big Data Analytics
By:
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
Free Chapter
Big Data Analytics at a 10,000-Foot View
Getting Started with Apache Hadoop and Apache Spark
Deep Dive into Apache Spark
Big Data Analytics with Spark SQL, DataFrames, and Datasets
Real-Time Analytics with Spark Streaming and Structured Streaming
Notebooks and Dataflows with Spark and Hadoop
Machine Learning with Spark and Hadoop
Building Recommendation Systems with Spark and Mahout
Graph Analytics with GraphX
Interactive Analytics with SparkR
Index
Customer Reviews