文件名称：Big.Data.Analytics.with.Spark.and.Hadoop.17858
文件大小：6.52MB
文件格式：PDF
更新时间：2019-11-09 11:43:24
Spark Hadoop BigData Analytics
Key Features This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Book Description 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. What you will learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. About the Author Venkat Ankam has over 18 years of IT experience and over 5 years in big data technologies, working with customers to design and develop scalable big data applications. Having worked with multiple clients globally, he has tremendous experience in big data analytics using Hadoop and Spark. He is a Cloudera Certified Hadoop Developer and Administrator and also a Databricks Certified Spark Developer. He is the founder and presenter of a few Hadoop and Spark meetup groups globally and loves to share knowledge with the community. Venkat has delivered hundreds of trainings, presentations, and white papers in the big data sphere. While this is his first attempt at writing a book, many more books are in the pipeline. Table of Contents Chapter 1: Big Data Analytics at a 10,000-Foot View Chapter 2: Getting Started with Apache Hadoop and Apache Spark Chapter 3: Deep Dive into Apache Spark Chapter 4: Big Data Analytics with Spark SQL, DataFrames, and Datasets Chapter 5: Real-Time Analytics with Spark Streaming and Structured Streaming Chapter 6: Notebooks and Dataflows with Spark and Hadoop Chapter 7: Machine Learning with Spark and Hadoop Chapter 8: Building Recommendation Systems with Spark and Mahout Chapter 9: Graph Analytics with GraphX Chapter 10: Interactive Analytics with SparkR