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.

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Spis treści

Big Data Analytics. Real time analytics using Apache Spark and Hadoop eBook -- spis treści

• 1. Big Data Analytics at 10,000 foot view
• 2. Getting started with Apache Hadoop and Apache Spark
• 3. Deep Dive into Apache Spark
• 4. Big Data Analytics with Spark SQL and DataFrames
• 5. Real-time Analytics with Spark Streaming
• 6. Notebooks and Dataflows with Spark and Hadoop
• 7. Machine Learning with Spark and Hadoop
• 8. Building Recommendation Systems with Spark and Mahout
• 9. Graph analysis with Graphx
• 10. Interactive Analytics with SparkR