Big Data Analytics with Spark
A Practitioner's Guide to Using Spark for Large Scale Data Analysis
(Sprache: Englisch)
Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics...
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Produktinformationen zu „Big Data Analytics with Spark “
Klappentext zu „Big Data Analytics with Spark “
Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert.Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics.
This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You'll learn the basics of functional programming in Scala, so that you can write Spark applications in it.
What's more, Big Data Analytics with Spark provides an introduction to other big data technologies thatare commonly used
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along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language.
There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost-possibly a big boost-to your career.
There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost-possibly a big boost-to your career.
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Inhaltsverzeichnis zu „Big Data Analytics with Spark “
Chapter 1: Big Data Technology Landscape (30 pages)Chapter Goal:
Familiarize readers with important big data technologies that get used along with Spark. Knowledge of some of these technologies is required in order to effectively use Spark.
- Cluster computing - Hadoop MapReduce, HDFS, Hive
- Data serialization - Avro, Proto Buffer
- Columnar storage - Parquet
- Messaging system - Kafka, ZeroMQ
- NoSQL databases - HBase, Cassandra
- Distributed SQL Query engine - Apache Drill, Impala, PrestoDB
Chapter 2: Functional Programming in Scala (30 pages)
Chapter Goal:
Introduce Scala so that readers can understand and write Spark applications in Scala, which is the primary language supported by Spark.
- Key functional programming concepts
- Basic Scala constructs
- Scala Shell
- Simple Build Tool (SBT)
- Developing and running a stand-alone Scala app
Chapter 3: Spark’s Essentials (35 pages)
Chapter Goal:
Introduce Spark fundamentals and key concepts
- What is Spark
- Why Spark is hot
- Why Spark is faster than Hadoop MapReduce
- Resilient Distributed Datasets (RDD)
Chapter 4: Spark Shell (10 pages)
Chapter Goal:
Introduce Spark Shell and show how it can be used for interactive data analysis
- Spark shell introduction
- Interactive data analysis in Spark-shell
Chapter 5: A Stand-alone Spark Application (10 pages)
Chapter Goal:
Provide step-by-step directions for writing and running a Spark application
- Basic structure of a stand-alone Spark application
- Compiling a Spark application
Chapter 6: Spark Streaming (10 pages)
Chapter Goal:
- Introduce Spark streaming and show an example app using Spark streaming
1. Spark streaming introduction
2. How Spark streaming works
3. A spark streaming example app
Chapter 7: Spark SQL (20
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pages)
Chapter Goal:
- Introduce Spark SQL along with a few examples
- Spark SQL introduction
- Why use Spark SQL
- Language integrated queries
- A sample Spark SQL app
- Spark SQL Thrift server
Chapter 8: MLib (40 pages)
Chapter Goal:
- Introduce machine learning and MLlib along with a few examples
- Machine learning introduction
- Linear regression
- Logistic regression
- Classification
- Clustering
- Recommender system
- Building a machine learning application with Mlib
- MLBase
Chapter 9: GraphX (10 pages)
Chapter Goal:
- Introduce Graph analysis and GraphX along with a few examples
- GraphX introduction
- A sample app
Chapter 10: Deploying Spark (15 pages)
This chapter would walk us through deployment of Spark with different cluster management technologies such as YARN, Mesos, and services like AWS (EC2)
Deploying Spark Cluster in Standalone Mode (2 pages)
Goal:
- Introduce Standalone Spark cluster manager and provide step-by-step directions for installing and running Spark application on a Standalone Spark cluster
- Spark stand-alone cluster introduction
- Installing a Spark cluster in stand-alone mode
- Running a Spark app on a stand-alone Spark cluster
Deploying Spark Cluster with YARN (2 pages)
Goal:
- Introduce YARN cluster manager and provide step-by-step directions for installing and running Spark application on a YARN cluster
- YARN introduction
- Installing a Spark cluster on YARN
- Running a Spark app on a Spark-on-YARN cluster
Deploying Spark Cluster with Mesos (2 pages)
Goal:
- Introduce Mesos cluster manager and provide step-by-step directions for installing and running Spark application on a Mesos cluster
- Mesos introduction
- Installing a Spark cluster with Mesos
- Running a Spark app on a Spark-on-Mesos cluster
Deploying Spark on AWS EC2 (2 pages)
Goal:
- Provide step-by-step directions for installing and running a Spark cluster on EC2
- EC2 introduction
- How to install and run Spark on EC2
Chapter 11: Monitoring a Spark Cluster (20 pages)
Chapter Goal:
- Introduce built-in monitoring and management capabilities provided by Spark
- Spark management interface
- Monitoring a Spark cluster using a web browser
- Monitoring Spark libraries and application
- Monitoring a Spark cluster using Ganglia
Chapter Goal:
- Introduce Spark SQL along with a few examples
- Spark SQL introduction
- Why use Spark SQL
- Language integrated queries
- A sample Spark SQL app
- Spark SQL Thrift server
Chapter 8: MLib (40 pages)
Chapter Goal:
- Introduce machine learning and MLlib along with a few examples
- Machine learning introduction
- Linear regression
- Logistic regression
- Classification
- Clustering
- Recommender system
- Building a machine learning application with Mlib
- MLBase
Chapter 9: GraphX (10 pages)
Chapter Goal:
- Introduce Graph analysis and GraphX along with a few examples
- GraphX introduction
- A sample app
Chapter 10: Deploying Spark (15 pages)
This chapter would walk us through deployment of Spark with different cluster management technologies such as YARN, Mesos, and services like AWS (EC2)
Deploying Spark Cluster in Standalone Mode (2 pages)
Goal:
- Introduce Standalone Spark cluster manager and provide step-by-step directions for installing and running Spark application on a Standalone Spark cluster
- Spark stand-alone cluster introduction
- Installing a Spark cluster in stand-alone mode
- Running a Spark app on a stand-alone Spark cluster
Deploying Spark Cluster with YARN (2 pages)
Goal:
- Introduce YARN cluster manager and provide step-by-step directions for installing and running Spark application on a YARN cluster
- YARN introduction
- Installing a Spark cluster on YARN
- Running a Spark app on a Spark-on-YARN cluster
Deploying Spark Cluster with Mesos (2 pages)
Goal:
- Introduce Mesos cluster manager and provide step-by-step directions for installing and running Spark application on a Mesos cluster
- Mesos introduction
- Installing a Spark cluster with Mesos
- Running a Spark app on a Spark-on-Mesos cluster
Deploying Spark on AWS EC2 (2 pages)
Goal:
- Provide step-by-step directions for installing and running a Spark cluster on EC2
- EC2 introduction
- How to install and run Spark on EC2
Chapter 11: Monitoring a Spark Cluster (20 pages)
Chapter Goal:
- Introduce built-in monitoring and management capabilities provided by Spark
- Spark management interface
- Monitoring a Spark cluster using a web browser
- Monitoring Spark libraries and application
- Monitoring a Spark cluster using Ganglia
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Autoren-Porträt von Mohammed Guller
Mohammed Guller is the principal architect at Glassbeam, where he leads the development of advanced and predictive analytics products. He is a big data and Spark expert. He is frequently invited to speak at big data-related conferences. He is passionate about building new products, big data analytics, and machine learning. Over the last 20 years, Mohammed has successfully led the development of several innovative technology products from concept to release. Prior to joining Glassbeam, he was the founder of TrustRecs.com, which he started after working at IBM for five years. Before IBM, he worked in a number of hi-tech start-ups, leading new product development.
Bibliographische Angaben
- Autor: Mohammed Guller
- 2016, 1st ed., XXIII, 277 Seiten, Maße: 17,8 x 25,4 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1484209656
- ISBN-13: 9781484209653
- Erscheinungsdatum: 25.12.2015
Sprache:
Englisch
Pressezitat
"Programmers seeking to learn the Spark framework and its libraries will benefit greatly from this book. ... The book is well written, with a good balance between presenting simple computer science concepts, such as functional programming, and introducing Scala, the Spark core language. ... the book provides substantial information on cluster-based data analysis using Spark, a prominent framework used by data scientists. It is very nicely written, with interesting contemporary considerations and several source code examples." (Andre Maximo, Computing Reviews, computingreviews.com, June, 2016)
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