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Learning Spark
Jules S. Damji,Brooke Wenig,Tathagata Das,Denny Lee
Author : Jules S. Damji,Brooke Wenig,Tathagata Das,Denny Lee
Publisher : O'Reilly Media
Page : 400 pages
File Size : 46,5 Mb
Release : 2020-07-16
Category : Computers
ISBN : 9781492050018
Learning Spark by Jules S. Damji,Brooke Wenig,Tathagata Das,Denny Lee Pdf
Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow
Spark in Action, Second Edition
Jean-Georges Perrin
Author : Jean-Georges Perrin
Publisher : Manning Publications
Page : 574 pages
File Size : 41,9 Mb
Release : 2020-06-02
Category : Computers
ISBN : 9781617295522
Spark in Action, Second Edition by Jean-Georges Perrin Pdf
Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment
Machine Learning with Spark - Second Edition
Rajdeep Dua,Manpreet Singh Ghotra,Nick Pentreath
Author : Rajdeep Dua,Manpreet Singh Ghotra,Nick Pentreath
Publisher : Unknown
Page : 572 pages
File Size : 49,7 Mb
Release : 2016-10-31
Category : Electronic
ISBN : 1785889931
Machine Learning with Spark - Second Edition by Rajdeep Dua,Manpreet Singh Ghotra,Nick Pentreath Pdf
Develop intelligent machine learning systems with SparkAbout This Book*Get to the grips with the latest version of Apache Spark*Utilize Spark's machine learning library to implement predictive analytics*Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.What You Will Learn*Get hands-on with the latest version of Spark ML*Create your first Spark program with Scala and Python*Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2*Access public machine learning datasets and use Spark to load, process, clean, and transform data*Use Spark's machine learning library to implement programs by utilizing well-known machine learning models*Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models*Write Spark functions to evaluate the performance of your machine learning modelsIn DetailSpark ML is the machine learning module of Spark. It uses in-memory RDDs to process machine learning models faster for clustering, classification, and regression.This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.
Learning Spark
Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia
Author : Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia
Publisher : "O'Reilly Media, Inc."
Page : 276 pages
File Size : 52,8 Mb
Release : 2015-01-28
Category : Computers
ISBN : 9781449359065
Learning Spark by Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia Pdf
This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.--
Learning Spark, 2nd Edition
Jules Damji,Denny Lee,Brooke Wenig,Tathagata Das
Author : Jules Damji,Denny Lee,Brooke Wenig,Tathagata Das
Publisher : Unknown
Page : 300 pages
File Size : 51,7 Mb
Release : 2020
Category : Electronic
ISBN : OCLC:1119132190
Learning Spark, 2nd Edition by Jules Damji,Denny Lee,Brooke Wenig,Tathagata Das Pdf
Data is getting bigger, arriving faster, and coming in varied formats-and it all needs to be processed at scale for analytics or machine learning. How can you process such varied data workloads efficiently? Enter Apache Spark. Updated to emphasize new features in Spark 2.x., this second edition shows data engineers and scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine-learning algorithms. Through discourse, code snippets, and notebooks, you'll be able to: Learn Python, SQL, Scala, or Java high-level APIs: DataFrames and Datasets Peek under the hood of the Spark SQL engine to understand Spark transformations and performance Inspect, tune, and debug your Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow Use open source Pandas framework Koalas and Spark for data transformation and feature engineering.
Fast Data Processing with Spark - Second Edition
Krishna Sankar,Holden Karau
Author : Krishna Sankar,Holden Karau
Publisher : Unknown
Page : 0 pages
File Size : 51,7 Mb
Release : 2015-03-31
Category : Apache Hadoop
ISBN : 178439257X
Fast Data Processing with Spark - Second Edition by Krishna Sankar,Holden Karau Pdf
Chapter 2: Using the Spark Shell; Loading a simple text file; Using the Spark shell to run Logistic regression; Interactively Loading data from S3; Running Spark shell in Python; Summary; Chapter 3: Building and Running a Spark Application; Building your Spark project with sbt; Building your Spark job with Maven; Building your Spark job with something else; Summary; Chapter 4: Creating a SparkContext; Scala; Java; SparkContext - metadata; Shared Java and Scala APIs; Python; Summary; Chapter 5: Loading and Saving Data in Spark; RDDs; Loading data into an RDD; Saving your data; Summary
Spark: The Definitive Guide
Bill Chambers,Matei Zaharia
Author : Bill Chambers,Matei Zaharia
Publisher : "O'Reilly Media, Inc."
Page : 712 pages
File Size : 44,6 Mb
Release : 2018-02-08
Category : Computers
ISBN : 9781491912294
Spark: The Definitive Guide by Bill Chambers,Matei Zaharia Pdf
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation
Big Data Processing with Apache Spark
Srini Penchikala
Author : Srini Penchikala
Publisher : Lulu.com
Page : 106 pages
File Size : 48,5 Mb
Release : 2018-03-13
Category : Computers
ISBN : 9781387659951
Big Data Processing with Apache Spark by Srini Penchikala Pdf
Apache Spark is a popular open-source big-data processing framework thatÕs built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, itÕs also hundred times faster in memory and ten times faster even when running on disk compared to traditional data processing frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data processing, and graph data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX.
Mastering Apache Spark 2.x
Romeo Kienzler
Author : Romeo Kienzler
Publisher : Packt Publishing Ltd
Page : 354 pages
File Size : 44,9 Mb
Release : 2017-07-26
Category : Computers
ISBN : 9781785285226
Mastering Apache Spark 2.x by Romeo Kienzler Pdf
Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark. Master the art of real-time processing with the help of Apache Spark 2.x Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames Learn how specific parameter settings affect overall performance of an Apache Spark cluster Leverage Scala, R and python for your data science projects In Detail Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform. The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x. You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets. You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks. Style and approach This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.
Mastering Apache Spark 2.x
Romeo Kienzler
Author : Romeo Kienzler
Publisher : Unknown
Page : 354 pages
File Size : 42,6 Mb
Release : 2017-07-20
Category : Computers
ISBN : 1786462745
Mastering Apache Spark 2.x by Romeo Kienzler Pdf
Advanced analytics on your Big Data with latest Apache Spark 2.xAbout This Book* An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities.* Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark.* Master the art of real-time processing with the help of Apache Spark 2.xWho This Book Is ForIf you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected.What You Will Learn* Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J* Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming* Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames* Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud* Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames* Learn how specific parameter settings affect overall performance of an Apache Spark cluster* Leverage Scala, R and python for your data science projectsIn DetailApache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform.The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x.You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets.You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks.Style and approachThis book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.
Apache Spark Quick Start Guide
Shrey Mehrotra,Akash Grade
Author : Shrey Mehrotra,Akash Grade
Publisher : Packt Publishing Ltd
Page : 150 pages
File Size : 46,5 Mb
Release : 2019-01-31
Category : Computers
ISBN : 9781789342666
Apache Spark Quick Start Guide by Shrey Mehrotra,Akash Grade Pdf
A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark. Key FeaturesLearn about the core concepts and the latest developments in Apache SparkMaster writing efficient big data applications with Spark’s built-in modules for SQL, Streaming, Machine Learning and Graph analysisGet introduced to a variety of optimizations based on the actual experienceBook Description Apache Spark is a flexible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications. What you will learnLearn core concepts such as RDDs, DataFrames, transformations, and moreSet up a Spark development environmentChoose the right APIs for your applicationsUnderstand Spark’s architecture and the execution flow of a Spark applicationExplore built-in modules for SQL, streaming, ML, and graph analysisOptimize your Spark job for better performanceWho this book is for If you are a big data enthusiast and love processing huge amount of data, this book is for you. If you are data engineer and looking for the best optimization techniques for your Spark applications, then you will find this book helpful. This book also helps data scientists who want to implement their machine learning algorithms in Spark. You need to have a basic understanding of any one of the programming languages such as Scala, Python or Java.
Spark in Action, Second Edition
Jean-Georges Perrin
Author : Jean-Georges Perrin
Publisher : Manning
Page : 0 pages
File Size : 40,5 Mb
Release : 2020-06-02
Category : Computers
ISBN : 1617295523
Spark in Action, Second Edition by Jean-Georges Perrin Pdf
Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Foreword by Rob Thomas. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment
An Architecture for Fast and General Data Processing on Large Clusters
Matei Zaharia
Author : Matei Zaharia
Publisher : Morgan & Claypool
Page : 242 pages
File Size : 50,5 Mb
Release : 2016-05-01
Category : Computers
ISBN : 9781970001587
An Architecture for Fast and General Data Processing on Large Clusters by Matei Zaharia Pdf
The past few years have seen a major change in computing systems, as growing data volumes and stalling processor speeds require more and more applications to scale out to clusters. Today, a myriad data sources, from the Internet to business operations to scientific instruments, produce large and valuable data streams. However, the processing capabilities of single machines have not kept up with the size of data. As a result, organizations increasingly need to scale out their computations over clusters. At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too. This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing. We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective. This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.
Big Data Analytics with Spark
Mohammed Guller
Author : Mohammed Guller
Publisher : Apress
Page : 290 pages
File Size : 41,9 Mb
Release : 2015-12-29
Category : Computers
ISBN : 9781484209646
Big Data Analytics with Spark by Mohammed Guller Pdf
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 that are commonly used 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.
Big Data Processing with Apache Spark
Manuel Ignacio Franco Galeano
Author : Manuel Ignacio Franco Galeano
Publisher : Unknown
Page : 142 pages
File Size : 54,5 Mb
Release : 2018-10-31
Category : Computers
ISBN : 1789808812
Big Data Processing with Apache Spark by Manuel Ignacio Franco Galeano Pdf
No need to spend hours ploughing through endless data - let Spark, one of the fastest big data processing engines available, do the hard work for you. Key Features Get up and running with Apache Spark and Python Integrate Spark with AWS for real-time analytics Apply processed data streams to machine learning APIs of Apache Spark Book Description Processing big data in real time is challenging due to scalability, information consistency, and fault-tolerance. This book teaches you how to use Spark to make your overall analytical workflow faster and more efficient. You'll explore all core concepts and tools within the Spark ecosystem, such as Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming. You'll begin by learning data processing fundamentals using Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After grasping these fundamentals, you'll move on to using Spark Streaming APIs to consume data in real time from TCP sockets, and integrate Amazon Web Services (AWS) for stream consumption. By the end of this book, you'll not only have understood how to use machine learning extensions and structured streams but you'll also be able to apply Spark in your own upcoming big data projects. What you will learn Write your own Python programs that can interact with Spark Implement data stream consumption using Apache Spark Recognize common operations in Spark to process known data streams Integrate Spark streaming with Amazon Web Services (AWS) Create a collaborative filtering model with the movielens dataset Apply processed data streams to Spark machine learning APIs Who this book is for Data Processing with Apache Spark is for you if you are a software engineer, architect, or IT professional who wants to explore distributed systems and big data analytics. Although you don't need any knowledge of Spark, prior experience of working with Python is recommended.
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