20 Best Book For Apache Spark (2023 Update)

Take a journey toward discovering, learning, and using apache spark 3.0. in this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of apache spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications. beginning apache spark 3 begins by explaining different ways of interacting with apache spark, such as spark concepts and architecture, and spark unified stack. next, it offers an overview of spark sql before moving on to its advanced features. it covers tips and techniques for dealing with performance issues, followed by an overview of the structured streaming processing engine. it concludes with a demonstration of how to develop machine learning applications using spark mllib and how to manage the machine learning development lifecycle. this book is packed with practical examples and code snippets to help you master concepts and features immediately after they are covered in each section. after reading this book, you will have the knowledge required to build your own big data pipelines, applications, and machine learning applications. – what you will learn – master the spark unified data analytics engine and its various components – work in tandem to provide a scalable, fault tolerant and performant data processing engine – leverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and spark sqldevelop machine learning applications using spark mllib – manage the machine learning development lifecycle using mlflow – who this book is for – data scientists, data engineers and software developers.

Jeffrey aven is a big data consultant and instructor based in melbourne, australia. jeff has an extensive background in data management and several years of experience consulting and teaching in the areas of hadoop, hbase, spark, and other big data ecosystem technologies. jeff has won accolades as a big data instructor and is also an accomplished consultant who has been involved in several high-profile, enterprise-scale big data implementations across different industries in the region.

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

Apache spark is amazing when everything clicks. but if you haven't seen the performance improvements you expected, or still don't feel confident enough to use spark in production, this practical book is for you. authors holden karau and rachel warren demonstrate performance optimizations to help your spark queries run faster and handle larger data sizes, while using fewer resources. ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. not only will you gain a more comprehensive understanding of spark, you'll also learn how to make it sing. with this book, you'll explore: how spark sql's new interfaces improve performance over sql's rdd data structure the choice between data joins in core spark and spark sql techniques for getting the most out of standard rdd transformations how to work around performance issues in spark's key/value pair paradigm writing high-performance spark code without scala or the jvm how to test for functionality and performance when applying suggested improvements using spark mllib and spark ml machine learning libraries spark's streaming components and external community packages

Awards for spark change: – nautilus awards 2020 in silver: gift & specialty – american book fest 2020 best book award winner in spirituality: inspirational – chanticleer international book awards 2020 semifinalist: spirituality and enlightenmentattain greater self-awareness and orient toward your highest potential through a process of engaged, incisive questioning.it’s been said that finding the right question is as important as finding its answer. as author jennie lee writes, “quality questions lead to quality answers. questions promote deeper thought, connection, authenticity, and humility.” in spark change, lee shows you how to identify your most important personal questions and explore how they might redefine the trajectory of your life. here, lee guides you through 108 inspiring prompts designed to deepen your awareness of your innermost needs and initiate powerful shifts throughout your life. whether it’s examining the attitudes that hold you back or investigating where you truly want to go in life, these guided inquiries are meant to cultivate gratitude for your gifts, peace with the present moment, and resilience in the face of life’s challenges. for the last two decades, lee has used conscious inquiry practices to encourage self-reflection in her yoga therapy practice. inspired by the source teachings of classical yoga as well as lee’s own inner journey, these prompts delve into some of the most enduring questions of psychology, self-improvement, and the spiritual path. with lee’s piercing insight and constant guidance, spark change provides 108 prime opportunities to stop, ponder, and listen to the call of your most essential self.

New york times bestseller – the star of the hit netflix series tidying up with marie kondo and author of the life-changing magic of tidying up helps you visualize your ideal lifestyle.japanese decluttering guru marie kondo has revolutionized homes—and lives—across the world. now, kondo presents an illustrated guide to usingher acclaimed konmari method to create a joy-filled home that works the way you need it to.spark joy features step-by-step folding illustrations for everything from shirts to socks, plus drawings of perfectly organized drawers and closets. kondo also answers frequently asked questions, such as whether to keep “necessary” items that may not bring you joy. with guidance on specific categories including kitchen tools, cleaning supplies, hobby goods, digital photos, and even building your own personal “power spot” in your home, this comprehensive companion is sure to spark joy in anyone who wants to simplify their life.

Michael vey is missing and it's up to the electroclan to find him in this electrifying seventh installment of the new york times bestselling series! the final book in the michael vey series opens with the electroclan facing a devastating loss: michael is missing. he made the ultimate sacrifice to save his friends and now he's gone. what is next for them and the revolution? the battle on the island of hades ended with a devastating explosion that left the island a smoking ruin and much of hatch's army dead. however, hatch survived and while his plans have certainly suffered a setback, he's more determined than ever to bring the world's governments under his control. but first, he wants to wipe out the resistance and capture the remaining members of the electroclan. as hatch's forces storm into action, it seems nothing can stand in their way. the electroclan is divided. the voice is captured, and michael's mother is being used as bait to lure the leader of the resistance out of hiding. can anything–or anyone–stop the elgen? or is this the end?

4 paperbacks, 119 total possible endings! #5 mystery of the maya: your best friend tom goes missing on assignment in mexico. you have to help find him. your search could bring you back in time to the mysterious mayan civilization, or into a modern revolution. the wrong choice could turn you into a human sacrifice on a bloody altar. 144 pages, 39 possible endings. #6 house of danger: you are an accomplished young detective with several tough cases under your belt. then comes a case that is as difficult as it is frightening. an anonymous caller begs for help. but before you can find out more, the line goes dead… the case leads you to a house with a truly evil secret. 144 pages, 20 possible endings. #7 race forever: as soon as you could see over the steering wheel of an suv, your father began teaching you about cars and driving. now you're off to africa for your first rally on your own. are you ready to face the toughest driving conditions in the world and even tougher competitors? 144 pages, 33 possible endings. #8 escape: the year is 2045. civil wars and foreign attacks have split the u.s. into three hostile political areas. you are a citizen of turtalia, a new democracy that struggles under the control of a harsh neighboring state, dorado. you have successfully infiltrated dorado and obtained important information about their plans to invade turtalia, but will you be able to make it home alive? 144 pages, 27 possible endings.

"puts you right in the cockpit with your finger on the trigger. a truly awesome read; and a climax that hollywood couldn't invent."—andy mcnab, author of immediate actionthe apache ah mk 1 is the deadliest, most technically advanced helicopter in the world—and the toughest to fly. in the cockpit of an apache, hands, feet, and even eyes need to operate independetly. as strong as a tank and equipped with two rolls-royce rtm-322 engines, the helicopter is nearly impossible to shoot down. and thanks to a powerful array of weapons and cameras, the apache helicopter can sport prey from miles away—and kill the enemy with a flick of the finger.in 2007, ed macy's apache squadron was dispatched to afghanistan's notorious helmand province, with the mission to fight alongside and protect the men on the ground by any means necessary. when a marine goes missing in action, macy and his team are the army's only hope of bringing him back alive. with a soldier strapped to each side of two gunships, they must land in the heart of jugroom fort, a taliban stronghold, and come face-to-face with hordes of their unrelenting enemy. what follows is a breathtaking rescue, unlike any the world has ever seen." macy…does for the apache helicopter's gunships what dan mills did for the infantry in sniper one: he puts readers in the cockpit of an aircraft that requires great skill and attention to keep in the air."—publishers weekly"macy is the real deal. nobody could write that powerfully about combat, or amotionally about the men fighting with him, unless he has been at the gunship's controls."—sergeant major dan mills, author of sniper one"thoroughly absorbing… a real find for students of modern warfare."—booklist

Critical acclaim for the last comanche chief"truly distinguished. neeley re-creates the character and achievements of this most significant of all comanche leaders." — robert m.

Data in all domains is getting bigger. how can you work with it efficiently? recently updated for spark 1.3, this book introduces apache spark, the open source cluster computing system that makes data analytics fast to write and fast to run. with spark, you can tackle big datasets quickly through simple apis in python, java, and scala. this edition includes new information on spark sql, spark streaming, setup, and maven coordinates. written by the developers of spark, this book will have data scientists and engineers up and running in no time. 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. quickly dive into spark capabilities such as distributed datasets, in-memory caching, and the interactive shell leverage spark's powerful built-in libraries, including spark sql, spark streaming, and mllib use one programming paradigm instead of mixing and matching tools like hive, hadoop, mahout, and storm learn how to deploy interactive, batch, and streaming applications connect to data sources including hdfs, hive, json, and s3 master advanced topics like data partitioning and shared variables

Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data – key features: become well-versed with the core concepts of apache spark and delta lake for building data platforms – learn how to ingest, process, and analyze data that can be later used for training machine learning models – understand how to operationalize data models in production using curated data – book description: in the world of ever-changing data and ever-evolving schemas, it is important to build data pipelines that can auto-adjust to changes. this book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. – starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use microsoft azure cloud services effectively for data engineering. you'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. once you've explored the main features of delta lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using delta lake. packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. – by the end of this data engineering book, you'll have learned how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ml, and artificial intelligence (ai) tasks. – what you will learn: discover the challenges you may face in the data engineering world – add acid transactions to apache spark using delta lake – understand effective design strategies to build enterprise-grade data lakes – explore architectural and design patterns for building efficient data ingestion pipelines – orchestrate a data pipeline for preprocessing data using apache spark and delta lake apis – automate deployment and monitoring of data pipelines in production – get to grips with securing, monitoring, and managing data pipelines models efficiently – who this book is for: this book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. if you already work with py – spark and want to use delta lake for data engineering, you'll find this book useful. basic knowledge of python, spark, and sql is expected.

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. you'll explore the basic operations and common functions of spark'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, spark's scalable machine-learning library. get a gentle overview of big data and spark. learn about dataframes, sql, and datasets-spark's core apis-through worked examples. dive into spark'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, spark's stream-processing engine.

Deepen the conversation-and connection-with a year of daily questions. – what if you asked your partner to describe their ideal day instead of the same old "how was your day"? what if you shared dreams about love instead of lists of errands? wouldn't it be wonderful if your communication ran deeper every day? open this couples journal and let the conversation begin. – a year of us: a couples journal gives you one insightful question per day to spark real talk, intimacy, and fun in your relationship. over the course of a year, you'll ask each other about big dreams and small moments that make up your lives together. with topics that cover future goals, sexuality, psychology, and more, this couples journal proves that-no matter how long you've been together-when your conversation goes deeper, so does your connection. – a year of us: a couples journal takes you on a journey to: grow closer-chart new territory in your outer lives and inner worlds-with questions designed to encourage growth. – ask and answer one question per day-dive deep into dreams and goals, past and present, intimacy and sexuality, philosophy and psychology. – record your discoveries-jot down answers for every question to track your process in this couples journal. plus, dedicated pages allow you to set goals and reflect on your year of journaling together. one year from now, you'll know each other better and love each other stronger with a year of us: a couples journal.

Analyze vast amounts of data in record time using apache spark with databricks in the cloud. learn the fundamentals, and more, of running analytics on large clusters in azure and aws, using apache spark with databricks on top. discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. – this book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. you will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. from there you will learn how apache spark, an open source framework, can enable all those cpus for data analytics use. finally, you will see how services such as databricks provide the power of apache spark, without you having to know anything about configuring hardware or software. by removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. – this book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including apache spark, apache hadoop, apache hive, python, and sql. valuable exercises help reinforce what you have learned. – what you will learn – discover the value of big data analytics that leverage the power of the cloud – get started with databricks using sql and python in either microsoft azure or awsunderstand the underlying technology, and how the cloud and spark fit into the bigger picture – see how these tools are used in the real world – run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free – this book is for data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. it is assumed that the reader has data experience, but perhaps minimal exposure to apache spark and azure databricks. the book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation. – robert ilijason is a 20-year veteran in the business intelligence (bi) segment. he has worked as a contractor for some of europe’s biggest companies and has conducted large-scale analytics projects within the areas of retail, telecom, banking, government, and more. robert has seen his share of analytic trends come and go over the years, but unlike most of them, he strongly believes that apache spark in the cloud, especially with azure databricks, is a game changer.

Develop applications for the big data landscape with spark and hadoop. this book also explains the role of spark in developing scalable machine learning and analytics applications with cloud technologies. beginning apache spark 2 gives you an introduction to apache spark and shows you how to work with it. – along the way, you’ll discover resilient distributed datasets (rdds); use spark sql for structured data; and learn stream processing and build real-time applications with spark structured streaming. furthermore, you’ll learn the fundamentals of spark ml for machine learning and much more. after you read this book, you will have the fundamentals to become proficient in using apache spark and know when and how to apply it to your big data applications. what you will learn understand spark unified data processing platform – how to run spark in spark shell or databricks use and manipulate rdds deal with structured data using spark sql through its operations and advanced functions – build real-time applications using spark structured streaming – develop intelligent applications with the spark machine learning library – who this book is for – programmers and developers active in big data, hadoop, and java but who are new to the apache spark platform.

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 deep – ml, h2o and deeplearning4j* study highly optimised unified batch and real-time data processing using spark – sql and structured streaming* evaluate large-scale graph processing and analysis using graph – x and graph – frames* 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, system – ml and graph – frames* 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, system – ml, 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 data – frames and datasets. – you will also learn about the updates on the apis and how data – frames 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.

This book discusses various components of spark such as spark core, data – frames, datasets and sql, spark streaming, spark mlib, and r on spark with the help of practical code snippets for each topic. practical apache spark also covers the integration of apache spark with kafka with examples. you’ll follow a learn-to-do-by-yourself approach to learning – learn the concepts, practice the code snippets in scala, and complete the assignments given to get an overall exposure. on completion, you’ll have knowledge of the functional programming aspects of scala, and hands-on expertise in various spark components. you’ll also become familiar with machine learning algorithms with real-time usage. – what you will learn – discover the functional programming features of scala – understand the complete architecture of spark and its components – integrate apache spark with hive and kafka use spark sql, data – frames, and datasets to process data using traditional sql queries – work with different machine learning concepts and libraries using spark's mllib packages – who this book is for – developers and professionals who deal with batch and stream data processing.

A solution-based guide to put your deep learning models into production with the power of apache spark key features discover practical recipes for distributed deep learning with apache spark learn to use libraries such as keras and tensorflow solve problems in order to train your deep learning models on apache spark book description with deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. as a result, this will help deep learning models train with higher efficiency and speed. with the help of the apache spark deep learning cookbook, you'll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. from setting up apache spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. in addition to this, you'll get access to deep learning code within spark that can be reused to answer similar problems or tweaked to answer slightly different problems. you will also learn how to stream and cluster your data with spark. once you have got to grips with the basics, you'll explore how to implement and deploy deep learning models, such as convolutional neural networks (cnn) and recurrent neural networks (rnn) in spark, using popular libraries such as tensorflow and keras. by the end of the book, you'll have the expertise to train and deploy efficient deep learning models on apache spark. knowledge of the core machine learning concepts and a basic understanding of the apache spark framework is required to get the best out of this book. additionally, some programming knowledge in python is a plus.

Speed up the design and implementation of deep learning solutions using apache spark key features explore the world of distributed deep learning with apache spark train neural networks with deep learning libraries such as bigdl and tensorflow develop spark deep learning applications to intelligently handle large and complex datasets book description deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. hands-on deep learning with apache spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on apache spark. the book starts with the fundamentals of apache spark and deep learning. you will set up spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. you will then implement deep learning models, such as convolutional neural networks (cnns), recurrent neural networks (rnns), and long short-term memory (lstm) on spark. as you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. during the course of this book, you will use popular deep learning frameworks, such as tensorflow, deeplearning4j, and keras to train your distributed models. by the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases. what you will learn understand the basics of deep learning set up apache spark for deep learning understand the principles of distribution modeling and different types of neural networks obtain an understanding of deep learning algorithms discover textual analysis and deep learning with spark use popular deep learning frameworks, such as deeplearning4j, tensorflow, and keras explore popular deep learning algorithms who this book is for if you are a scala developer, data scientist, or data analyst who wants to learn how to use spark for implementing efficient deep learning models, hands-on deep learning with apache spark is for you. knowledge of the core machine learning concepts and some exposure to spark will be helpful.