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Pro Spark Streaming

Pro Spark Streaming
  • Author : Zubair Nabi
  • Publisher :Unknown
  • Release Date :2016-06-13
  • Total pages :230
  • ISBN : 9781484214794
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Summary : Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture Who This Book Is For Data scientists, big data experts, BI analysts, and data architects.

Learning Spark

Learning Spark
  • Author : Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia
  • Publisher :Unknown
  • Release Date :2015-01-28
  • Total pages :276
  • ISBN : 9781449359058
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Summary : 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

Spark: The Definitive Guide

Spark: The Definitive Guide
  • Author : Bill Chambers,Matei Zaharia
  • Publisher :Unknown
  • Release Date :2018-02-08
  • Total pages :606
  • ISBN : 9781491912294
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Summary : 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 Learn how you can apply MLlib to a variety of problems, including classification or recommendation

Stream Processing with Apache Spark

Stream Processing with Apache Spark
  • Author : Gerard Maas,Francois Garillot
  • Publisher :Unknown
  • Release Date :2019-06-05
  • Total pages :452
  • ISBN : 9781491944219
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Summary : Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You’ll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams

Cognitive Analytics: Concepts, Methodologies, Tools, and Applications

Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
  • Author : Management Association, Information Resources
  • Publisher :Unknown
  • Release Date :2020-03-06
  • Total pages :1961
  • ISBN : 9781799824619
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Summary : Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries, including business and healthcare. It is necessary to develop specific software programs that can analyze and interpret large amounts of data quickly in order to ensure adequate usage and predictive results. Cognitive Analytics: Concepts, Methodologies, Tools, and Applications provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques. It also examines the incorporation of pattern management as well as decision-making and prediction processes through the use of data management and analysis. Highlighting a range of topics such as natural language processing, big data, and pattern recognition, this multi-volume book is ideally designed for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, software engineers, IT specialists, and academicians.

Pro Hadoop Data Analytics

Pro Hadoop Data Analytics
  • Author : Kerry Koitzsch
  • Publisher :Unknown
  • Release Date :2016-12-29
  • Total pages :298
  • ISBN : 9781484219102
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Summary : Learn advanced analytical techniques and leverage existing tool kits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems that go beyond the basics of classification, clustering, and recommendation. Pro Hadoop Data Analytics emphasizes best practices to ensure coherent, efficient development. A complete example system will be developed using standard third-party components that consist of the tool kits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system. The book also highlights the importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. You'll discover the importance of mix-and-match or hybrid systems, using different analytical components in one application. This hybrid approach will be prominent in the examples. What You'll Learn Build big data analytic systems with the Hadoop ecosystem Use libraries, tool kits, and algorithms to make development easier and more effective Apply metrics to measure performance and efficiency of components and systems Connect to standard relational databases, noSQL data sources, and more Follow case studies with example components to create your own systems Who This Book Is For Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop, the Hadoop ecosystem, and other associated technologies.

Beginning Apache Spark 2

Beginning Apache Spark 2
  • Author : Hien Luu
  • Publisher :Unknown
  • Release Date :2018-08-16
  • Total pages :393
  • ISBN : 9781484235799
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Summary : 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.

Mastering Apache Spark

Mastering Apache Spark
  • Author : Mike Frampton
  • Publisher :Unknown
  • Release Date :2015-09-30
  • Total pages :318
  • ISBN : 9781783987153
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Summary : Gain expertise in processing and storing data by using advanced techniques with Apache Spark About This Book Explore the integration of Apache Spark with third party applications such as H20, Databricks and Titan Evaluate how Cassandra and Hbase can be used for storage An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities 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 Extend the tools available for processing and storage Examine clustering and classification using MLlib Discover Spark stream processing via Flume, HDFS Create a schema in Spark SQL, and learn how a Spark schema can be populated with data Study Spark based graph processing using Spark GraphX Combine Spark with H20 and deep learning and learn why it is useful Evaluate how graph storage works with Apache Spark, Titan, HBase and Cassandra Use Apache Spark in the cloud with Databricks and AWS In Detail Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment. 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.

High Performance Spark

High Performance Spark
  • Author : Holden Karau,Rachel Warren
  • Publisher :Unknown
  • Release Date :2017-05-25
  • Total pages :358
  • ISBN : 9781491943175
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Summary : 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

Practical Apache Spark

Practical Apache Spark
  • Author : Subhashini Chellappan,Dharanitharan Ganesan
  • Publisher :Unknown
  • Release Date :2019-01-02
  • Total pages :280
  • ISBN : 9781484236529
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Summary : Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, 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, DataFrames, 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.

Machine Learning

Machine Learning
  • Author : Jason Bell
  • Publisher :Unknown
  • Release Date :2014-10-20
  • Total pages :408
  • ISBN : 9781118889497
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Summary : Dig deep into the data with a hands-on guide to machinelearning Machine Learning: Hands-On for Developers and TechnicalProfessionals provides hands-on instruction and fully-codedworking examples for the most common machine learning techniquesused by developers and technical professionals. The book contains abreakdown of each ML variant, explaining how it works and how it isused within certain industries, allowing readers to incorporate thepresented techniques into their own work as they follow along. Acore tenant of machine learning is a strong focus on datapreparation, and a full exploration of the various types oflearning algorithms illustrates how the proper tools can help anydeveloper extract information and insights from existing data. Thebook includes a full complement of Instructor's Materials tofacilitate use in the classroom, making this resource useful forstudents and as a professional reference. At its core, machine learning is a mathematical, algorithm-basedtechnology that forms the basis of historical data mining andmodern big data science. Scientific analysis of big data requires aworking knowledge of machine learning, which forms predictionsbased on known properties learned from training data. MachineLearning is an accessible, comprehensive guide for thenon-mathematician, providing clear guidance that allows readersto: Learn the languages of machine learning including Hadoop,Mahout, and Weka Understand decision trees, Bayesian networks, and artificialneural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficientmachine learning By learning to construct a system that can learn from data,readers can increase their utility across industries. Machinelearning sits at the core of deep dive data analysis andvisualization, which is increasingly in demand as companiesdiscover the goldmine hiding in their existing data. For the techprofessional involved in data science, Machine Learning:Hands-On for Developers and Technical Professionals providesthe skills and techniques required to dig deeper.

Exam Ref 70-775 Perform Data Engineering on Microsoft Azure HDInsight

Exam Ref 70-775 Perform Data Engineering on Microsoft Azure HDInsight
  • Author : Raju Shreewastava
  • Publisher :Unknown
  • Release Date :2018-05-22
  • Total pages :432
  • ISBN : 1509308059
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Summary : Direct from Microsoft, this Exam Ref is the official study guide for the Microsoft 70-775 Perform Data Engineering on Microsoft Azure HDInsight certification exam. Exam Ref 70-775 Perform Data Engineering on Microsoft Azure HDInsight offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern IT professionals need to successfully administer and provision HDInsight clusters, and implement effective Big Data processing solutions with HDInsight. Coverage includes: Deploy and configure HDInsight clusters, deploy and secure multi-user HDInsight clusters, ingest data for processing, and manage and debug HDInsight jobs Implement Big Data batch solutions with Hive and Apache Pig, design batch ETL solutions with Spark, and operationalize Hadoop and Spark Create and implement interactive queries with Spark SQL and Interactive Hive; perform exploratory analyses with Spark SQL and Hive, Jupyter, and Apache Zeppelin; perform interactive processing with Apache Phoenix on HBase Implement real-time processing: create Spark streaming applications (including structured streaming); leverage Apache Storm, Kafka, and HBase Microsoft Exam Ref publications stand apart from third-party study guides because they: Provide guidance from Microsoft, the creator of Microsoft certification exams Target IT professional-level exam candidates with content focused on their needs, not "one-size-fits-all" content Streamline study by organizing material according to the exam's objective domain (OD), covering one functional group and its objectives in each chapter Feature Thought Experiments to guide candidates through a set of "what if?" scenarios, and prepare them more effectively for Pro-level style exam questions Explore big picture thinking around the planning and design aspects of the IT pro's job role This is one of two exams required to earn the MCSA Data Engineering with Azure certification. (The second is Exam 70-776 Perform Big Data Engineering on Microsoft Cloud Services.)

Ninja: Get Good

Ninja: Get Good
  • Author : Tyler "Ninja" Blevins
  • Publisher :Unknown
  • Release Date :2019-08-20
  • Total pages :160
  • ISBN : 9781984826763
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Summary : From one of the leading Fortnite gamers in the world comes your game plan for outclassing the rest at playing video games. Packed with illustrations, photographs, anecdotes, and insider tips, this complete compendium includes everything Tyler "Ninja" Blevins wishes he knew before he got serious about gaming. Here's how to: -Build a gaming PC -Practice with purpose -Develop strategy -Improve your game sense -Pull together the right team -Stream with skill -Form a community online -And much more Video games come and go, but Ninja's lessons are timeless. Pay attention to them and you'll find that you're never really starting over when the next big game launches. Who knows--you may even beat him one day. As he says, that's up to you.

Relevant Query Answering over Streaming and Distributed Data

Relevant Query Answering over Streaming and Distributed Data
  • Author : Shima Zahmatkesh
  • Publisher :Unknown
  • Release Date :2021
  • Total pages :229
  • ISBN : 9783030383398
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Summary :

Official Google Cloud Certified Professional Data Engineer Study Guide

Official Google Cloud Certified Professional Data Engineer Study Guide
  • Author : Dan Sullivan
  • Publisher :Unknown
  • Release Date :2020-05-11
  • Total pages :352
  • ISBN : 9781119618454
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Summary : The proven Study Guide that prepares you for this new Google Cloud exam The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. • Build and operationalize storage systems, pipelines, and compute infrastructure • Understand machine learning models and learn how to select pre-built models • Monitor and troubleshoot machine learning models • Design analytics and machine learning applications that are secure, scalable, and highly available. This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.

Kafka: The Definitive Guide

Kafka: The Definitive Guide
  • Author : Neha Narkhede,Gwen Shapira,Todd Palino
  • Publisher :Unknown
  • Release Date :2017-08-31
  • Total pages :322
  • ISBN : 9781491936115
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Summary : Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems

Practical Real-time Data Processing and Analytics

Practical Real-time Data Processing and Analytics
  • Author : Shilpi Saxena,Saurabh Gupta
  • Publisher :Unknown
  • Release Date :2017-09-28
  • Total pages :360
  • ISBN : 9781787289864
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Summary : A practical guide to help you tackle different real-time data processing and analytics problems using the best tools for each scenario About This Book Learn about the various challenges in real-time data processing and use the right tools to overcome them This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time Who This Book Is For If you are a Java developer who would like to be equipped with all the tools required to devise an end-to-end practical solution on real-time data streaming, then this book is for you. Basic knowledge of real-time processing would be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great. What You Will Learn Get an introduction to the established real-time stack Understand the key integration of all the components Get a thorough understanding of the basic building blocks for real-time solution designing Garnish the search and visualization aspects for your real-time solution Get conceptually and practically acquainted with real-time analytics Be well equipped to apply the knowledge and create your own solutions In Detail With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you'll be equipped with a clear understanding of how to solve challenges on your own. We'll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You'll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner. Style and Approach In this practical guide to real-time analytics, each chapter begins with a basic high-level concept of the topic, followed by a practical, hands-on implementation of each concept, where you can see the working and execution of it. The book is written in a DIY style, with plenty of practical use cases, well-explained code examples, and relevant screenshots and diagrams.

Data Analytics with Hadoop

Data Analytics with Hadoop
  • Author : Benjamin Bengfort,Jenny Kim
  • Publisher :Unknown
  • Release Date :2016-06
  • Total pages :288
  • ISBN : 9781491913765
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Summary : Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib

Mining of Massive Datasets

Mining of Massive Datasets
  • Author : Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman
  • Publisher :Unknown
  • Release Date :2014-11-13
  • Total pages :476
  • ISBN : 9781107077232
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Summary : Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Spark in Action

Spark in Action
  • Author : Petar Zecevic,Marko Bonaci
  • Publisher :Unknown
  • Release Date :2016-08-28
  • Total pages :450
  • ISBN : 1617292605
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Summary : Working with big data can be complex and challenging, in part because of the multiple analysis frameworks and tools required. Apache Spark is a big data processing framework perfect for analyzing near-real-time streams and discovering historical patterns in batched data sets. But Spark goes much further than other frameworks. By including machine learning and graph processing capabilities, it makes many specialized data processing platforms obsolete. Spark's unified framework and programming model significantly lowers the initial infrastructure investment, and Spark's core abstractions are intuitive for most Scala, Java, and Python developers. Spark in Action teaches readers to use Spark for stream and batch data processing. It starts with an introduction to the Spark architecture and ecosystem followed by a taste of Spark's command line interface. Readers then discover the most fundamental concepts and abstractions of Spark, particularly Resilient Distributed Datasets (RDDs) and the basic data transformations that RDDs provide. The first part of the book covers writing Spark applications using the the core APIs. Readers also learn how to work with structured data using Spark SQL, how to process near-real time data with Spark Streaming, how to apply machine learning algorithms with Spark MLlib, how to apply graph algorithms on graph-shaped data using Spark GraphX, and an introduction to Spark clustering. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Mastering Azure Analytics

Mastering Azure Analytics
  • Author : Zoiner Tejada
  • Publisher :Unknown
  • Release Date :2017-04-06
  • Total pages :412
  • ISBN : 9781491956625
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Summary : Helps users understand the breadth of Azure services by organizing them into a reference framework they can use when crafting their own big-data analytics solution.