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███ ▀ * Title..: Hadoop and Spark Fundamentals
███ * Date...: 22-07-2018
▄ ███ * Type...: Bookware
▀▓███▓▓ █ * Disks..: 48x100mb
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░▒▓██████▀ ▄▀ ▀▓▀ Release notes:
▄▄▄
███ 9+ Hours of Video Instruction The perfect (and fast) way to
███ get started with Hadoop and Spark Hadoop and Spark
███ Fundamentals LiveLessons provides 9+ hours of video
███ introduction to the Apache Hadoop Big Data ecosystem. The
███ tutorial includes background information and explains the
███ core components of Hadoop, including Hadoop Distributed File
███ Systems (HDFS), MapReduce, the YARN resource manager, and
███ YARN Frameworks. In addition, it demonstrates how to use
███ Hadoop at several levels, including the native Java
███ interface, C++ pipes, and the universal streaming program
███ interface. Examples include how to use benchmarks and high-
███ level tools, including the Apache Pig scripting language,
███ Apache Hive "SQL-like" interface, Apache Flume for streaming
███ input, Apache Sqoop for import and export of relational
███ data, and Apache Oozie for Hadoop workflow management. In
███ addition, there is comprehensive coverage of Spark, PySpark,
███ and the Zeppelin web-GUI. The steps for easily installing a
███ working Hadoop/Spark system on a desktop/laptop and on a
███ local stand-alone cluster using the powerful Ambari GUI are
███ also included. All software used in these LiveLessons is
███ open source and freely available for your use and
███ experimentation. A bonus lesson includes a quick primer on
███ the Linux command line as used with Hadoop and Spark. About
███ the Instructor Douglas Eadline, PhD, began his career as a
███ practitioner and a chronicler of the Linux cluster HPC
███ revolution and now documents big data analytics. Starting
███ with the first Beowulf Cluster how-to document, Doug has
███ written hundreds of articles, white papers, and
███ instructional documents covering High Performance Computing
███ (HPC) and Data Analytics. Prior to starting and editing the
███ popular ClusterMonkey.net website in 2005, he served as
███ editor-in-chief for ClusterWorld Magazine, and was senior
███ HPC editor for Linux Magazine. Currently, he is a writer and
███ consultant to the HPC/Data Analytics industry and leader of
███ the Limulus Personal Cluster Project. He is author of Hadoop
███ Fundamentals LiveLessons and Apache Hadoop YARN Fundamentals
███ LiveLessons videos from Pearson, and book coauthor of Apache
███ Hadoop YARN: Moving beyond MapReduce and Batch Processing
███ with Apache Hadoop 2 and Practical Data Science with Hadoop
███ and Spark: Designing and Building Effective Analytics at
███ Scale. He is also the sole author of Hadoop 2 Quick Start
███ Guide: Learn the Essentials of Big Data Computing in the
███ Apache Hadoop 2 Ecosystem. Skill Level Beginner Intermediate
███ Learn How To Understand Hadoop design and key components How
███ the MapReduce process works in Hadoop Understand the
███ relationship of Spark and Hadoop Key aspects of the new YARN
███ design and Frameworks Use, administer, and program HDFS Run
███ and administer Hadoop/Spark programs Write basic
███ MapReduce/Spark programs Install Hadoop/Spark on a
███ laptop/desktop Run Apache Pig, Hive, Flume, Sqoop, Oozie,
███ Spark applications Perform basic data Ingest with Hive and
███ Spark Use the Zeppelin web-GUI for Spark/Hive programing
███ Install and administer Hadoop with the Apache Ambari GUI
███ tool Who Should Take This Course Users, developers, and
███ administrators interested in learning the fundamental
███ aspects and operations of the open source Hadoop and Spark
███ ecosystems Course Requirements Basic understanding of
███ programming and development A working knowledge of Linux
███ systems and tools Familiarity with Bash, Python, Java, and
███ C++ Lesson 1: Background Concepts This lesson introduces
███ Hadoop and Spark along with the many aspects and features
███ that enable the analysis of large unstructured data sets.
███ Many of these discussions about Hadoop ignore the
███ fundamental change Hadoop brings to data management. Doug
███ explains this key point using the data lake metaphor, and
███ then provides background on how the Hadoop data platform,
███ MapReduce, and Spark fit into the data analytics landscape.
███ A bonus lesson is also included for new Linux users that
███ provides the basics of the command line interface used
███ throughout these lessons. Lesson 2: Running Hadoop on a
███ Desktop or Laptop A real Hadoop installation, whether it be
███ a local cluster or in the cloud, can be difficult to
███ configure and possibly an expensive proposition. In order to
███ make the examples of this tutorial more accessible, you
███ learn how to install the Hortonworks HDP Sandbox on a
███ desktop or laptop. The "Sandbox" is a freely available
███ Hadoop virtual machine that provides a full Hadoop
███ environment (including Spark). You can use this environment
███ to try most of the examples in this tutorial. If you would
███ rather learn about Hadoop and Spark installation details, we
███ will also do a direct single (Linux) machine install using
███ the latest Hadoop and Spark binary code. Lesson 3: The
███ Hadoop Distributed File System The backbone of Hadoop is the
███ Hadoop Distributed File System or HDFS. In this lesson you
███ learn the basics of HDFS and how it is different from many
███ standard file systems used today. In particular, Doug
███ explains why various design trade-offs provide HDFS with a
███ performance edge in big data applications. You also learn
███ how to navigate HDFS using the Hadoop tools and how to use
███ HDFS in user programs. Finally, I present some of the new
███ features available in HDFS including high availability,
███ federation, snapshots, and NFS access. Lesson 4: Hadoop
███ MapReduce If the Hadoop Distributed File System is the
███ backbone of Hadoop, then MapReduce is the muscle that
███ operates on big data. In this lesson, Doug shows you how
███ MapReduce compares to a traditional search approach. From
███ there, he shows you how to compile and run a Java MapReduce
███ application. Deeper background on how MapReduce works is
███ presented along with how to use MapReduce with other
███ languages and how to do simple debugging of a MapReduce
███ program. Lesson 5: Hadoop MapReduce Examples This lesson
███ continues with MapReduce examples. Doug first shows you a
███ multifile word count program, and then moves on to a more
███ practical log file analysis. From there, he demonstrates how
███ to use a really large text file, like Wikipedia. The lesson
███ concludes with some examples of running MapReduce benchmarks
███ and the using the YARN job browser. Lesson 6: Higher Level
███ Tools While Hadoop is very effective at presenting a basic
███ scalable MapReduce model, some higher-level approaches have
███ been developed. In this lesson, Doug teaches you how to use
███ Apache Pigé─∞a Hadoop scripting language that simplifies
███ using MapReduce. In addition, he shows you how to use Apache
███ Hive QLé─∞an SQL-like language that enables higher-level "ad
███ hoc" queries using MapReduce and HDFS. And finally, the
███ Oozie workflow manager is presented. Lesson 7: Using the
███ Spark Language Spark has become a popular tool for data
███ analytics. In this lesson, Doug provides some of the basic
███ aspects of the Spark language and demonstrates the Python-
███ Spark interface, PySpark, with a simple command line
███ example. Additional aspects of the Spark language are also
███ used in the next two lessons. Lesson 8: Getting Data into
███ Hadoop HDFS The first, and often overlooked step in data
███ analytics, is "data ingest." As was demonstrated in Lesson
███ 3, files can be simply copied into HDFS. However, there are
███ methods that can preserve and import structure that could be
███ lost with simple copying. In this lesson. Doug demonstrates
███ how to import data into Hive tables and use Spark to import
███ data into HDFS. He also demonstrates importing log and other
███ streaming data directly into HDFS using Apache Flume.
███ Finally, a complete example of using Apache Sqoop to import
███ and export a relational database into and out of HDFS is
███ presented. Lesson 9: Using the Zeppelin Web Interface
███ Although much of the early Hadoop applications were
███ developed using the command line interface, new web-based
███ GUI tools such as Apache Zeppelin offer a more user-friendly
███ approach to application development. In this lesson, a walk-
███ through of the Zeppelin interface is provided and includes
███ an example of how to create an interactive Zeppelin notebook
███ for a simple Spark application. Lesson 10: Learning Basic
███ Hadoop Installation and Administration One of the challenges
███ facing Hadoop users and administrators is setting up a real
███ cluster for production use. In this lesson, Doug teaches you
███ how to use the Ambari web GUI to install, monitor, and
███ administer a full Hadoop installation. He also provides a
███ few important command line tools that will help with basic
███ administration. Finally, some additional HDFS features such
███ as snapshots and NFSv3 mounts are demonstrated. About
███ Pearson Video Training Pearson publishes expert-led video
███ tutorials covering a wide selection of technology topics
███ designed to teach you the skills you need to succeed. These
███ professional and personal technology videos feature world-
███ leading author instructors published by your trusted
███ technology brands: Addison-Wesley, Cisco Press, Pearson IT
███ Certification, Prentice Hall, Sams, and Que Topics include:
███ IT Certification, Network Security, Cisco Technology,
███ Programming, Web Development, Mobile Development, and more.
███ Learn more about Pearson Video training at
███ http://www.informit.com/video.
███
███
███ More info:
███ https://www.informit.com/
███
███ Install notes:
███
███ Unrar, Learn and Enjoy!
███
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███ in memory of ▀ ▄█▀▀ ▄▀ drawn by the honored
███ THEPROF ▀ FX/SAC
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