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[FreeCoursesOnline.Me] [MANNING] The Ultimate Introduction to Big Data [FCO]

[FreeCoursesOnline.Me] [MANNING] The Ultimate Introduction to Big Data [FCO]

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Description
Author : Frank Kane
Publisher : Manning Publications
Released : April, 2019
Duration : 14h 29m
Language : English
Torrent Contains : 100 Files
Course Source : https://www.manning.com/livevideo/the-ultimate-introduction-to-big-data

Video Description

See it. Do it. Learn it! Businesses rely on data for decision-making, success, and survival. The volume of data companies can capture is growing every day, and big data platforms like Hadoop help store, manage, and analyze it. In The Ultimate Introduction to Big Data, big data guru Frank Kane introduces you to big data processing systems and shows you how they fit together. This liveVideo spotlights over 25 different technologies in over 14 hours of video instruction.

Distributed by Manning Publications

This course was created independently by big data expert Frank Kane and is distributed by Manning through our exclusive liveVideo platform.

About the subject

Designed for data storage and processing, Hadoop is a reliable, fault-tolerant operating system. The most celebrated features of this open source Apache project are HDFS, Hadoop’s highly-scalable distributed file system, and the MapReduce data processing engine. Together, they can process vast amounts of data across large clusters. An ecosystem of hundreds of technologies has sprung up around Hadoop to answer the ever-growing demand for large-scale data processing solutions. Understanding the architecture of massive-scale data processing applications is an increasingly important and desirable skill, and you’ll have it when you complete this liveVideo course!

About the video

The Ultimate Introduction to Big Data teaches you how to design powerful distributed data applications. With lots of hands-on exercises, instructor Frank Kane goes beyond Hadoop to cover many related technologies, giving you valuable firsthand experience with modern data processing applications. You’ll learn to choose an appropriate data storage technology for your application and discover how Hadoop clusters are managed by YARN, Tez, Mesos, and other technologies. You’ll also experience the combined power of HDFS and MapReduce for storing and analyzing data at scale.

Using other key parts of the Hadoop ecosystem like Hive and MySQL, you’ll analyze relational data, and then tackle non-relational data analysis using HBase, Cassandra, and MongoDB. With Kafka, Sqoop, and Flume, you’ll make short work of publishing data to your Hadoop cluster. When you’re done, you’ll have a deep understanding of data processing applications on Hadoop and its distributed systems.

Prerequisites

Suitable for software engineers, program managers, data analysts, database administrators, system architects, and everyone else with an interest in learning about Hadoop, its ecosystem, and how it relates to their work. Familiarity with the Linux command line would be helpful, along with some programming experience in Python or Scala.

What you will learn

• Using HDFS and MapReduce for storing and analyzing data at scale
• Analyzing relational data using Hive and MySQL
• Creating scripts to process data on a Hadoop cluster using Pig and Spark
• Using HBase, Cassandra, and MongoDB to analyze non-relational data
• Querying data interactively with Drill, Phoenix, and Presto
• Choosing an appropriate data storage technology for your application
• Understanding how Hadoop clusters are managed by YARN, Tez, Mesos, Zookeeper, Zeppelin, Hue, and Oozie
• Publishing data to your Hadoop cluster using Kafka, Sqoop, and Flume
• Consuming streaming data using Spark Streaming, Flink, and Storm

About the instructor

Frank Kane holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. He spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to millions of customers every day. Sundog Software, his own company specializing in virtual reality environment technology and teaching others about big data analysis, is his pride and joy.

Table of Contents

• LEARN ALL THE BUZZWORDS AND INSTALL HADOOP
• USING HADOOPS CORE: HDFS AND MAPREDUCE
• PROGRAMMING HADOOP WITH PIG
• PROGRAMMING HADOOP WITH SPARK
• USING RELATIONAL DATA STORES WITH HADOOP
• USING NON-RELATIONAL DATA STORES WITH HADOOP
• QUERYING YOUR DATA INTERACTIVELY
• MANAGING YOUR CLUSTER
• FEEDING DATA TO YOUR CLUSTER
• ANALYZING STREAMS OF DATA
• DESIGNING REAL-WORLD SYSTEMS
• LEARNING MORE

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File list
  • [FreeCoursesOnline.Me] [MANNING] The Ultimate Introduction to Big Data [FCO]
  • 01 - Introduction, and install Hadoop on your desktop!.mp4 236.4 MB
  • 02 - Hadoop overview and history.mp4 107.7 MB
  • 03 - Overview of the Hadoop ecosystem.mp4 90.6 MB
  • 04 - Tips for using this course.mp4 28 MB
  • 05 - HDFS - what it is and how it works.mp4 49.1 MB
  • 06 - Install the MovieLens dataset into HDFS using the Ambari UI.mp4 47.8 MB
  • 07 - Install the MovieLens dataset into HDFS using the command line.mp4 52.4 MB
  • 08 - MapReduce - what it is and how it works.mp4 31.4 MB
  • 09 - How MapReduce distributes processing.mp4 47.6 MB
  • 10 - MapReduce example - break down movie ratings by rating score.mp4 40.1 MB
  • 11 - Installing Python, MRJob, and nano.mp4 41 MB
  • 12 - Code up the ratings histogram MapReduce job and run it.mp4 28.5 MB
  • 13 - Exercise - Rank movies by their popularity.mp4 21 MB
  • 14 - Check your results against mine!.mp4 49.1 MB
  • 15 - Introducing Ambari.mp4 39.3 MB
  • 16 - Introducing Pig.mp4 34.4 MB
  • 17 - Find the oldest movie with a 5-star rating using Pig.mp4 61.2 MB
  • 18 - Find old 5-star movies with Pig.mp4 52.2 MB
  • 19 - More Pig Latin.mp4 20.3 MB
  • 20 - Exercise - Find the most-rated, one-star movie.mp4 19.5 MB
  • 21 - Compare your results to mine!.mp4 39.2 MB
  • 22 - Why Spark.mp4 31.6 MB
  • 23 - The Resilient Distributed Dataset (RDD).mp4 20.6 MB
  • 24 - Find the movie with the lowest average rating with RDDs.mp4 94.7 MB
  • 25 - Datasets and Spark 2.0.mp4 17.4 MB
  • 26 - Find the movie with the lowest average rating wth DataFrames.mp4 65.1 MB
  • 27 - Movie recommendations with MLLib.mp4 90.1 MB
  • 28 - Exercise - Filter the lowest-rated movies by number of ratings.mp4 37.3 MB
  • 29 - Check your results against mine!.mp4 58.9 MB
  • 30 - What is Hive.mp4 34 MB
  • 31 - Use Hive to find the most popular movie.mp4 35.3 MB
  • 32 - How Hive works.mp4 26.5 MB
  • 33 - Exercise - Use Hive to find the movie with the highest average rating.mp4 24.2 MB
  • 34 - Compare your solution to mine.mp4 13.9 MB
  • 35 - Integrating MySQL with Hadoop.mp4 23 MB
  • 36 - Install MySQL and import our movie data.mp4 48.4 MB
  • 37 - Use Sqoop to import data from MySQL to HDFS_Hive.mp4 42.7 MB
  • 38 - Use Sqoop to export data from Hadoop to MySQL.mp4 47.4 MB
  • 39 - Why NoSQL.mp4 96.3 MB
  • 40 - What is HBase.mp4 36.4 MB
  • 41 - Import movie ratings into HBase.mp4 61.4 MB
  • 42 - Use HBase with Pig to import data at scale.mp4 63.8 MB
  • 43 - Cassandra overview.mp4 61 MB
  • 44 - Installing Cassandra.mp4 95.9 MB
  • 45 - Write Spark output into Cassandra.mp4 75.4 MB
  • 46 - MongoDB overview.mp4 65.6 MB
  • 47 - Install MongoDB and integrate it with Spark.mp4 84.6 MB
  • 48 - Using the MongoDB shell.mp4 52.4 MB
  • 49 - Choosing a database technology.mp4 91.3 MB
  • 50 - Choose a database for a given problem.mp4 27.6 MB
  • 51 - Overview of Drill.mp4 44.8 MB
  • 52 - Setting up Drill.mp4 63.5 MB
  • 53 - Querying across multiple databases.mp4 18.3 MB
  • 54 - Overview of Phoenix.mp4 33.8 MB
  • 55 - Install Phoenix and query HBase with it.mp4 45.9 MB
  • 56 - Integrate Phoenix with Pig.mp4 79.8 MB
  • 57 - Overview of Presto.mp4 37.3 MB
  • 58 - Install Presto and query Hive with it.mp4 85.4 MB
  • 59 - Query both Cassandra and Hive using Presto.mp4 69.7 MB
  • 60 - YARN explained.mp4 45.6 MB
  • 61 - Tez explained.mp4 14 MB
  • 62 - Use Hive on Tez and measure the performance benefit.mp4 48.5 MB
  • 63 - Mesos explained.mp4 41.2 MB
  • 64 - ZooKeeper explained.mp4 38.4 MB
  • 65 - Simulating a failing master with ZooKeeper.mp4 52.7 MB
  • 66 - Oozie explained.mp4 42 MB
  • 67 - Set up a simple Oozie workflow.mp4 82.5 MB
  • 68 - Zeppelin overview.mp4 40 MB
  • 69 - Use Zeppelin to analyze movie ratings, part 1.mp4 40 MB
  • 70 - Use Zeppelin to analyze movie ratings, part 2.mp4 35.3 MB
  • 71 - Hue overview.mp4 32.4 MB
  • 72 - Other technologies worth mentioning.mp4 27.6 MB
  • 73 - Kafka explained.mp4 46.3 MB
  • 74 - Setting up Kafka and publishing some data.mp4 43.3 MB
  • 75 - Publishing web logs with Kafka.mp4 77.7 MB
  • 76 - Flume explained.mp4 25.8 MB
  • 77 - Set up Flume and publish logs with it.mp4 40.1 MB
  • 78 - Set up Flume to monitor a directory and store its data in HDFS.mp4 68.3 MB
  • 79 - Spark Streaming - introduction.mp4 56.1 MB
  • 80 - Analyze web logs published with Flume using Spark Streaming.mp4 89.2 MB
  • 81 - Exercise - Monitor Flume-published logs for errors in real time.mp4 52.1 MB
  • 82 - Solution - Aggregating HTTP access codes with Spark Streaming.mp4 30.6 MB
  • 83 - Apache Storm - Introduction.mp4 28.8 MB
  • 84 - Count words with Storm.mp4 87.4 MB
  • 85 - Flink - an overview.mp4 22.4 MB
  • 86 - Counting words with Flink.mp4 73.9 MB
  • 87 - The best of the rest.mp4 28.7 MB
  • 88 - Review - how the pieces fit together.mp4 31 MB
  • 89 - Understanding your requirements.mp4 28.3 MB
  • 90 - Sample application - consume webserver logs and keep track of top sellers.mp4 37 MB
  • 91 - Sample application - serving movie recommendations to a website.mp4 35.6 MB
  • 92 - Exercise - Design a system to report web sessions per day.mp4 7.4 MB
  • 93 - Solution - Design a system to report daily sessions.mp4 31.5 MB
  • 94 - Books and online resources.mp4 60.8 MB
  • Discuss.FTUForum.com.html 31.9 KB
  • FreeCoursesOnline.Me.html 108.3 KB
  • FTUForum.com.html 100.4 KB
  • How you can help Team-FTU.txt 235 B

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