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MANNING | The Ultimate Introduction to Big Data [FCO] TGx Exclusive

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English

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4.57 GB

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File	Size
01 - Introduction, and install Hadoop on your desktop!.mp4	236.41 MB
02 - Hadoop overview and history.mp4	107.71 MB
03 - Overview of the Hadoop ecosystem.mp4	90.61 MB
04 - Tips for using this course.mp4	28.03 MB
05 - HDFS - what it is and how it works.mp4	49.12 MB
06 - Install the MovieLens dataset into HDFS using the Ambari UI.mp4	47.81 MB
07 - Install the MovieLens dataset into HDFS using the command line.mp4	52.43 MB
08 - MapReduce - what it is and how it works.mp4	31.37 MB
09 - How MapReduce distributes processing.mp4	47.57 MB
10 - MapReduce example - break down movie ratings by rating score.mp4	40.07 MB
11 - Installing Python, MRJob, and nano.mp4	40.97 MB
12 - Code up the ratings histogram MapReduce job and run it.mp4	28.48 MB
13 - Exercise - Rank movies by their popularity.mp4	21.03 MB
14 - Check your results against mine!.mp4	49.14 MB
15 - Introducing Ambari.mp4	39.35 MB
16 - Introducing Pig.mp4	34.36 MB
17 - Find the oldest movie with a 5-star rating using Pig.mp4	61.22 MB
18 - Find old 5-star movies with Pig.mp4	52.17 MB
19 - More Pig Latin.mp4	20.33 MB
20 - Exercise - Find the most-rated, one-star movie.mp4	19.47 MB
21 - Compare your results to mine!.mp4	39.18 MB
22 - Why Spark.mp4	31.55 MB
23 - The Resilient Distributed Dataset (RDD).mp4	20.58 MB
24 - Find the movie with the lowest average rating with RDDs.mp4	94.68 MB
25 - Datasets and Spark 2.0.mp4	17.39 MB
26 - Find the movie with the lowest average rating wth DataFrames.mp4	65.13 MB
27 - Movie recommendations with MLLib.mp4	90.08 MB
28 - Exercise - Filter the lowest-rated movies by number of ratings.mp4	37.33 MB
29 - Check your results against mine!.mp4	58.88 MB
30 - What is Hive.mp4	33.99 MB
31 - Use Hive to find the most popular movie.mp4	35.25 MB
32 - How Hive works.mp4	26.48 MB
33 - Exercise - Use Hive to find the movie with the highest average rating.mp4	24.24 MB
34 - Compare your solution to mine.mp4	13.89 MB
35 - Integrating MySQL with Hadoop.mp4	23.04 MB
36 - Install MySQL and import our movie data.mp4	48.38 MB
37 - Use Sqoop to import data from MySQL to HDFS_Hive.mp4	42.67 MB
38 - Use Sqoop to export data from Hadoop to MySQL.mp4	47.43 MB
39 - Why NoSQL.mp4	96.34 MB
40 - What is HBase.mp4	36.43 MB
41 - Import movie ratings into HBase.mp4	61.43 MB
42 - Use HBase with Pig to import data at scale.mp4	63.81 MB
43 - Cassandra overview.mp4	61.05 MB
44 - Installing Cassandra.mp4	95.90 MB
45 - Write Spark output into Cassandra.mp4	75.38 MB
46 - MongoDB overview.mp4	65.64 MB
47 - Install MongoDB and integrate it with Spark.mp4	84.60 MB
48 - Using the MongoDB shell.mp4	52.38 MB
49 - Choosing a database technology.mp4	91.35 MB
50 - Choose a database for a given problem.mp4	27.62 MB
51 - Overview of Drill.mp4	44.81 MB
52 - Setting up Drill.mp4	63.52 MB
53 - Querying across multiple databases.mp4	18.29 MB
54 - Overview of Phoenix.mp4	33.84 MB
55 - Install Phoenix and query HBase with it.mp4	45.87 MB
56 - Integrate Phoenix with Pig.mp4	79.84 MB
57 - Overview of Presto.mp4	37.29 MB
58 - Install Presto and query Hive with it.mp4	85.38 MB
59 - Query both Cassandra and Hive using Presto.mp4	69.74 MB
60 - YARN explained.mp4	45.62 MB
61 - Tez explained.mp4	14.02 MB
62 - Use Hive on Tez and measure the performance benefit.mp4	48.46 MB
63 - Mesos explained.mp4	41.22 MB
64 - ZooKeeper explained.mp4	38.45 MB
65 - Simulating a failing master with ZooKeeper.mp4	52.71 MB
66 - Oozie explained.mp4	41.96 MB
67 - Set up a simple Oozie workflow.mp4	82.45 MB
68 - Zeppelin overview.mp4	39.98 MB
69 - Use Zeppelin to analyze movie ratings, part 1.mp4	40.01 MB
70 - Use Zeppelin to analyze movie ratings, part 2.mp4	35.33 MB
71 - Hue overview.mp4	32.37 MB
72 - Other technologies worth mentioning.mp4	27.64 MB
73 - Kafka explained.mp4	46.29 MB
74 - Setting up Kafka and publishing some data.mp4	43.32 MB
75 - Publishing web logs with Kafka.mp4	77.74 MB
76 - Flume explained.mp4	25.81 MB
77 - Set up Flume and publish logs with it.mp4	40.12 MB
78 - Set up Flume to monitor a directory and store its data in HDFS.mp4	68.26 MB
79 - Spark Streaming - introduction.mp4	56.08 MB
80 - Analyze web logs published with Flume using Spark Streaming.mp4	89.24 MB
81 - Exercise - Monitor Flume-published logs for errors in real time.mp4	52.12 MB
82 - Solution - Aggregating HTTP access codes with Spark Streaming.mp4	30.60 MB
83 - Apache Storm - Introduction.mp4	28.83 MB
84 - Count words with Storm.mp4	87.42 MB
85 - Flink - an overview.mp4	22.37 MB
86 - Counting words with Flink.mp4	73.90 MB
87 - The best of the rest.mp4	28.72 MB
88 - Review - how the pieces fit together.mp4	30.99 MB
89 - Understanding your requirements.mp4	28.33 MB
90 - Sample application - consume webserver logs and keep track of top sellers.mp4	37.00 MB
91 - Sample application - serving movie recommendations to a website.mp4	35.57 MB
92 - Exercise - Design a system to report web sessions per day.mp4	7.44 MB
93 - Solution - Design a system to report daily sessions.mp4	31.47 MB
94 - Books and online resources.mp4	60.75 MB
Discuss.FTUForum.com.html	31.89 kB
FreeCoursesOnline.Me.html	108.30 kB
FTUForum.com.html	100.44 kB
How you can help Team-FTU.txt	235.00 B
NulledPremium.com.url	163.00 B
Torrent Downloaded From GloDls.to.txt	84.00 B

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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