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[FreeCoursesOnline.Me] [MANNING] The Ultimate Introduction To Big Data [FCO]
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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
Filename
Size
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
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