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Monday, October 1 • 10:00am - 3:00pm
All Day Training Class on "Hadoop Ecosystem" (Separate Registration is Required)

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One Day Intensive Hands-on Hadoop Training Class. Topics Covered Are:

Hadoop BI Developer Hadoop – Architecture, HDFS, EcoSystem & Map Reduce

Time: 10am – noon, 1pm – 4:30pm

 

Audience: Engineers, Programmers, Networking specialists, Managers, Executives

 

Software covered: HDFS, MapReduce, Pig, Hive, HBase

 

Labs: 5 labs, 20 mins each

 

Objectives:

  • Introduce students to the core concepts of Hadoop

  • Deep dive into the critical architecture paths of HDFS, MapReduce and HBase

  • Teach the basics of how to effectively write Pig and Hive scripts

  • Explain how to choose the correct use cases for Hadoop

  • Give each student access to an individual 1-node Hadoop cluster in Rackspace to run through some hands-on labs for the 5 software components: HDFS, MapReduce, Pig, Hive, HBase

  • Provide links to the best books, blog posts and videos for students to learn more about Hadoop on their own

 

Summary: This is a fast paced, vendor agnostic, technical overview of the Hadoop landscape. No prior knowledge of databases or programming is assumed. This survey course is targeted towards both technical and non-technical people who want to understand the emerging world of Big Data, with a specific focus on Hadoop. In each sub-topic, the instructor will provide links and resource recommendations for students who want to explore that area further (for example, YouTube videos, books, blog posts). Students will be given a ~100 page PDF slide deck which can be used as reference material after the course. PDFs will also be given out for the 5 short labs in the course.

Course structure:

 10am – 10:30am: Introduction to Big Data and Hadoop

 10:30am – 11:15am: HDFS Lecture

11:15am – 11:40am: HDFS lab

 11:40am – noon: MapReduce Introduction Lecture

 Noon – 1pm: Lunch

1pm – 1:20pm: MapReduce Advanced Lecture

1:20pm – 1:40pm: MapReduce Lab

1:40pm – 2pm: Pig Lecture

2pm – 2:20pm: Pig lab

2:20pm – 2:40pm: Hive Lecture

2:40pm – 3pm: Hive Lab

3pm – 3:40pm: HBase Lecture

3:40pm – 4pm – HBase Lab

4pm – 4:30pm: Next-gen Hadoop (2.0) Lecture

Session 1: Intro to Hadoop (10am to 10:30am)

  • Parallel Computer vs. Distributed Computing

  • Brief history of Hadoop

  • Scaling with Hadoop

  • Hadoop clusters at Yahoo! and Facebook

  • RDBMS/SQL vs. Hadoop

  • Hadoop Daemons introduction: NameNode, DataNode, JobTracker, TaskTracker

  • Intro to the Hadoop ecosystem: HDFS, MapReduce, Pig, Hive, HBase, ZooKeeper

  • Vendor Comparison (Cloudera vs. Hortonworks vs. Amazon EMR)

  • Hardware + Software recommendations for Hadoop

 

Session 2: HDFS (10:30am – 11:40am)

  • Linux File system options

  • Sample HDFS commands

  • HDFS sample architecture at Yahoo!

  • Data Locality

  • Rack Awareness

  • Write Pipeline

  • Read Pipeline

  • NameNode architecture (EditLog, FsImage, location of replicas, safe mode)

  • Secondary NameNode architecture

  • DataNode architecture

  • Heartbeats

  • Block Scanner

  • Fsck Health Check + file breakdown

  • Balancer

  • LAB #1: Exploring the HDFS cmd line

 

Session 3: MapReduce (11:40am to 1:40pm, minus one hour lunch)

  • MapReduce Architecture

  • JobTracker/TaskTracker

  • Combiner

  • Partitioner (shuffle)

  • Thinking in the MapReduce way (examples of Mappers & Reducers)

  • Counters

  • Hadoop Streaming (with python)

  • Hadoop Java example

  • Input/output formats

  • Speculative Execution

  • Distributed Cache

  • Job Scheduling (FIFO, Fair Scheduler, Capacity Scheduler)

  • LAB #2: Running MapReduce wordcount in Python & Java

 

Session 4: Pigs Eat Anything (1:40pm to 2:20pm)

  • Pig philosophy and architecture

  • Pig Latin and the Grunt shell

  • Loading data

  • Data types and schemas

  • Pig Latin details: structure, functions, expressions, relational operators

  • Intro to User Defined Functions and Scripts

  • LAB #3: Exploring Pig Latin commands

 

Session 5: Hive for Structured Data (2:20pm to 3pm)

  • Hive philosophy and architecture

  • Hive vs. RDBMS

  • HiveQL and Hive Shell

  • Managing tables

  • Data types and schemas

  • Querying data

  • LAB #4: Analyzing movie reviews with Hive

 

Session 6: Real-time I/O with HBase (3pm – 4pm)

  • HBase versions and origins

  • HBase architecture

  • HBase core concepts

  • HBase vs. RDBMS

  • HBase Master and Region Servers

  • Data Modeling

  • Column Families and Regions

  • HBase Internals: Bloom Filters and Block Indexes

  • Write Pipeline / Read Pipeline

  • Compactions

  • LAB #5: Intro to the HBase command line

 

Session 7: Next-gen Hadoop (4pm – 4:30pm)

  • HDFS improvements: HDFS Federation, NameNode HA, Snapshots

  • MapReduce improvements: YARN, Performance

 


Monday October 1, 2012 10:00am - 3:00pm
Redwood Room