Hadoop Platform and Application Framework

About this course: This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment. In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data. You'll feel empowered to have conversations about big data and the data analysis process.

Created by:  University of California, San Diego

• Taught by:  Natasha Balac, Director, Predictive Analytics Center of Excellence (PACE)

  San Diego Supercomputer Center

• Taught by:  Paul Rodriguez, Research Programmer

  San Diego Supercomputer Center (SDSC)

• Taught by:  Andrea Zonca, HPC Applications Specialist

  San Diego Supercomputer Center (SDSC)

Commitment 5 weeks of study, 1-2 hours/week Language English How To Pass Pass all graded assignments to complete the course. Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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University of California, San Diego UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.

Syllabus


WEEK 1


Hadoop Basics



Welcome to the first module of the Big Data Platform course. This first module will provide insight into Big Data Hype, its technologies opportunities and challenges. We will take a deeper look into the Hadoop stack and tool and technologies associated with Big Data solutions.


7 videos, 4 readings expand

1. Video: Hadoop Stack Basics
2. Video: The Apache Framework: Basic Modules
3. Video: Hadoop Distributed File System (HDFS)
4. Video: The Hadoop "Zoo"
5. Video: Hadoop Ecosystem Major Components
6. Reading: Apache Hadoop Ecosystem
7. Reading: Lesson 1 Slides (PDF)
8. Reading: Hardware & Software Requirements
9. Video: Exploring the Cloudera VM: Hands-On Part 1
10. Video: Exploring the Cloudera VM: Hands-On Part 2
11. Reading: Lesson 2 Slides - Cloudera VM Tour

Graded: Basic Hadoop Stack

WEEK 2


Introduction to the Hadoop Stack
In this module we will take a detailed look at the Hadoop stack ranging from the basic HDFS components, to application execution frameworks, and languages, services.


10 videos, 6 readings expand

1. Video: Overview of the Hadoop Stack
2. Video: The Hadoop Distributed File System (HDFS) and HDFS2
3. Video: MapReduce Framework and YARN
4. Reading: Hadoop Basics - Lesson 1 Slides
5. Video: The Hadoop Execution Environment
6. Video: YARN, Tez, and Spark
7. Video: Hadoop Resource Scheduling
8. Reading: Lesson 2: Hadoop Execution Environment - Slides
9. Video: Hadoop-Based Applications
10. Video: Introduction to Apache Pig
11. Video: Introduction to Apache HIVE
12. Video: Introduction to Apache HBASE
13. Reading: Lesson 3: Hadoop-Based Applications Overview - All Slides
14. Reading: Command list for Applications Slides
15. Reading: Tips to handle service connection errors
16. Reading: References for Applications

Graded: Overview of Hadoop Stack
Graded: Hadoop Execution Environment
Graded: Hadoop Applications

WEEK 3


Introduction to Hadoop Distributed File System (HDFS)



In this module we will take a detailed look at the Hadoop Distributed File System (HDFS). We will cover the main design goals of HDFS, understand the read/write process to HDFS, the main configuration parameters that can be tuned to control HDFS performance and robustness, and get an overview of the different ways you can access data on HDFS.


9 videos, 5 readings expand

1. Video: Overview of HDFS Architecture
2. Video: The HDFS Performance Envelope
3. Video: Read/Write Processes in HDFS
4. Reading: Lesson 1: Introduction to HDFS - Slides
5. Reading: HDFS references
6. Video: HDFS Tuning Parameters
7. Video: HDFS Performance and Robustness
8. Reading: Lesson 2: HDFS Performance and Tuning - Slides
9. Video: Overview of HDFS Access, APIs, and Applications
10. Video: HDFS Commands
11. Video: Native Java API for HDFS
12. Video: REST API for HDFS
13. Reading: HDFS Access, APIs
14. Reading: Lesson 3: HDFS Access, APIs, Applications - Slides

Graded: HDFS Architecture
Graded: HDFS performance,tuning, and robustness
Graded: Accessing HDFS

WEEK 4


Introduction to Map/Reduce



This module will introduce Map/Reduce concepts and practice. You will learn about the big idea of Map/Reduce and you will learn how to design, implement, and execute tasks in the map/reduce framework. You will also learn the trade-offs in map/reduce and how that motivates other tools.


9 videos, 3 readings expand

1. Video: Introduction to Map/Reduce
2. Video: The Map/Reduce Framework
3. Video: A MapReduce Example: Wordcount in detail
4. Reading: Lesson 1: Introduction to MapReduce - Slides
5. Reading: A note on debugging map/reduce programs.
6. Video: MapReduce: Intro to Examples and Principles
7. Video: MapReduce Example: Trending Wordcount
8. Video: MapReduce Example: Joining Data
9. Video: MapReduce Example: Vector Multiplication
10. Video: Computational Costs of Vector Multiplication
11. Video: MapReduce Summary
12. Reading: Lesson 2: MapReduce Examples and Principles - Slides

Graded: Running Wordcount with Hadoop streaming, using Python code
Graded: Lesson 1 Review
Graded: Joining Data

WEEK 5


Spark



Welcome to module 5, Introduction to Spark, this week we will focus on the Apache Spark cluster computing framework, an important contender of Hadoop MapReduce in the Big Data Arena. Spark provides great performance advantages over Hadoop MapReduce,especially for iterative algorithms, thanks to in-memory caching. Also, gives Data Scientists an easier way to write their analysis pipeline in Python and Scala,even providing interactive shells to play live with data.


10 videos, 4 readings expand

1. Video: Introduction to Apache Spark
2. Video: Architecture of Spark
3. Reading: Setup PySpark on the Cloudera VM
4. Reading: Lesson 1: Intro to Apache Spark - Slides
5. Video: Resilient Distributed Datasets
6. Video: Spark Transformations
7. Video: Wide Transformations
8. Reading: Lesson 2: RDD and Transformations - Slides
9. Video: Directed Acyclic Graph (DAG) Scheduler
10. Video: Actions in Spark
11. Video: Memory Caching in Spark
12. Video: Broadcast Variables
13. Video: Accumulators
14. Reading: Lesson 3: Scheduling, Actions, Caching - Slides

Graded: Spark Lesson 1
Graded: Spark Lesson 2
Graded: Simple Join in Spark
Graded: Spark Lesson 3
Graded: Advanced Join in Spark