University of California San Diego
Hadoop Platform and Application Framework
University of California San Diego

Hadoop Platform and Application Framework

Taught in English

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148,794 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Natasha Balac, Ph.D.
Paul Rodriguez
Andrea Zonca

Instructors: Natasha Balac, Ph.D.

4.0

(3,318 reviews)

25 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace

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Assessments

11 quizzes

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There are 5 modules in this course

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.

What's included

7 videos4 readings1 quiz

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.

What's included

10 videos6 readings3 quizzes

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.

What's included

9 videos5 readings3 quizzes

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.

What's included

9 videos3 readings1 quiz2 programming assignments

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.

What's included

10 videos4 readings3 quizzes2 programming assignments

Instructors

Instructor ratings
3.8 (92 ratings)
Natasha Balac, Ph.D.
University of California San Diego
4 Courses210,468 learners
Paul Rodriguez
University of California San Diego
3 Courses181,880 learners

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