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.
Plataforma Hadoop e framework de aplicação
oferecido por
120,212 já inscritos!
Universidade da Califórnia, San Diego
Informações sobre o curso
40,333
40,333Habilidades que você terá
Python ProgrammingApache HadoopMapreduceApache Spark
Programa - O que você aprenderá com este curso
Semana
12 horas para concluir
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 vídeos (total de (Total 53 mín.) min), 4 leituras, 1 teste
7 videos
The Apache Framework: Basic Modules3min
Hadoop Distributed File System (HDFS)5min
The Hadoop "Zoo"5min
Hadoop Ecosystem Major Components11min
Exploring the Cloudera VM: Hands-On Part 116min
Exploring the Cloudera VM: Hands-On Part 26min
4 leituras
Apache Hadoop Ecosystem10min
Lesson 1 Slides (PDF)10min
Hardware & Software Requirements10min
Lesson 2 Slides - Cloudera VM Tour10min
1 exercício prático
Basic Hadoop Stack20min
Semana
23 horas para concluir
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 vídeos (total de (Total 70 mín.) min), 6 leituras, 3 testes
10 videos
The Hadoop Distributed File System (HDFS) and HDFS28min
MapReduce Framework and YARN8min
The Hadoop Execution Environment4min
YARN, Tez, and Spark11min
Hadoop Resource Scheduling6min
Hadoop-Based Applications3min
Introduction to Apache Pig7min
Introduction to Apache HIVE7min
Introduction to Apache HBASE7min
6 leituras
Hadoop Basics - Lesson 1 Slides10min
Lesson 2: Hadoop Execution Environment - Slides10min
Lesson 3: Hadoop-Based Applications Overview - All Slides10min
Command list for Applications Slides10min
Tips to handle service connection errors10min
References for Applications10min
3 exercícios práticos
Overview of Hadoop Stack10min
Hadoop Execution Environment14min
Hadoop Applications12min
Semana
32 horas para concluir
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 vídeos (total de (Total 58 mín.) min), 5 leituras, 3 testes
9 videos
The HDFS Performance Envelope5min
Read/Write Processes in HDFS4min
HDFS Tuning Parameters6min
HDFS Performance and Robustness9min
Overview of HDFS Access, APIs, and Applications5min
HDFS Commands8min
Native Java API for HDFS4min
REST API for HDFS8min
5 leituras
Lesson 1: Introduction to HDFS - Slides10min
HDFS references10min
Lesson 2: HDFS Performance and Tuning - Slides10min
HDFS Access, APIs10min
Lesson 3: HDFS Access, APIs, Applications - Slides10min
3 exercícios práticos
HDFS Architecture12min
HDFS performance,tuning, and robustness10min
Accessing HDFS12min
Semana
47 horas para concluir
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 vídeos (total de (Total 27 mín.) min), 3 leituras, 3 testes
9 videos
The Map/Reduce Framework2min
A MapReduce Example: Wordcount in detail4min
MapReduce: Intro to Examples and Principles2min
MapReduce Example: Trending Wordcount1min
MapReduce Example: Joining Data4min
MapReduce Example: Vector Multiplication2min
Computational Costs of Vector Multiplication3min
MapReduce Summary2min
3 leituras
Lesson 1: Introduction to MapReduce - Slides10min
A note on debugging map/reduce programs.10min
Lesson 2: MapReduce Examples and Principles - Slides10min
1 exercício prático
Lesson 1 Review14min
Semana
58 horas para concluir
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 vídeos (total de (Total 70 mín.) min), 4 leituras, 5 testes
10 videos
Architecture of Spark7min
Resilient Distributed Datasets10min
Spark Transformations10min
Wide Transformations10min
Directed Acyclic Graph (DAG) Scheduler8min
Actions in Spark2min
Memory Caching in Spark5min
Broadcast Variables2min
Accumulators1min
4 leituras
Setup PySpark on the Cloudera VM10min
Lesson 1: Intro to Apache Spark - Slides10min
Lesson 2: RDD and Transformations - Slides10min
Lesson 3: Scheduling, Actions, Caching - Slides10min
3 exercícios práticos
Spark Lesson 112min
Spark Lesson 210min
Spark Lesson 312min
29%
comecei uma nova carreira após concluir estes cursos
24%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
I'm forced to give 5 stars. I don't want to have a certification on a poor quality course (another coursera mistake). This material needs tremendous amount of work to get finished and revised.
Super hands on introduction to key Hadoop components, such as Spark, Map Reduce, Hive, Pig, HBase, HDFS, YARN, Squoop and Flume.\n\nI can't wait to the next course on the specialization.
Instrutores
Natasha Balac
Director, Predictive Analytics Center of Excellence (PACE)San Diego Supercomputer Center
Paul Rodriguez
Research ProgrammerSan Diego Supercomputer Center (SDSC)
Andrea Zonca
HPC Applications SpecialistSan Diego Supercomputer Center (SDSC)
Sobre Universidade da Califórnia, 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.
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