The course "Big Data and Hadoop Foundations and Setup" offers a comprehensive introduction to the world of Big Data and Hadoop, providing foundational knowledge crucial for navigating modern data-driven environments. You’ll explore the limitations of traditional data processing technologies and understand how Hadoop addresses these challenges with its robust architecture and ecosystem.
Through detailed modules, you will gain a deep understanding of Big Data concepts, the role of Data Science and Big Data Analytics, and the trends shaping the Big Data revolution. The course demystifies Hadoop's subprojects and distributions, giving you the tools to differentiate between them and apply their features to real-world problems.
What sets this course apart is its hands-on approach. You'll install, configure, and run Hadoop in a Linux environment, building the technical proficiency needed to process large-scale data effectively. Whether you’re looking to enhance your career in Data Science or understand Big Data’s transformative impact on businesses, this course equips you with the skills to succeed.
This course provides an in-depth understanding of Big Data concepts, including Data Science and Big Data Analytics. It explores the limitations of traditional data processing and introduces modern technologies that support Big Data solutions. Learners will gain a comprehensive understanding of Hadoop architecture, its subprojects, and distributions. The course includes hands-on experience with installing, configuring, and running Hadoop in a Big Data environment. By the end of the course, learners will be equipped to leverage Hadoop for processing large-scale data.
涵盖的内容
2篇阅读材料
显示有关单元内容的信息
2篇阅读材料•总计15分钟
Course Overview•5分钟
Instructor Biography: Karthik Shyamsunder•10分钟
Big Data Revolution (Introduction)
第 2 单元•小时 后完成
单元详情
In this introductory module, we introduce the trends and concepts behind the Big Data revolution that is taking place in the Information Technology marketplace.
涵盖的内容
8个视频6篇阅读材料3个作业
显示有关单元内容的信息
8个视频•总计76分钟
Introduction - Big Data Revolution•2分钟
Data Explosion•10分钟
Data Economy•9分钟
Big Data Analytics•21分钟
Data Science•16分钟
Historical Data Processing Technologies•10分钟
Modern Data Processing Technologies•6分钟
Big Data Revolution Summary•1分钟
6篇阅读材料•总计75分钟
Data Explosion•15分钟
Data Economy•10分钟
Big Data Analytics•10分钟
Data Science•15分钟
Historical Data Processing Technologies•15分钟
Modern Data Processing Technologies•10分钟
3个作业•总计90分钟
Big Data Revolution (Introduction)•60分钟
The Big Data Landscape: Revolution, Explosion, Economy, and Science•15分钟
Evolution of Data Processing: From Historical to Modern Technologies•15分钟
Hadoop Architecture and Ecosystem
第 3 单元•小时 后完成
单元详情
This module provides a comprehensive overview of Apache Hadoop, exploring its architecture, history, and foundational principles. You will also delve into the Hadoop ecosystem, examining its technology stack, popular distributions, and key documentation to help you navigate and understand this powerful big data framework.
涵盖的内容
8个视频7篇阅读材料3个作业
显示有关单元内容的信息
8个视频•总计122分钟
Introduction - Apache Hadoop Architecture and Ecosystem•2分钟
Hadoop History•14分钟
Hadoop Architecture•36分钟
Key Principles behind Hadoop Architecture•13分钟
Hadoop Technology Stack•28分钟
Hadoop Distributions•15分钟
Hadoop Documentation•12分钟
Summary - Apache Hadoop Architecture and Ecosystem•1分钟
7篇阅读材料•总计88分钟
Hadoop History•10分钟
Hadoop Architecture•15分钟
Key Principles behind Hadoop Architecture•10分钟
Hadoop Technology Stack•10分钟
Hadoop Distributions•15分钟
Hadoop Documentation•8分钟
Self-Reflective Reading: Coexistence of Hadoop and Traditional Data Warehouses•20分钟
3个作业•总计90分钟
Hadoop Architecture and Ecosystem•60分钟
Exploring Hadoop: Architecture, History, and Key Principles•15分钟
Navigating Hadoop: Technology Stack, Distributions, and Documentation•15分钟
Setting up Hadoop
第 4 单元•小时 后完成
单元详情
In this module, you will learn how to set up a Hadoop environment on a Linux Virtual Machine. We will guide you through downloading, installing, configuring, and running Hadoop using VirtualBox and CentOS Linux. Additionally, you will explore the setup and configuration of Hadoop 3 YARN, concluding with a comprehensive summary of the process.
涵盖的内容
5个视频4篇阅读材料3个作业
显示有关单元内容的信息
5个视频•总计103分钟
Introduction - Setting Up and Running Hadoop•2分钟
Setting Up VirtualBox•10分钟
Setting Up CentOS Linux VM on VirtualBox•28分钟
Setting Up Hadoop 3 YARN•61分钟
Summary - Setting Up Hadoop•1分钟
4篇阅读材料•总计110分钟
Setting Up VirtualBox•10分钟
Setting Up CentOS Linux VM on VirtualBox•10分钟
Steps to Configure a Single Node Hadoop 3 YARN Cluster•50分钟
Self-Reflective Reading: Challenges and Solutions for Large-Scale Deployments•40分钟
3个作业•总计90分钟
Setting up Hadoop•60分钟
Getting Started with Hadoop: Setting Up VirtualBox and CentOS Linux•15分钟
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.