Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
This presentation will give you Information about :
1. What is Hadoop,
2. History of Hadoop,
3. Building Blocks – Hadoop Eco-System,
4. Who is behind Hadoop?,
5. What Hadoop is good for and why it is Good?,
Introduction to the Hadoop Ecosystem with Hadoop 2.0 aka YARN (Java Serbia Ed...Uwe Printz
Talk held at the Java User Group on 05.09.2013 in Novi Sad, Serbia
Agenda:
- What is Big Data & Hadoop?
- Core Hadoop
- The Hadoop Ecosystem
- Use Cases
- What‘s next? Hadoop 2.0!
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
This document presents best practices for deploying and operating an identity management infrastructure. It builds on Hitachi ID’s years of experience in deploying password management and user provisioning into some of the largest and most complex organizations in the world.
The document is organized as follows:
• Overview: Defining Identity Management:
Some basic definitions that help clarify the subsequent material.
• Long Term Commitment:
Identity management is more accurately described as a change in the IT organization and business processes than a finite project. Deployment can reasonably be expected to continue indefinitely, with more features and integrations are added over time.
• Focus on Business Drivers:
Given the long-term investment in identity management, it makes sense to identify and focus the highest priority business drivers first.
• Deliver Early and Often:
To minimize project risk and to ensure a positive return on investment, it is essential to deliver tangible results early in the project, and keep delivering new benefits regularly.
• Usability and Adoption:
Identity management is focused on the user – a human being represented on multiple IT systems, by a combination of identity attributes and privileges. It follows that user adoption is a prerequisite to success.
• Critical Path and Common nterdependencies:
Some integrations and features depend on others. This section identifies major interdependencies, which impact project timelines.
• Project Management Methodology:
A typical methodology for delivering a given project milestone.
• Typical Timeline and Deliverables:
Pulling all of the above together, a sample project timeline is developed, step-by-step.
Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
This presentation will give you Information about :
1. What is Hadoop,
2. History of Hadoop,
3. Building Blocks – Hadoop Eco-System,
4. Who is behind Hadoop?,
5. What Hadoop is good for and why it is Good?,
Introduction to the Hadoop Ecosystem with Hadoop 2.0 aka YARN (Java Serbia Ed...Uwe Printz
Talk held at the Java User Group on 05.09.2013 in Novi Sad, Serbia
Agenda:
- What is Big Data & Hadoop?
- Core Hadoop
- The Hadoop Ecosystem
- Use Cases
- What‘s next? Hadoop 2.0!
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
This document presents best practices for deploying and operating an identity management infrastructure. It builds on Hitachi ID’s years of experience in deploying password management and user provisioning into some of the largest and most complex organizations in the world.
The document is organized as follows:
• Overview: Defining Identity Management:
Some basic definitions that help clarify the subsequent material.
• Long Term Commitment:
Identity management is more accurately described as a change in the IT organization and business processes than a finite project. Deployment can reasonably be expected to continue indefinitely, with more features and integrations are added over time.
• Focus on Business Drivers:
Given the long-term investment in identity management, it makes sense to identify and focus the highest priority business drivers first.
• Deliver Early and Often:
To minimize project risk and to ensure a positive return on investment, it is essential to deliver tangible results early in the project, and keep delivering new benefits regularly.
• Usability and Adoption:
Identity management is focused on the user – a human being represented on multiple IT systems, by a combination of identity attributes and privileges. It follows that user adoption is a prerequisite to success.
• Critical Path and Common nterdependencies:
Some integrations and features depend on others. This section identifies major interdependencies, which impact project timelines.
• Project Management Methodology:
A typical methodology for delivering a given project milestone.
• Typical Timeline and Deliverables:
Pulling all of the above together, a sample project timeline is developed, step-by-step.
Do you know what the arrow on a NYC Parking Sign means?Lawrence Berezin
Joe got a NYC parking ticket, but the arrow didn't appear to point to Joe's parking space. Is there a defense to this unjust parking ticket? Yes...the "Arrow" defense. Here are the exhibits Joe submitted to support his defense that the arrow was pointing in the wrong direction.
What are Software Defined Application ServicesLori MacVittie
Application services are those network services that reside at layers 4-7 of the network. They include availability, performance, security, mobility and identity and access control. But just what are "software-defined" application services?
Hadoop Reporting and Analysis - JaspersoftHortonworks
Hadoop is deployed for a variety of uses, including web analytics, fraud detection, security monitoring, healthcare, environmental analysis, social media monitoring, and other purposes.
This is a power point presentation on Hadoop and Big Data. This covers the essential knowledge one should have when stepping into the world of Big Data.
This course is available on hadoop-skills.com for free!
This course builds a basic fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
• This course builds Understanding of Big Data problems with easy to understand examples and illustrations.
• History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop and was called Nutch
• What is Hadoop Magic which makes it so unique and powerful.
• Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
• And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
This course is available for free on hadoop-skills.com
Top 10 military interview questions with answersjasminewhite302
In this file, you can ref interview materials for military such as, military situational interview, military behavioral interview, military phone interview, military interview thank you letter, military interview tips …
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
What is Hadoop | Introduction to Hadoop | Hadoop Tutorial | Hadoop Training |...Edureka!
This Edureka "What is Hadoop" Tutorial (check our hadoop blog series here: https://goo.gl/lQKjL8) will help you understand all the basics of Hadoop. Learn about the differences in traditional and hadoop way of storing and processing data in detail. Below are the topics covered in this tutorial:
1) Traditional Way of Processing - SEARS
2) Big Data Growth Drivers
3) Problem Associated with Big Data
4) Hadoop: Solution to Big Data Problem
5) What is Hadoop?
6) HDFS
7) MapReduce
8) Hadoop Ecosystem
9) Demo: Hadoop Case Study - Orbitz
Subscribe to our channel to get updates.
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Hopsworks in the cloud Berlin Buzzwords 2019 Jim Dowling
This talk, given at Berlin Buzzwords 2019, describes the recent progress in making Hopsworks a cloud-native platform, with HA data-center support added for HopsFS.
Hadoop simplifies your job as a Data Warehousing professional. With Hadoop, you can manage any volume, variety and velocity of data, flawlessly and comparably in less time. As a Data Warehousing professional, you will undoubtedly have troubleshooting and data processing skills. These skills are sufficient for you to be a proficient Hadoop-er.
Key Questions Answered
What is Big Data and Hadoop?
What are the limitations of current Data Warehouse solutions?
How Hadoop solves these problems?
Real World Hadoop Use-Case in Data Warehouse Solutions?
Forrester predicts, CIOs who are late to the Hadoop game will finally make the platform a priority in 2015. Hadoop has evolved as a must-to-know technology and has been a reason for better career, salary and job opportunities for many professionals.
Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Ha...Edureka!
This Edureka "Hadoop tutorial For Beginners" ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand the problem with traditional system while processing Big Data and how Hadoop solves it. This tutorial will provide you a comprehensive idea about HDFS and YARN along with their architecture that has been explained in a very simple manner using examples and practical demonstration. At the end, you will get to know how to analyze Olympic data set using Hadoop and gain useful insights.
Below are the topics covered in this tutorial:
1. Big Data Growth Drivers
2. What is Big Data?
3. Hadoop Introduction
4. Hadoop Master/Slave Architecture
5. Hadoop Core Components
6. HDFS Data Blocks
7. HDFS Read/Write Mechanism
8. What is MapReduce
9. MapReduce Program
10. MapReduce Job Workflow
11. Hadoop Ecosystem
12. Hadoop Use Case: Analyzing Olympic Dataset
What to learn during the 21 days Lockdown | EdurekaEdureka!
Register Here: https://resources.edureka.co/21-days-learning-plan-webinar/
In light of the complete national lockdown for 21 days, we invite you to join a FREE webinar by renowned Mentor and Advisor, Nitin Gupta as he helps you create a 21-day learning gameplan to maximize returns for your career.
The webinar will help freshers and experienced professionals to capitalize on these 21 days and figure out the best technologies to learn while confined to home.
You will also get all your questions and doubts resolved in real-time.
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Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | EdurekaEdureka!
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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YouTube Link:https://youtu.be/CVv8zhYEjUE
Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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YouTube Link:https://youtu.be/LvgqSMlIXFs
Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| EdurekaEdureka!
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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YouTube Link: https://youtu.be/R132INtDg9k
** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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3. Slide 3 www.edureka.co/big-data-and-hadoop
At the end of this session , you will understand the:
→ Big Data Learning Paths
→ Big Data Introduction
→ Hadoop and Its Eco-System
→ Hadoop Architecture
→ Next Step on How to Setup Hadoop
4. Slide 4 www.edureka.co/big-data-and-hadoop
• Java / Python / Ruby
• Hadoop Eco-system
• NoSQL DB
• Spark
• Linux Administration
• Cluster Management
• Cluster Performance
• Virtualization
• Statistics Skills
• Machine Learning
• Hadoop Essentials
• Expertise in R
Developer/Testing
Administration
Data Analyst
Big Data and Hadoop
MapReduce
Design Patterns
Apache
Spark & Scala
Apache Cassandra
Linux Administration Hadoop Administration
Data Science
Business Analytics
Using R
Advance Predictive
Modelling in R
Talend for Big Data
Data Visualization
Using Tableau
5. Slide 5 www.edureka.co/big-data-and-hadoop
→ Lots of Data (Terabytes or Petabytes)
→ Big data is the term for a collection of data sets so
large and complex that it becomes difficult to process
using on-hand database management tools or
traditional data processing applications
→ The challenges include capture, curation, storage,
search, sharing, transfer, analysis, and visualization Big Data
6. Slide 6 www.edureka.co/big-data-and-hadoop
→ Systems / Enterprises generate huge amount of data from Terabytes to Petabytes of information
Stock market generates about one terabyte of new trade data per day to
perform stock trading analytics to determine trends for optimal trades
7. Slide 7 www.edureka.co/big-data-and-hadoop
→ By 2020, IDC (International Data Corporation) predicts the number will have reached 40,000 EB, or 40 Zettabytes (ZB)
→ The world’s information is doubling every two years. By 2020 the world will generate 50 times the amount of
information and 75 times the number of information containers
8. Slide 8 www.edureka.co/big-data-and-hadoop
IBM’s Definition – Big Data Characteristics
http://www-01.ibm.com/software/data/bigdata/
VOLUME
Web
logs
Images
Videos
Audios
Sensor
Data
VARIET
Y
VELOCITY VERACITY
Min Max Mean SD
4.3 7.9 5.84 0.83
2.0 4.4 3.05 0.43
0.1 2.5 1.20 0.76
10. Slide 10 www.edureka.co/big-data-and-hadoop
Map the following to corresponding data type:
» XML files, e-mail body
» Audio, Video, Images, Archived documents
» Data from Enterprise systems (ERP, CRM etc.)
11. Slide 11 www.edureka.co/big-data-and-hadoop
Ans. XML files, e-mail body → Semi-structured data
Audio, Video, Image, Files, Archived documents → Unstructured data
Data from Enterprise systems (ERP, CRM etc.) → Structured data
12. Slide 12 www.edureka.co/big-data-and-hadoop
More on Big Data
• http://www.edureka.in/blog/the-hype-behind-big-data/
Why Hadoop?
• http://www.edureka.in/blog/why-hadoop/
Opportunities in Hadoop
• http://www.edureka.in/blog/jobs-in-hadoop/
Big Data
• http://en.wikipedia.org/wiki/Big_Data
IBM’s definition – Big Data Characteristics
• http://www-01.ibm.com/software/data/bigdata/
13. Slide 13Slide 13Slide 13 www.edureka.co/big-data-and-hadoop
→ Web and e-tailing
» Recommendation Engines
» Ad Targeting
» Search Quality
» Abuse and Click Fraud Detection
→ Telecommunications
» Customer Churn Prevention
» Network Performance Optimization
» Calling Data Record (CDR) Analysis
» Analysing Network to Predict Failure
http://wiki.apache.org/hadoop/PoweredBy
14. Slide 14Slide 14Slide 14 www.edureka.co/big-data-and-hadoop
→ Government
» Fraud Detection and Cyber Security
» Welfare Schemes
» Justice
→ Healthcare and Life Sciences
» Health Information Exchange
» Gene Sequencing
» Serialization
» Healthcare Service Quality Improvements
» Drug Safety
http://wiki.apache.org/hadoop/PoweredBy
16. Slide 16Slide 16Slide 16 www.edureka.co/big-data-and-hadoop
→ Insight into data can provide Business Advantage.
→ Some key early indicators can mean Fortunes to Business.
→ More Precise Analysis with more data.
*Sears was using traditional systems such as Oracle Exadata, Teradata and SAS etc., to store and process the customer activity and sales data.
Case Study: Sears Holding Corporation
17. Slide 17Slide 17Slide 17 www.edureka.co/big-data-and-hadoop
Mostly Append
BI Reports + Interactive Apps
RDBMS (Aggregated Data)
ETL Compute Grid
Storage only Grid (Original Raw Data)
Collection
Inctrumentation
A meagre
10% of the
~2PB data is
available for
BI
Storage
2. Moving data to compute doesn’
t scale
90% of
the ~2PB
archived
Processing
3. Premature data
death
1. Can’t explore original
high fidelity raw data
18. Slide 18Slide 18Slide 18 www.edureka.co/big-data-and-hadoop
Mostly Append
BI Reports + Interactive Apps
RDBMS (Aggregated Data)
Hadoop : Storage + Compute Grid
Collection
Instrumentation
Both
Storage
And
Processing
Entire ~2PB
Data is
available for
processing
No Data
Archiving
1. Data Exploration &
Advanced analytics
2. Scalable throughput for ETL &
aggregation
3. Keep data alive
forever
*Sears moved to a 300-Node Hadoop cluster to keep 100% of its data available for processing rather than a meagre 10% as
was the case with existing Non-Hadoop solutions.
19. Slide 19Slide 19Slide 19 www.edureka.co/big-data-and-hadoop
Read 1 TB Data
4 I/O Channels
Each Channel – 100 MB/s
1 Machine
4 I/O Channels
Each Channel – 100 MB/s
10 Machine
22. Slide 22Slide 22Slide 22 www.edureka.co/big-data-and-hadoop
→ Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters
of commodity computers using a simple programming model.
→ It is an Open-source Data Management with scale-out storage and distributed processing.
24. Slide 24 www.edureka.co/big-data-and-hadoop
Ans. Large Data Sets.
It is also capable of processing small data-sets. However, to
experience the true power of Hadoop, one needs to have
data in TB’s. Because this is where RDBMS takes hours and
fails whereas Hadoop does the same in couple of minutes.
25. Slide 25Slide 25Slide 25 www.edureka.co/big-data-and-hadoop
Pig Latin
Data Analysis
Hive
DW System
Other YARN
Frameworks
(MPI, GRAPH)
HBaseMapReduce Framework
YARN
Cluster Resource Management
Apache Oozie
(Workflow)
HDFS
(Hadoop Distributed File System)
Hadoop 2.0
Sqoop
Unstructured or
Semi-structured Data Structured Data
Flume
Mahout
Machine Learning
30. Slide 30 www.edureka.co/big-data-and-hadoop
Namenode NS
Storage
…
NamespaceBlockStorage
Namespace
NN-1 NN-k NN-n
Common Storage
BlockStorage
Pool 1 Pool k Pool n
Block Pools
… …
Hadoop 1.0 Hadoop 2.0
DatanodeDatanode
Datanode 1
…
Datanode m
…
Datanode 2
…
Block Management
31. Slide 31 www.edureka.co/big-data-and-hadoop
How does HDFS Federation help HDFS Scale horizontally?
a. Reduces the load on any single NameNode by using the multiple,
independent NameNode to manage individual parts of the file system
namespace.
b. Provides cross-data centre (non-local) support for HDFS, allowing a
cluster administrator to split the Block Storage outside the local cluster.
32. Slide 32 www.edureka.co/big-data-and-hadoop
Ans. Option (a)
In order to scale the name service horizontally, HDFS federation
uses multiple independent NameNode. The NameNode are
federated, that is, the NameNode are independent and don’t
require coordination with each other.
33. Slide 33 www.edureka.co/big-data-and-hadoop
You have configured two name nodes to manage /marketing and
/finance respectively. What will happen if you try to put a file in
/accounting directory?
35. Slide 35 www.edureka.co/big-data-and-hadoop
Node Manager
Container
App
Master
Node Manager
Container
App
Master
HDFS YARN
Resource
Manager
All name space edits
logged to shared NFS
storage; single writer
(fencing)
Read edit logs and
applies to its own
namespace
Secondary
Name Node
DataNode
Standby
NameNode
Active
NameNode
DataNode Data Node
DataNodeDataNode
NameNode
High
Availability
Next Generation
MapReduce
*Not necessary to
configure
Secondary
NameNode
Client
Shared Edit Logs
HDFS HIGH AVAILABILITY
Node Manager
Container
App
Master
Node Manager
Container
App
Master
36. Slide 36 www.edureka.co/big-data-and-hadoop
Node Manager
Container
App
Master
Node Manager
Container
App
Master
HDFS YARN
Resource
Manager
All name space edits
logged to shared NFS
storage; single writer
(fencing)
Read edit logs and
applies to its own
namespace
Secondary
Name Node
DataNode
Standby
NameNode
Active
NameNode
DataNode Data Node
DataNodeDataNode
NameNode
High
Availability
Next Generation
MapReduce
*Not necessary to
configure
Secondary
NameNode
Client
Shared Edit Logs
HDFS HIGH AVAILABILITY
Node Manager
Container
App
Master
Node Manager
Container
App
Master
37. Slide 37 www.edureka.co/big-data-and-hadoop
HDFS HA was developed to overcome the following disadvantage in Hadoop
1.0?
a. Single Point of Failure of Name-Node
b. Only one version can be run in classic Map-Reduce
c. Too much burden on Job Tracker
40. Slide 40 www.edureka.co/big-data-and-hadoop
Facebook
→ We use Hadoop to store copies of internal log and dimension data sources and use
it as a source for reporting/analytics and machine learning.
→ Currently we have 2 major clusters:
» A 1100-machine cluster with 8800 cores and about 12 PB raw storage.
» A 300-machine cluster with 2400 cores and about 3 PB raw storage.
» Each (commodity) node has 8 cores and 12 TB of storage.
» We are heavy users of both streaming as well as the Java APIs. We have built
a higher level data warehousing framework using these features called Hive
(see the http://Hadoop.apache.org/hive/). We have also developed a FUSE
implementation over HDFS.
41. Slide 41 www.edureka.co/big-data-and-hadoop
Hadoop can run in any of the following three modes:
Fully-Distributed Mode
Pseudo-Distributed Mode
→ No daemons, everything runs in a single JVM.
→ Suitable for running MapReduce programs during development.
→ Has no DFS.
→ Hadoop daemons run on the local machine.
→ Hadoop daemons run on a cluster of machines.
Standalone (or Local) Mode
43. Slide 43 www.edureka.co/big-data-and-hadoop
• Referring the documents present in the LMS under assignment solve the below problem.
How many such DataNodes you would need to read 100TB data in 5 minutes in your Hadoop Cluster?
44. Slide 44
Your feedback is important to us, be it a compliment, a suggestion or a complaint. It helps us to make
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