This presentation will give you Information about :
1. Map/Reduce Overview and Architecture Installation
2. Developing Map/Red Jobs Input and Output Formats
3. Job Configuration Job Submission
4. Practicing Map Reduce Programs (atleast 10 Map Reduce
5. Algorithms )Data Flow Sources and Destinations
6. Data Flow Transformations Data Flow Paths
7. Custom Data Types
8. Input Formats
9. Output Formats
10. Partitioning Data
11. Reporting Custom Metrics
12. Distributing Auxiliary Job Data
This presentation will give you Information about :
1. Map/Reduce Overview and Architecture Installation
2. Developing Map/Red Jobs Input and Output Formats
3. Job Configuration Job Submission
4. Practicing Map Reduce Programs (atleast 10 Map Reduce
5. Algorithms )Data Flow Sources and Destinations
6. Data Flow Transformations Data Flow Paths
7. Custom Data Types
8. Input Formats
9. Output Formats
10. Partitioning Data
11. Reporting Custom Metrics
12. Distributing Auxiliary Job Data
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
Hadoop Interview Questions and Answers by rohit kapakapa rohit
Hadoop Interview Questions and Answers - More than 130 real time questions and answers covering hadoop hdfs,mapreduce and administrative concepts by rohit kapa
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
A guide to using Apache Hadoop as your open source big data platform of choice, including the vendors that make various Hadoop flavors, related open source tools, Hadoop capabilities and suitable applications.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
In this session you will learn:
1. Meet MapReduce
2. Word Count Algorithm – Traditional approach
3. Traditional approach on a Distributed System
4. Traditional approach – Drawbacks
5. MapReduce Approach
6. Input & Output Forms of a MR program
7. Map, Shuffle & Sort, Reduce Phase
8. WordCount Code walkthrough
9. Workflow & Transformation of Data
10. Input Split & HDFS Block
11. Relation between Split & Block
12. Data locality Optimization
13. Speculative Execution
14. MR Flow with Single Reduce Task
15. MR flow with multiple Reducers
16. Input Format & Hierarchy
17. Output Format & Hierarchy
In this session you will learn:
1. History of hadoop
2. Hadoop Ecosystem
3. Hadoop Animal Planet
4. What is Hadoop?
5. Distinctions of hadoop
6. Hadoop Components
7. The Hadoop Distributed Filesystem
8. Design of HDFS
9. When Not to use Hadoop?
10. HDFS Concepts
11. Anatomy of a File Read
12. Anatomy of a File Write
13. Replication & Rack awareness
14. Mapreduce Components
15. Typical Mapreduce Job
Общенациональная система принятия решений «Вече»mike3b
Существующая проблема.
Основная задача политика – принимать решение и тем самым вести граждан и страну к процветанию.
Большинство решений принимаются не правильно из-за:
- Отсутствие обратной связи, незнания актуальных потребностей.
- Отсутствия ответственности политиков.
- Не профессионализма.
- Лоббирования частных (внутренних/внешних) в ущерб обще-национальных интересов.
- Отсутствие поддержки (денег, времени, голосов).
Необходима система по взаимодействию людей, политиков, профильных организаций, общественных организаций, экспертов, которая поможет установить взаимовыгодное сотрудничество для всех сторон.
Решение.
Платформа (веб-портал и мобильные приложения) основная задача которой контроль и влияние на правильность принятия политических решений, от локальных до общенациональных.
1. Создание проектов для решения конкретной проблемы и подтверждение их актуальности
- Отзывы граждан экспертов, профильных организаций и политиков.ю
- Голосования и рейтинги решений
2. Программы политиков, план действий, что делал и какой результат, что делает и какой прогресс, что планирует сделать.
3. Прозрачность решений, действий, истории, а также отзывы и рейтинги в портале наглядно покажут «слив» национальных интересов
4. Обсуждение проблем, идей и предложений (круглый стол)
5. С политиками, профильными организациями, экспертами
6. Формирование «давления» на политиков посредством привлечения ресурсов (СМИ, общественные организации, граждане, бизнес…) для предотвращения принятия не выгодных для общества решений и помощи принятия более целостных решений.
Заключение
Мы, авторы проек
This presentation introduces you to the web and mobile surveying tool Responster and how it works.
Learn how traditional surveying is performed and how Responster re-imagines this to make it easier, more powerful and more rewarding.
Visit www.responster.com to sign up for your free account!
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
Hadoop Interview Questions and Answers by rohit kapakapa rohit
Hadoop Interview Questions and Answers - More than 130 real time questions and answers covering hadoop hdfs,mapreduce and administrative concepts by rohit kapa
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
A guide to using Apache Hadoop as your open source big data platform of choice, including the vendors that make various Hadoop flavors, related open source tools, Hadoop capabilities and suitable applications.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
In this session you will learn:
1. Meet MapReduce
2. Word Count Algorithm – Traditional approach
3. Traditional approach on a Distributed System
4. Traditional approach – Drawbacks
5. MapReduce Approach
6. Input & Output Forms of a MR program
7. Map, Shuffle & Sort, Reduce Phase
8. WordCount Code walkthrough
9. Workflow & Transformation of Data
10. Input Split & HDFS Block
11. Relation between Split & Block
12. Data locality Optimization
13. Speculative Execution
14. MR Flow with Single Reduce Task
15. MR flow with multiple Reducers
16. Input Format & Hierarchy
17. Output Format & Hierarchy
In this session you will learn:
1. History of hadoop
2. Hadoop Ecosystem
3. Hadoop Animal Planet
4. What is Hadoop?
5. Distinctions of hadoop
6. Hadoop Components
7. The Hadoop Distributed Filesystem
8. Design of HDFS
9. When Not to use Hadoop?
10. HDFS Concepts
11. Anatomy of a File Read
12. Anatomy of a File Write
13. Replication & Rack awareness
14. Mapreduce Components
15. Typical Mapreduce Job
Общенациональная система принятия решений «Вече»mike3b
Существующая проблема.
Основная задача политика – принимать решение и тем самым вести граждан и страну к процветанию.
Большинство решений принимаются не правильно из-за:
- Отсутствие обратной связи, незнания актуальных потребностей.
- Отсутствия ответственности политиков.
- Не профессионализма.
- Лоббирования частных (внутренних/внешних) в ущерб обще-национальных интересов.
- Отсутствие поддержки (денег, времени, голосов).
Необходима система по взаимодействию людей, политиков, профильных организаций, общественных организаций, экспертов, которая поможет установить взаимовыгодное сотрудничество для всех сторон.
Решение.
Платформа (веб-портал и мобильные приложения) основная задача которой контроль и влияние на правильность принятия политических решений, от локальных до общенациональных.
1. Создание проектов для решения конкретной проблемы и подтверждение их актуальности
- Отзывы граждан экспертов, профильных организаций и политиков.ю
- Голосования и рейтинги решений
2. Программы политиков, план действий, что делал и какой результат, что делает и какой прогресс, что планирует сделать.
3. Прозрачность решений, действий, истории, а также отзывы и рейтинги в портале наглядно покажут «слив» национальных интересов
4. Обсуждение проблем, идей и предложений (круглый стол)
5. С политиками, профильными организациями, экспертами
6. Формирование «давления» на политиков посредством привлечения ресурсов (СМИ, общественные организации, граждане, бизнес…) для предотвращения принятия не выгодных для общества решений и помощи принятия более целостных решений.
Заключение
Мы, авторы проек
This presentation introduces you to the web and mobile surveying tool Responster and how it works.
Learn how traditional surveying is performed and how Responster re-imagines this to make it easier, more powerful and more rewarding.
Visit www.responster.com to sign up for your free account!
This presentation provides a comprehensive introduction to the Hadoop Distributed System, a powerful and widely used framework for distributed storage and processing of large-scale data. Hadoop has revolutionized the way organizations manage and analyze data, making it a crucial tool in the field of big data and data analytics.
In this presentation, we explore the key components and features of Hadoop, shedding light on the fundamental building blocks that enable its exceptional data processing capabilities. We cover essential topics, including the Hadoop Distributed File System (HDFS), MapReduce, YARN (Yet Another Resource Negotiator), and Hadoop Ecosystem components like Hive, Pig, and Spark.
Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand 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. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2. INTRODUCTION
Hadoop is a framework that allows for the distributed processing of
large data sets across clusters of commodity computer using a simple
programming model.
It is an open-source data management with scale-out storage &
distributed processing.
The objective of this tool is to support running applications on
BigData.
It is an open-source set of tools and distributed under Apache license.
3. BigData
• Big data is a term used to describe the voluminous amount of unstructured
and semi-structured data a company creates.
• Data that would take too much time and cost too much money to load into a
relational database for analysis.
• Big data doesn't refer to any specific quantity, the term is often used when
speaking about petabytes and exabytes of data.
4. Characteristics of Big Data
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
5. What Caused The Problem?
1
2 1
2
Year
Standard Hard Drive Size
(in Mb)
1990 1370
2010 1000000
Year
Data Transfer Rate
(Mbps)
1990 4.4
2010 100
7. So,What Is The Problem?
The transfer speed is around 100 MB/s
A standard disk is 1 Terabyte
Time to read entire disk= 10000 seconds or 3 Hours!
Increase in processing time may not be as helpful because
• Network bandwidth is now more of a limiting factor
• Physical limits of processor chips have been reached
8. So What do We Do?
•The obvious solution is that we use multiple
processors to solve the same problem by
fragmenting it into pieces.
•Imagine if we had 100 drives, each holding
one hundredth of the data. Working in
parallel, we could read the data in under two
minutes.
11. Hadoop core component
There are two parts of Hadoop:-
HDFS (Hadoop distributed file system)
Mapreduce (Processing)
12. MapReduce
Hadoop limits the amount of communication which can be performed by
the processes, as each individual record is processed by a task in isolation
from one another
By restricting the communication between nodes, Hadoop makes the
distributed system much more reliable. Individual node failures can be
worked around by restarting tasks on other machines.
The other workers continue to operate as though nothing went wrong,
leaving the challenging aspects of partially restarting the program to the
underlying Hadoop layer.
Map : (in_value,in_key)(out_key, intermediate_value)
Reduce: (out_key, intermediate_value) (out_value list)
13. What is MapReduce?
MapReduce is a programming model
Programs written in this functional style are automatically parallelized and executed
on a large cluster of commodity machines
MapReduce is an associated implementation for processing and generating large
data sets.
MapReduce
MAP
map function that
processes a key/value pair
to generate a set of
intermediate key/value
pairs
REDUCE
and a reduce function
that merges all
intermediate values
associated with the same
intermediate key.
14. The Programming Model Of MapReduce
Map, written by the user, takes an input pair and produces a set of
intermediate key/value pairs. The MapReduce library groups together
all intermediate values associated with the same intermediate key I and
passes them to the Reduce
15. The Reduce function, also written by the user, accepts an intermediate key I
and a set of values for that key. It merges together these values to form a
possibly smaller set of values
16. How MapReduce Works
A Map-Reduce job usually splits the input data-set into independent chunks
which are processed by the map tasks in a completely parallel manner.
The framework sorts the outputs of the maps, which are then input to the
reduce tasks.
Typically both the input and the output of the job are stored in a file-
system. The framework takes care of scheduling tasks, monitoring them and
re-executes the failed tasks.
A MapReduce job is a unit of work that the client wants to be performed: it
consists of the input data, the MapReduce program, and configuration
information. Hadoop runs the job by dividing it into tasks, of which there
are two types: map tasks and reduce tasks
17.
18. Fault Tolerance
There are two types of nodes that control the job execution process:
tasktrackers and jobtrackers
The jobtracker coordinates all the jobs run on the system by scheduling
tasks to run on tasktrackers.
Tasktrackers run tasks and send progress reports to the jobtracker, which
keeps a record of the overall progress of each job.
If a tasks fails, the jobtracker can reschedule it on a different tasktracker.
24. Combiner Functions
• Many MapReduce jobs are limited by the bandwidth available on the
cluster.
• In order to minimize the data transferred between the map and reduce tasks,
combiner functions are introduced.
• Hadoop allows the user to specify a combiner function to be run on the map
output—the combiner function’s output forms the input to the reduce
function.
• Combiner finctions can help cut down the amount of data shuffled between
the maps and the reduces.
25. Hadoop Streaming:
• Hadoop provides an API to MapReduce that allows you to write your
map and reduce functions in languages other than Java.
• Hadoop Streaming uses Unix standard streams as the interface
between Hadoop and your program, so you can use any language
that can read standard input and write to standard output to write
your MapReduce program.
26. Hadoop Pipes:
• Hadoop Pipes is the name of the C++ interface to Hadoop MapReduce.
• Unlike Streaming, which uses standard input and output to
communicate with the map and reduce code, Pipes uses sockets as the
channel over which the tasktracker communicates with the process
running the C++ map or reduce function. JNI is not used.
27. HADOOP DISTRIBUTED FILESYSTEM (HDFS)
Filesystems that manage the storage across a network of machines are
called distributed filesystems.
Hadoop comes with a distributed filesystem called HDFS, which stands for
Hadoop Distributed Filesystem.
HDFS, the Hadoop Distributed File System, is a distributed file system
designed to hold very large amounts of data (terabytes or even petabytes),
and provide high-throughput access to this information.
28. Namenodes and Datanodes
A HDFS cluster has two types of node operating in a master-worker
pattern: a namenode (the master) and a number of datanodes
(workers).
The namenode manages the filesystem namespace. It maintains the
filesystem tree and the metadata for all the files and directories in the
tree.
Datanodes are the work horses of the filesystem. They store and
retrieve blocks when they are told to (by clients or the namenode), and
they report back to the namenode periodically with lists of blocks that
they are storing.
29. Without the namenode, the filesystem cannot be used. In fact, if the
machine running the namenode were obliterated, all the files on the
filesystem would be lost since there would be no way of knowing how
to reconstruct the files from the blocks on the datanodes.
Important to make the namenode resilient to failure, and Hadoop
provides two mechanisms for this:
1. is to back up the files that make up the persistent state of the
filesystem metadata. Hadoop can be configured so that the namenode
writes its persistent state to multiple filesystems.
2. Another solution is to run a secondary namenode. The secondary
namenode usually runs on a separate physical machine, since it
requires plenty of CPU and as much memory as the namenode to
perform the merge. It keeps a copy of the merged namespace image,
which can be used in the event of the namenode failing
30. File System Namespace
HDFS supports a traditional hierarchical file organization. A user or an
application can create and remove files, move a file from one directory
to another, rename a file, create directories and store files inside these
directories.
HDFS does not yet implement user quotas or access permissions. HDFS
does not support hard links or soft links. However, the HDFS
architecture does not preclude implementing these features.
The Namenode maintains the file system namespace. Any change to
the file system namespace or its properties is recorded by the
Namenode. An application can specify the number of replicas of a file
that should be maintained by HDFS. The number of copies of a file is
called the replication factor of that file. This information is stored by
the Namenode.