this is a presentation on hadoop basics. Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models.
Accompanying slides for the class “Introduction to Hadoop” at the PRACE Autumn school 2020 - HPC and FAIR Big Data organized by the faculty of Mechanical Engineering of the University of Ljubljana (Slovenia).
Accompanying slides for the class “Introduction to Hadoop” at the PRACE Autumn school 2020 - HPC and FAIR Big Data organized by the faculty of Mechanical Engineering of the University of Ljubljana (Slovenia).
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. 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-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaEdureka!
YouTube Link: https://youtu.be/ll_O9JsjwT4
** Big Data Hadoop Certification Training - https://www.edureka.co/big-data-hadoop-training-certification **
This Edureka PPT on "Hadoop components" will provide you with detailed knowledge about the top Hadoop Components and it will help you understand the different categories of Hadoop Components. This PPT covers the following topics:
What is Hadoop?
Core Components of Hadoop
Hadoop Architecture
Hadoop EcoSystem
Hadoop Components in Data Storage
General Purpose Execution Engines
Hadoop Components in Database Management
Hadoop Components in Data Abstraction
Hadoop Components in Real-time Data Streaming
Hadoop Components in Graph Processing
Hadoop Components in Machine Learning
Hadoop Cluster Management tools
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this presentation describes the company from where I did my summer training and what is bigdata why we use big data, big data challenges, the issue in big data, the solution of big data issues, hadoop, docker , Ansible etc.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
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!
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
PRACE Autumn school 2021 - Big Data with Hadoop and Keras
27-30 September 2021
Fakulteta za strojništvo
Europe/Ljubljana
Data and scripts are available at: https://www.events.prace-ri.eu/event/1226/timetable/
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. 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-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaEdureka!
YouTube Link: https://youtu.be/ll_O9JsjwT4
** Big Data Hadoop Certification Training - https://www.edureka.co/big-data-hadoop-training-certification **
This Edureka PPT on "Hadoop components" will provide you with detailed knowledge about the top Hadoop Components and it will help you understand the different categories of Hadoop Components. This PPT covers the following topics:
What is Hadoop?
Core Components of Hadoop
Hadoop Architecture
Hadoop EcoSystem
Hadoop Components in Data Storage
General Purpose Execution Engines
Hadoop Components in Database Management
Hadoop Components in Data Abstraction
Hadoop Components in Real-time Data Streaming
Hadoop Components in Graph Processing
Hadoop Components in Machine Learning
Hadoop Cluster Management tools
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
this presentation describes the company from where I did my summer training and what is bigdata why we use big data, big data challenges, the issue in big data, the solution of big data issues, hadoop, docker , Ansible etc.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
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!
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
PRACE Autumn school 2021 - Big Data with Hadoop and Keras
27-30 September 2021
Fakulteta za strojništvo
Europe/Ljubljana
Data and scripts are available at: https://www.events.prace-ri.eu/event/1226/timetable/
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
One of the challenges in storing and processing the data and using the latest internet technologies has resulted in large volumes of data. The technique to manage this massive amount of data and to pull out the value, out of this volume is collectively called Big data. Over the recent years, there has been a rising interest in big data for social media analysis. Online social media have become the important platform across the world to share information. Facebook, one of the largest social media site receives posts in millions every day. One of the efficient technologies that deal with the Big Data is Hadoop. Hadoop, for processing large data volume jobs uses MapReduce programming model. This paper provides a survey on Hadoop and its role in facebook and a brief introduction to HIVE.
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.
Enroll Free Live demo of Hadoop online training and big data analytics courses online and become certified data analyst/ Hadoop developer. Get online Hadoop training & certification.
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
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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.
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?
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.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
2. Big Data
"Big Data” is 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.
In simple terms, "Big Data" consists of very large volumes of
heterogeneous data that is being generated, often, at high
speeds.
Big Data requires the use of a new set of tools, applications
and frameworks to process and manage the data.
3. Characteristics of Big Data:
The characteristics of Big Data are
popularly known as Three V's of
Big Data.
Volume:
This size aspect of data is referred to as
Volume in the Big Data world.
Velocity:
This speed aspect of data generation is
referred to as Velocity in the Big Data
world.
Variety:
This aspect of varied data formats is
referred to as Variety in the Big Data
world.
Sources of Big Data can be
broadly classified into six
different categories:
1.Enterprise Data
2. Transactional Data
3. Social Media
4. Activity Generated
5. Public Data
6. Archives
Sources of Big Data:
4. Hadoop is an Apache open source framework written in java
that allows distributed processing of large datasets across
clusters of computers using simple programming
models. That manages data processing and storage for big
data applications running in clustered systems.
5. History of Hadoop had
started in the year 2002 with
the project Apache
Nutch. Hadoop was created
by Doug Cutting, the creator
of Apache Lucene, the widely
used text search library.
According to Hadoop's creator
Doug Cutting, "The name
Hadoop given by my kid to a
stuffed yellow elephant. Short,
relatively easy to spell and
pronounce, meaningless, and
not used elsewhere.
Hadoop was created by Doug Cutting and
Mike Cafarella.
History of Hadoop
6. Characteristics of Hadoop
Hadoop provides
a reliable shared
storage (HDFS)
and analysis
system (Map-
Reduce).
Hadoop is highly
scalable
As Hadoop
scales linearly, a
Hadoop Cluster
can contain tens,
hundreds, or
even thousands
of servers.
Hadoop is highly
flexible and can
process both
structured as
well as
unstructured
data. Hadoop has
built-in fault
tolerance.
Hadoop works on
the principle of
write once and
read multiple
times. Hadoop is
optimized for
large and very
large data sets.
Hadoop is very
cost effective as
it can work with
commodity
hardware and
does not require
expensive high-
end hardware.
7. Hadoop works in a master-worker / master-slave
fashion.
Hadoop has two core components: HDFS and
MapReduce.
HDFS (Hadoop Distributed File System) offers a
highly reliable and distributed storage, and ensures
reliability, by storing the data across multiple nodes.
MapReduce offers an analysis system which can
perform complex computations on large datasets. This
component is responsible for performing all the
computations and works by breaking down a large
complex computation into multiple tasks and assigns
those to individual worker/slave nodes.
The master contains the Namenode and Job Tracker
components.
Namenode holds the information about all the other
nodes in the Hadoop Cluster.
Job Tracker keeps track of the individual tasks/jobs
assigned to each of the nodes and coordinates the
exchange of information and results.
Each Worker / Slave contains the Task Tracker and a
Datanode components.
Task Tracker is responsible for running the task /
computation assigned to it.
Datanode is responsible for holding the data.
8. Hadoop Distributions
Cloudera was the first company
to be formed to build enterprise
solutions based on Hadoop.
Cloudera has a Hadoop
distribution known as
Cloudera's Distribution for
Hadoop (CDH).
MapR is another major
distribution available in the
market.
MapR is available in the cloud
through some of the leading
cloud providers Amazon Web
Services (AWS), Google
Compute Engine, CenturyLink
Technology Solutions, and
OpenStack.
Amazon Web Services (AWS)
Elastic MapReduce (EMR) was
among the first Hadoop
offerings available in the market.
Azure HDInsight is Microsoft's
distribution of Hadoop.
Hortonworks has a Hadoop
distribution known as
Hortonworks Data Platform
(HDP).
Cloudera
Hortonworks
Amazon
Elastic Map
Reduce
(EMR)
MapR
Azure
HDInsight
9. Hadoop Ecosystem
Apach
e Hive
Apache
Pig
Apache
ZooKeeper
Y
1. Apache Pig is a software framework which offers a run-time
environment for execution of MapReduce jobs on a Hadoop Cluster via
a high-level scripting language called Pig Latin.
2. Apache Hive Data Warehouse framework facilitates the querying and
management of large datasets residing in a distributed store/file system
like Hadoop Distributed File System (HDFS).
3. Apache Mahout is a scalable machine learning and data mining library.
4. Apache HBase is a distributed, versioned, column-oriented, scalable
and a big data store on top of Hadoop/HDFS.
5. Apache Sqoop is a tool designed for efficiently transferring the data
between Hadoop and Relational Databases (RDBMS).
6. Apache Oozie is a job workflow scheduling and coordination manager
for managing the jobs executed on Hadoop.
7. Apache ZooKeeper is an open source coordination service for
distributed applications.
8. Apache Ambari is an open source software framework for provisioning,
managing, and monitoring Hadoop clusters.
Apache
Hive
Apache
HBase
Apache
Mahout
Apache
Sqoop
Apache
Oozie
HDFS and MapReduce are the two core components of the Hadoop Ecosystem and are at the heart of the Hadoop framework.
other Apache Projects which are built around the Hadoop Framework which are part of the Hadoop Ecosystem.
Apache
Ambari
10. Hadoop Core Components
YARN
Yet another resource negotiator) is a
resource manager that knows how to
allocate distributed computer
resources to various cluster
MapReduce
MapReduce is a framework that
enables running MapReduce jobs on
the hadoop cluster powered by YARN.
It provides a high level API for
implementing Custom Map and
Reduce function in various languages
as well as the code infrastructure
needed to submit, run and monitor
MapReduce jobs.
HDFS
(Hadoop diatributed file system)
designed for storing large files of the
magnitute of hundreds of megabytes
or gigabytes and provides
high_throughput streaming data
access to them.
11. Hadoop Versions
2.7.x2.7.7
31 May 2018
2.8 x.2.8.5
15 September 2018
2.9 x 2.9.2
9 november 2018
3.1 x 3.1.2
6 February 2019
3.2 x 3.2.0
16 january
12. Hadoop 2.8.0 installation
Download
Hadoop
and Java
Install
Java
Extract
the
Hadoop
file
Testing
Edit
configuration
files
Set
environment
variable
Set path
Replace
the bin
file
Format
name
node
13. Download Hadoop 2.8.0 (Link: http://www-
eu.apache.org/dist/hadoop/common/hadoop-
2.8.0/hadoop-
2.8.0.tar.gz OR http://archive.apache.org/dist/hadoop/c
ore//hadoop-2.8.0/hadoop-2.8.0.tar.gz)
install java on your under "C:JAVA"Java JDK 1.8.0.zip
(Link: http://www.oracle.com/technetwork/java/javase/
downloads/jdk8-downloads-2133151.html)
.
use "Javac -version" to check the version of Java installed
in your system
14. Extract file Hadoop 2.8.0.tar.gz or Hadoop-2.8.0.zip and place under "C:Hadoop-2.8.0".
HADOOP_HOME
JAVA_HOME
path
Set the path HADOOP_HOME Environment variable
Environment Variable -> New ->
Variable name: HADOOP_HOME
Variable value: C:hadoop-2.8.0bin
->OK
Set the path JAVA_HOME Environment variable
Environment Variable -> New ->
Variable name: JAVA_HOME
Variable value: C:javabin
->OK
set the Hadoop bin directory path and JAVA bin
directory path.
Environment variables -> System variables ->
path -> Edit -> New -> C:hadoop-2.8.0bin -> New
-> C:javabin -> OK
15. Edit the configuration
files, paste these
code below xml
paragraph and save
the files.
Create foldersfile C:/Hadoop-2.8.0/etc/hadoop
/mapred-site.xml
file C:Hadoop-2.8.0/etc/hadoop/h
dfs-site.xml
file C:/Hadoop-2.8.0/etc/hadoo
p/hadoop-env.cmd
file C:/Hadoop-2.8.0/etc/hadoop/y
arn-site.xml
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value
>
</property>
</configuration>
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>C:hadoop-
2.8.0datanamenode</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>C:hadoop-
2.8.0datadatanode</value>
</property>
</configuration>
<configuration>
<property>
<name>mapreduce.framework.nam
e</name>
<value>yarn</value>
</property>
</configuration>
Create
folder "data" under "C:Hadoo
p-2.8.0"
Create
folder "datanode" under "C:H
adoop-2.8.0data"
Create
folder "namenode" under "C:
Hadoop-2.8.0data"
<configuration>
<property>
<name>yarn.nodemanager.aux-
services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.nodemanager.auxservices
.mapreduce.shuffle.class</name>
<value>org.apache.hadoop.mapred.Sh
uffleHandler</value>
</property>
</configuration>
Edit file C:/Hadoop-
2.8.0/etc/hadoop/hadoop-
env.cmd by closing the
command
line "JAVA_HOME=%JAVA_HO
ME%" instead of
set "JAVA_HOME=C:Java" (On
C:java this is path to file
jdk.18.0)
file C:/Hadoop-
2.8.0/etc/hadoop/core-site.xml
16. Replace the bin file
Delete file bin on C:Hadoop-
2.8.0bin, replaced by file bin
on file just download (from
Hadoop Configuration.zip).
Dowload file Hadoop
Configuration.zip
(Link: https://github.com/Muha
mmadBilalYar/HADOOP-
INSTALLATION-ON-
WINDOW-
10/blob/master/Hadoop%20C
onfiguration.zip)
17. Open cmd and typing command "hdfs namenode –format" .
Format the namenode
We Create
Quality Professional
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18. Testing
Open cmd and change directory to "C:Hadoop-2.8.0sbin" and
type "start-all.cmd" to start apache.
Make sure these apps
are running
1.Hadoop Namenode
2.Hadoop datanode
3.YARN Resourc
Manager
4.YARN Node
Manager
Open: http://localhost:50070
Open: http://localhost:8088
19. MAPREDUCE
The MapReduce algorithm contains two important
tasks, namely Map and Reduce.
MapReduce is a framework using which we can write applications to process
huge amounts of data, in parallel, on large clusters of commodity hardware in a
reliable manner.
Map takes a set of data and converts it into another set
of data, where individual elements are broken down into
tuples (key/value pairs).
MapReduce is a processing technique and a program
model for distributed computing based on java.
reduce task, which takes the output from a map as an input and combines
those data tuples into a smaller set of tuples. As the sequence of the name
MapReduce implies, the reduce task is always performed after the map job.
Generally MapReduce
paradigm is based on sending
the computer to where the data
resides!
The major advantage of
MapReduce is that it is easy to
scale data processing over
multiple computing nodes.
MapReduce program executes in
three stages, namely map stage,
shuffle stage, and reduce stage.
MapReduce
1
8
7
6
5
4
3
2
20. Map stage − The
map or mapper’s job
is to process the input
data. Generally the
input data is in the
form of file or
directory and is stored
in the Hadoop file
system (HDFS). The
input file is passed to
the mapper function
line by line. The
mapper processes the
data and creates
several small chunks
of data.
Stages of
MapReduce
program
Reduce stage − This
stage is the
combination of
the Shuffle stage and
the Reduce stage.
The Reducer’s job is
to process the data
that comes from the
mapper. After
processing, it
produces a new set of
output, which will be
stored in the HDFS.