The document discusses the Hadoop ecosystem and its components for distributed storage and processing of big data. It describes how Hadoop consists of several key components including HDFS for storage, YARN for resource management, and MapReduce for distributed processing. It also outlines other components of the ecosystem like Hive, Pig, HBase, and Spark that provide additional functionality for working with large datasets.
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
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
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.
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
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
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.
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.
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 Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
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.
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.
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 Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
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.
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62_Tazeen_Sayed_Hadoop_Ecosystem.pptx
1. Hadoop Ecosystem: FromBig
Data to Big Results
Name: Tazeen GulrezSayed
Class : TE - A
Roll number :62
Subject: Data Science And Big Data Analytics
2. 1.Introduction to Hadoop Ecosystem
2.HDFS - Hadoop Distributed File System
3.YARN - Yet Another Resource
NegotiatorMapReduce
4.Other Components of Hadoop Ecosystem
5.Conclusion
INDEX
3. Hadoop is an open-source software framework that is
used for distributed storage and processing of big
data. It was created by Doug Cutting and Mike
Cafarella in 2005, and it has since become one of the
most popular big data processing platforms in the
world.
The Hadoop ecosystem consists of several
components, including HDFS (Hadoop Distributed File
System), YARN (Yet Another Resource Negotiator),
and MapReduce. These components work together to
provide a scalable, fault-tolerant platform for
processing large amounts ofdata.
Introduction to Hadoop
Ecosystem
4. HDFS - Hadoop Distributed File
System
Hadoop is an open-source software framework that is
used for distributed storage and processing of big
data. It was created by Doug Cutting and Mike
Cafarella in 2005, and it has since become one of the
most popular big data processing platforms in the
world.
The Hadoop ecosystem consists of several
components, including HDFS (Hadoop Distributed File
System), YARN (Yet Another Resource Negotiator),
and MapReduce. These components work together to
provide a scalable, fault-tolerant platform for
processing large amounts ofdata.
5. YARN - Yet Another Resource
Negotiator
YARN is the resource management layer of Hadoop. It
is responsible for managing resources in a Hadoop
cluster, such as CPU, memory, and disk space. YARN
allows multiple applications to run on the same
cluster without interfering with each other.
YARN also enables dynamic allocation of resources,
allowing applications to request additional resources
as needed. This makes it possible to run complex big
data applications that require significant amounts of
resources.
6. MapReduce
Map Reduce is a programming model used for
processing large datasets in parallel. It works by
breaking down a large dataset into smaller chunks,
which are then processed in parallel across multiple
nodes in a cluster. Map Reduce consists of two main
functions: map andreduce.
The map function takes input data and converts it into
key-value pairs, while the reduce function takes the
output of the map function and combines it into a
smaller set of key-value pairs. Map Reduce is highly
scalable and fault-tolerant, making it ideal for
processing large amounts ofdata.
7. Other Components ofHadoop
Ecosystem
In addition to HDFS, YARN, and MapReduce, the
Hadoop ecosystem includes several other
components that provide additional functionality.
These include Hive, Pig, HBase, and Spark.n addition
to HDFS, YARN, and MapReduce, the Hadoop
ecosystem includes several other components that
provide additional functionality. These include Hive,
Pig, HBase, and Spark.
Hive is a data warehouse system that provides SQL-
like querying capabilities for Hadoop. Pig is a high-
level platform for creating MapReduce programs.
HBase is a NoSQL database that provides real-time
access to data stored in Hadoop. Spark is a fast, in-
memory data processing engine that can be used with
Hadoop to perform real-time analytics.
8. CONCLUSION
The Hadoop ecosystem is a powerful platform
for processing large amounts of data. With its
distributed architecture, fault tolerance, and
scalability, Hadoop has become the go-to
solution for big data processing.
By understanding the various components of the
Hadoop ecosystem, businesses and
organizations can take advantage of its
capabilities to gain insights and make informed
decisions based on theirdata.
