Big data(hadoop)
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Big data
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
Big data analytics.
Big data refers to large, complex datasets that cannot be processed by traditional methods. The volume, velocity, and variety of big data are increasing rapidly due to sources like social media and mobile devices. Hadoop is an open-source framework that allows storing and processing big data in a distributed, parallel fashion across clusters of commodity hardware. It uses HDFS for storage and MapReduce for processing. HDFS divides files into blocks and stores replicas across nodes for reliability. MapReduce breaks jobs into map and reduce tasks to process data in parallel.
Big data and hadoop
This document provides an overview of big data and Hadoop. It discusses what big data is, its types including structured, semi-structured and unstructured data. Some key sources of big data are also outlined. Hadoop is presented as a solution for managing big data through its core components like HDFS for storage and MapReduce for processing. The Hadoop ecosystem including other related tools like Hive, Pig, Spark and YARN is also summarized. Career opportunities in working with big data are listed in the end.
Big data ppt
The document discusses big data, including what it is, sources of big data like social media and stock exchange data, and the three Vs of big data - volume, velocity, and variety. It then discusses Hadoop, the open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed computation, and YARN which manages computing resources. The document also provides overviews of Pig and Jaql, programming languages used for analyzing data in Hadoop.
Bigdata and hadoop
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
Hadoop
This document defines and describes big data and Hadoop. It states that big data is large datasets that cannot be processed using traditional techniques due to their volume, velocity and variety. It then describes the different types of data (structured, semi-structured, unstructured), challenges of big data, and Hadoop's use of MapReduce as a solution. It provides details on the Hadoop architecture including HDFS for storage and YARN for resource management. Common applications and users of Hadoop are also listed.
Bigdata & Hadoop
The document discusses big data and Hadoop. It notes that big data is characterized by volume, variety, velocity, and veracity. Hadoop is an open-source platform for distributed storage and processing of large datasets across clusters of commodity hardware. Hadoop consists of HDFS for storage and MapReduce as a programming model. Limitations of Hadoop 1.x include lack of horizontal scalability and high availability, while Hadoop 2.x addresses these with features like HDFS federation and YARN to support multiple workloads.
Presentation on BigData by Swapnaja
The document discusses big data and how it is being generated from various sources like social media, sensors, and mobile devices. It describes the key characteristics of big data known as the three V's - volume, velocity and variety. It then explains how Hadoop uses HDFS for storage and MapReduce for processing large datasets in parallel across clusters of computers. The conclusion states that big data presents both opportunities and challenges for industries in creating value from large and diverse datasets.
Recommended
Big data
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
Big data analytics.
Big data refers to large, complex datasets that cannot be processed by traditional methods. The volume, velocity, and variety of big data are increasing rapidly due to sources like social media and mobile devices. Hadoop is an open-source framework that allows storing and processing big data in a distributed, parallel fashion across clusters of commodity hardware. It uses HDFS for storage and MapReduce for processing. HDFS divides files into blocks and stores replicas across nodes for reliability. MapReduce breaks jobs into map and reduce tasks to process data in parallel.
Big data and hadoop
This document provides an overview of big data and Hadoop. It discusses what big data is, its types including structured, semi-structured and unstructured data. Some key sources of big data are also outlined. Hadoop is presented as a solution for managing big data through its core components like HDFS for storage and MapReduce for processing. The Hadoop ecosystem including other related tools like Hive, Pig, Spark and YARN is also summarized. Career opportunities in working with big data are listed in the end.
Big data ppt
The document discusses big data, including what it is, sources of big data like social media and stock exchange data, and the three Vs of big data - volume, velocity, and variety. It then discusses Hadoop, the open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed computation, and YARN which manages computing resources. The document also provides overviews of Pig and Jaql, programming languages used for analyzing data in Hadoop.
Bigdata and hadoop
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
Hadoop
This document defines and describes big data and Hadoop. It states that big data is large datasets that cannot be processed using traditional techniques due to their volume, velocity and variety. It then describes the different types of data (structured, semi-structured, unstructured), challenges of big data, and Hadoop's use of MapReduce as a solution. It provides details on the Hadoop architecture including HDFS for storage and YARN for resource management. Common applications and users of Hadoop are also listed.
Bigdata & Hadoop
The document discusses big data and Hadoop. It notes that big data is characterized by volume, variety, velocity, and veracity. Hadoop is an open-source platform for distributed storage and processing of large datasets across clusters of commodity hardware. Hadoop consists of HDFS for storage and MapReduce as a programming model. Limitations of Hadoop 1.x include lack of horizontal scalability and high availability, while Hadoop 2.x addresses these with features like HDFS federation and YARN to support multiple workloads.
Presentation on BigData by Swapnaja
The document discusses big data and how it is being generated from various sources like social media, sensors, and mobile devices. It describes the key characteristics of big data known as the three V's - volume, velocity and variety. It then explains how Hadoop uses HDFS for storage and MapReduce for processing large datasets in parallel across clusters of computers. The conclusion states that big data presents both opportunities and challenges for industries in creating value from large and diverse datasets.
Hadoop and its role in Facebook: An Overview
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.
Intro to Big Data Hadoop
Introduction to Big Data Hadoop Presented by Dr. Sandeep Deshmukh: Committer Apache Apex, DataTorrent engineer
A Glimpse of Bigdata - Introduction
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.
Introduction of Big data and Hadoop
this presentation has small introduction about Big data and hadoop framework for beginners . Hope you like it.
Big Data
The document discusses big data, providing definitions and facts about the volume of data being created. It describes the characteristics of big data using the 5 V's model (volume, velocity, variety, veracity, value). Different types of data are mentioned, from unstructured to structured. Hadoop is introduced as an open source software framework for distributed processing and analyzing large datasets using MapReduce and HDFS. Hardware and software requirements for working with big data and Hadoop are listed.
Hadoop in action
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.
Gail Zhou on "Big Data Technology, Strategy, and Applications"
Dr. Gail Zhou presented this topic at DevNexus on Feb 25, 2014. Big Data history, opportunities, and applications. Big Data key concepts, reference architecture with open source technology stacks. Hadoop architecture explained (HDFS, Map Reduce, and YARN). Big Data start-up challenges and strategies to overcome them. Technology update: Hadoop and Cassandra based technology offerings.
Big data computing
Big data refers to large, complex datasets that are growing exponentially and are difficult to process using traditional methods. Large companies like Walmart, Facebook, and AT&T generate huge amounts of big data through customer transactions, social media activity, and telecommunications networks. Apache Hadoop is an open source software framework that harnesses big data by using HDFS for data storage and MapReduce for distributed processing across clusters of computers. The Hadoop ecosystem includes tools like Ambari, Flume, Sqoop, Oozie, Pig, Mahout, Hive, HBase, and Zookeeper that support functions like provisioning, data collection, transfer, workflows, scripting, machine learning, querying, columnar storage, and
Big data abstract
This technology is most powerful area. The world are used in many number of data. So, this technology is stored the huge amount of data.
Big data Presentation
A short presentation on big data and the technologies available for managing Big Data. and it also contains a brief description of the Apache Hadoop Framework
1. what is hadoop part 1
Hadoop is an open source framework that allows for the distributed processing of large data sets across clusters of commodity hardware. It was designed to scale from terabytes to petabytes of data and to handle both structured and unstructured data. Hadoop uses a programming model called MapReduce that partitions work across nodes in a cluster. It is not a replacement for a relational database as it is designed for batch processing large volumes of data rather than transactional workloads or business intelligence queries. Big data refers to the large and growing volumes of structured, semi-structured and unstructured data that are beyond the ability of traditional databases to capture, manage, and process. Examples of big data sources include social media, sensors, and internet activity,
Introduction to Big Data and Hadoop using Local Standalone Mode
Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
big data overview ppt
This document presents an overview of big data. It defines big data as large, diverse data that requires new techniques to manage and extract value from. It discusses the 3 V's of big data - volume, velocity and variety. Examples of big data sources include social media, sensors, photos and business transactions. Challenges of big data include storage, transfer, processing, privacy and data sharing. Past solutions discussed include data sharding, while modern solutions include Hadoop, MapReduce, HDFS and RDF.
Big data PPT
This document defines key terms related to big data such as structured data, unstructured data, and semi-structured data. It discusses how data is generated from various sources and factors like sensors, social networks, and online shopping. It explains that big data refers to data that is too large to process using traditional methods due to its volume, velocity, and variety. Hadoop is introduced as an open source framework that uses HDFS for distributed storage and MapReduce for distributed processing of large data sets across computer clusters.
Comparison with Traditional databases
This document discusses several key differences between traditional databases and Hive. Hive uses a schema-on-read model where the schema is not enforced during data loading, making the initial load much faster. However, this impacts query performance since indexing and compression cannot be applied during loading. Pig Latin is a data flow language where each step transforms the input relation, unlike SQL which is declarative. While Hive originally lacked features like updates, transactions and indexing, the developers are working to integrate HBase and improve support for these features.
Big Data Final Presentation
The document discusses big data and its applications. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It outlines the three V's of big data - volume, variety, and velocity. Various types of structured, semi-structured, and unstructured data are described. Examples are given of how big data is used in various industries like automotive, finance, manufacturing, policing, and utilities to improve products, detect fraud, perform simulations, track suspects, and monitor assets. Popular big data software like Hadoop and MongoDB are also mentioned.
Big Data Analytics 2014
This document discusses open source tools for big data analytics. It introduces Hadoop, HDFS, MapReduce, HBase, and Hive as common tools for working with large and diverse datasets. It provides overviews of what each tool is used for, its architecture and components. Examples are given around processing log and word count data using these tools. The document also discusses using Pentaho Kettle for ETL and business intelligence projects with big data.
Big Data Hadoop Training by Easylearning Guru
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Big data
Big data refers to massive volumes of structured and unstructured data that are difficult to process using traditional databases. Hadoop is an open-source framework for distributed storage and processing of big data across clusters of commodity hardware. It uses HDFS for storage and MapReduce as a programming model. HDFS stores data in blocks across nodes for fault tolerance. MapReduce allows parallel processing of large datasets.
The Big Data Stack
The document summarizes the key components of the big data stack, from the presentation layer where users interact, through various processing and storage layers, down to the physical infrastructure of data centers. It provides examples like Facebook's petabyte-scale data warehouse and Google's globally distributed database Spanner. The stack aims to enable the processing and analysis of massive datasets across clusters of servers and data centers.
Big data Hadoop presentation
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
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
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Hadoop and its role in Facebook: An Overview
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.
Intro to Big Data Hadoop
Introduction to Big Data Hadoop Presented by Dr. Sandeep Deshmukh: Committer Apache Apex, DataTorrent engineer
A Glimpse of Bigdata - Introduction
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.
Introduction of Big data and Hadoop
this presentation has small introduction about Big data and hadoop framework for beginners . Hope you like it.
Big Data
The document discusses big data, providing definitions and facts about the volume of data being created. It describes the characteristics of big data using the 5 V's model (volume, velocity, variety, veracity, value). Different types of data are mentioned, from unstructured to structured. Hadoop is introduced as an open source software framework for distributed processing and analyzing large datasets using MapReduce and HDFS. Hardware and software requirements for working with big data and Hadoop are listed.
Hadoop in action
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.
Gail Zhou on "Big Data Technology, Strategy, and Applications"
Dr. Gail Zhou presented this topic at DevNexus on Feb 25, 2014. Big Data history, opportunities, and applications. Big Data key concepts, reference architecture with open source technology stacks. Hadoop architecture explained (HDFS, Map Reduce, and YARN). Big Data start-up challenges and strategies to overcome them. Technology update: Hadoop and Cassandra based technology offerings.
Big data computing
Big data refers to large, complex datasets that are growing exponentially and are difficult to process using traditional methods. Large companies like Walmart, Facebook, and AT&T generate huge amounts of big data through customer transactions, social media activity, and telecommunications networks. Apache Hadoop is an open source software framework that harnesses big data by using HDFS for data storage and MapReduce for distributed processing across clusters of computers. The Hadoop ecosystem includes tools like Ambari, Flume, Sqoop, Oozie, Pig, Mahout, Hive, HBase, and Zookeeper that support functions like provisioning, data collection, transfer, workflows, scripting, machine learning, querying, columnar storage, and
Big data abstract
This technology is most powerful area. The world are used in many number of data. So, this technology is stored the huge amount of data.
Big data Presentation
A short presentation on big data and the technologies available for managing Big Data. and it also contains a brief description of the Apache Hadoop Framework
1. what is hadoop part 1
Hadoop is an open source framework that allows for the distributed processing of large data sets across clusters of commodity hardware. It was designed to scale from terabytes to petabytes of data and to handle both structured and unstructured data. Hadoop uses a programming model called MapReduce that partitions work across nodes in a cluster. It is not a replacement for a relational database as it is designed for batch processing large volumes of data rather than transactional workloads or business intelligence queries. Big data refers to the large and growing volumes of structured, semi-structured and unstructured data that are beyond the ability of traditional databases to capture, manage, and process. Examples of big data sources include social media, sensors, and internet activity,
Introduction to Big Data and Hadoop using Local Standalone Mode
Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
big data overview ppt
This document presents an overview of big data. It defines big data as large, diverse data that requires new techniques to manage and extract value from. It discusses the 3 V's of big data - volume, velocity and variety. Examples of big data sources include social media, sensors, photos and business transactions. Challenges of big data include storage, transfer, processing, privacy and data sharing. Past solutions discussed include data sharding, while modern solutions include Hadoop, MapReduce, HDFS and RDF.
Big data PPT
This document defines key terms related to big data such as structured data, unstructured data, and semi-structured data. It discusses how data is generated from various sources and factors like sensors, social networks, and online shopping. It explains that big data refers to data that is too large to process using traditional methods due to its volume, velocity, and variety. Hadoop is introduced as an open source framework that uses HDFS for distributed storage and MapReduce for distributed processing of large data sets across computer clusters.
Comparison with Traditional databases
This document discusses several key differences between traditional databases and Hive. Hive uses a schema-on-read model where the schema is not enforced during data loading, making the initial load much faster. However, this impacts query performance since indexing and compression cannot be applied during loading. Pig Latin is a data flow language where each step transforms the input relation, unlike SQL which is declarative. While Hive originally lacked features like updates, transactions and indexing, the developers are working to integrate HBase and improve support for these features.
Big Data Final Presentation
The document discusses big data and its applications. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It outlines the three V's of big data - volume, variety, and velocity. Various types of structured, semi-structured, and unstructured data are described. Examples are given of how big data is used in various industries like automotive, finance, manufacturing, policing, and utilities to improve products, detect fraud, perform simulations, track suspects, and monitor assets. Popular big data software like Hadoop and MongoDB are also mentioned.
Big Data Analytics 2014
This document discusses open source tools for big data analytics. It introduces Hadoop, HDFS, MapReduce, HBase, and Hive as common tools for working with large and diverse datasets. It provides overviews of what each tool is used for, its architecture and components. Examples are given around processing log and word count data using these tools. The document also discusses using Pentaho Kettle for ETL and business intelligence projects with big data.
Big Data Hadoop Training by Easylearning Guru
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Big data
Big data refers to massive volumes of structured and unstructured data that are difficult to process using traditional databases. Hadoop is an open-source framework for distributed storage and processing of big data across clusters of commodity hardware. It uses HDFS for storage and MapReduce as a programming model. HDFS stores data in blocks across nodes for fault tolerance. MapReduce allows parallel processing of large datasets.
The Big Data Stack
The document summarizes the key components of the big data stack, from the presentation layer where users interact, through various processing and storage layers, down to the physical infrastructure of data centers. It provides examples like Facebook's petabyte-scale data warehouse and Google's globally distributed database Spanner. The stack aims to enable the processing and analysis of massive datasets across clusters of servers and data centers.
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Gail Zhou on "Big Data Technology, Strategy, and Applications"
Gail Zhou on "Big Data Technology, Strategy, and Applications"
Introduction to Big Data and Hadoop using Local Standalone Mode
Introduction to Big Data and Hadoop using Local Standalone Mode
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Big data Hadoop presentation
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
Big data refers to large amounts of data from various sources that is analyzed to solve problems. It is characterized by volume, velocity, and variety. Hadoop is an open source framework used to store and process big data across clusters of computers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and HIVE for querying. Other tools like Pig and HBase provide additional functionality. Together these tools provide a scalable infrastructure to handle the volume, speed, and complexity of big data.
Hadoop hdfs interview questions
The document provides an overview of Hadoop and HDFS. It discusses key concepts such as what big data is, examples of big data, an overview of Hadoop, the core components of HDFS and MapReduce, characteristics of HDFS including fault tolerance and throughput, the roles of the namenode and datanodes, and how data is stored and replicated in blocks in HDFS. It also answers common interview questions about Hadoop and HDFS.
Big Data & Hadoop
The document discusses big data and key related concepts like the 3 Vs of big data (volume, velocity, and variety), Hadoop, HDFS, and MapReduce. It explains that big data refers to large amounts of data that are too large to process on a single machine. Hadoop is an open-source software framework for distributed storage and processing of big data using the HDFS file system and MapReduce programming model. HDFS stores large files across clusters of machines, providing fault tolerance through data replication. MapReduce allows distributed processing of large datasets across clusters.
THE SOLUTION FOR BIG DATA
Discussed the huge problems facing on Data storages and enclosed the Technology for it.Discussed drawbacks and future goals of it.
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Discussed the huge problems facing on Data storages and enclosed the Technology for it.Discussed drawbacks and future goals of it.
hadoop
Hadoop is a framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses HDFS for fault-tolerant storage and MapReduce as a programming model for distributed computing. HDFS stores data across clusters of machines as blocks that are replicated for reliability. The namenode manages filesystem metadata while datanodes store and retrieve blocks. MapReduce allows processing of large datasets in parallel using a map function to distribute work and a reduce function to aggregate results. Hadoop provides reliable and scalable distributed computing on commodity hardware.
hadoop
Hadoop is a framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses HDFS for fault-tolerant storage and MapReduce as a programming model for distributed computing. HDFS stores data across clusters of machines and replicates it for reliability. MapReduce allows processing of large datasets in parallel by splitting work into independent tasks. Hadoop provides reliable and scalable storage and analysis of very large amounts of data.
Big data Analytics Hadoop
big data analytics using hadoop and basics of hadoop architecture mapreduce hive pig jaql and a mini project based on hadoop
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This document provides an overview of Hadoop and Big Data. It begins with introducing key concepts like structured, semi-structured, and unstructured data. It then discusses the growth of data and need for Big Data solutions. The core components of Hadoop like HDFS and MapReduce are explained at a high level. The document also covers Hadoop architecture, installation, and developing a basic MapReduce program.
Big Data and Hadoop
This document provides an overview of big data and Hadoop. It defines big data using the 3Vs - volume, variety, and velocity. It describes Hadoop as an open-source software framework for distributed storage and processing of large datasets. The key components of Hadoop are HDFS for storage and MapReduce for processing. HDFS stores data across clusters of commodity hardware and provides redundancy. MapReduce allows parallel processing of large datasets. Careers in big data involve working with Hadoop and related technologies to extract insights from large and diverse datasets.
Big Data and Hadoop - An Introduction
This document provides an overview of Hadoop, a tool for processing large datasets across clusters of computers. It discusses why big data has become so large, including exponential growth in data from the internet and machines. It describes how Hadoop uses HDFS for reliable storage across nodes and MapReduce for parallel processing. The document traces the history of Hadoop from its origins in Google's file system GFS and MapReduce framework. It provides brief explanations of how HDFS and MapReduce work at a high level.
Introduction to Apache Hadoop Eco-System
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
Hadoop Training | Hadoop Training For Beginners | Hadoop Architecture | Hadoo...
The document provides information about Hadoop training. It discusses the need for Hadoop in today's data-heavy world. It then describes what Hadoop is, its ecosystem including HDFS for storage and MapReduce for processing. It also discusses YARN and provides a bank use case. It further explains the architecture and working of HDFS and MapReduce in processing large datasets in parallel across clusters.
Hadoop introduction , Why and What is Hadoop ?
Hadoop Introduction
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Big Data and Hadoop
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.
INTRODUCTION OF BIG DATA
This document introduces big data and provides an overview of key concepts. Big data refers to large, complex datasets that cannot be processed by traditional software. It is characterized by volume, velocity, variety, and veracity. Hadoop is an open-source framework for storing and processing big data across clusters of computers using MapReduce. Hive provides a data warehouse infrastructure to process structured data in Hadoop, while MapReduce is a programming model for parallel processing of large datasets.
IJARCCE_49
This document discusses the evolution from traditional RDBMS to big data analytics. As data volumes grow rapidly, traditional RDBMS struggle to store and process large amounts of data. Hadoop provides a framework to store and process big data across commodity hardware. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed processing, Hive for SQL-like queries, and Sqoop for transferring data between Hadoop and relational databases. The document also outlines some applications and limitations of Hadoop.
Hadoop
Big data refers to large volumes of data that are diverse in type and are produced rapidly. It is characterized by the V's: volume, velocity, variety, veracity, and value. Hadoop is an open-source software framework for distributed storage and processing of big data across clusters of commodity servers. It has two main components: HDFS for storage and MapReduce for processing. Hadoop allows for the distributed processing of large data sets across clusters in a reliable, fault-tolerant manner. The Hadoop ecosystem includes additional tools like HBase, Hive, Pig and Zookeeper that help access and manage data. Understanding Hadoop is a valuable skill as many companies now rely on big data and Hadoop technologies.
BIG DATA
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The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
dbms calicut university B. sc Cs 4th sem.pdf
Its a seminar ppt on database management system using sql
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
This case study explores designing a scalable e-commerce platform, covering key requirements, system components, and best practices.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
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Dandelion Hashtable: beyond billion requests per second on a commodity server
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
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Best 20 SEO Techniques To Improve Website Visibility In SERP
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HCL Notes and Domino License Cost Reduction in the World of DLAU
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Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
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Big data(hadoop)
- 1. A Presentation On Big Data & Hadoop Submitted To:- Mrs. Sonika Narang Mrs. Poonam Beri Submitted By:- Ms. Shabnam 34633
- 2. Big data means really a big data, it is a collection of large & complex data that it becomes difficult to process using traditional data processing applications.
- 3. Black Box Data Social Media Data Stock Exchange Data Power Grid Data Transport Data
- 5. TYPES OF BIG DATA Structured Data:-Relational Data Semi-Structured Data:-XML Data Unstructured Data:-PDF ,Word ,Text ,Media Logs etc.
- 6. Daily, updation of 0.5 PBs on FACEBOOK including 40 millions PHOTOS. Daily ,videos uploading on YOUTUBE that can be watched for 1 year continously. Also affect INTERNET SEARCH,FINANCE & BUSINESS INFORMATION Challenge include in CAPTURE,SEARCHING,SHARING,ANALY- SIS,STORAGE & VISUALIZATION of data.
- 7. LIMITATION Can’t Deal With Huge Amount of Data SO TRADITIONAL APPROACH FAILS
- 8. Then the ACTUAL SOLUTION of BIG DATA IS NAMED
- 9. A software framework for distributed processing of large datasets across large clusters of computers Large datasets Terabytes or petabytes of data Large clusters hundreds or thousands of nodes Open-source implementation for Google MAPREDUCE Based on a simple data model, anydatawillfit
- 10. 2005: Doug Cutting and Michael J. Cafarella and team developed Hadoop to support distribution for the Nutch search engine project. Doug named it after his son's toy elephant The project was funded by YAHOO 2006: Yahoo gave the project to APACHE SOFTWARE FOUNDATION.
- 11. WHO USES HADOOP?
- 12. Architecture of hdoop MapReduce HDFS Hdoop Common
- 13. A software frameawork for distributing computation of huge data. Consists of two main phases ◦ Map ◦ Reduce The Map Task: converts input into individually broken elements. The Reduce Task: takes the output from a map task as input and combines.
- 14. How MapReduce Works?? We Love India We 1 Love 1 Love 1 India 1 India 1 We 2 We Play Cricket We 1 Tennis 1 Play 1 Play 1 Tennis MAP REDUCE We Love India We Play Cricket
- 15. HDFS Distributed File system used by Hadoop is (HDFS). Based on the Google File System (GFS). Designed to run on thousands of clusters of small computers. HDFS uses a MASTERSLAVE ARCHITECTURE
- 16. Master node is called namenode. Slave node is called datanode. Master (Name Node) manages the file system metadata. Slave( DataNodes) store the actual data. A file in an HDFS is split into several blocks Blocks are stored in a set of DataNodes. NameNode the maps blocks to the DataNodes. The DataNodes takes care of read, write, creation and deletion operatons based on instruction given by NameNode.
- 17. Provides access to HDFS. Contains Java libraries and utilities Contains the necessary java files & scripts to start HADOOP.
- 18. ADVANTAGES OF HADOOP Designed to detect & handle failures. • Automation distribution of data across the machines. Doesn’t rely on hardware for fault tolerance. • Servers can be added or removed dynamically.
- 19. ANY QUERIES????