Big data is high-volume, high-velocity, and high-variety data that is difficult to process using traditional data management tools. It is characterized by 3Vs: volume of data is growing exponentially, velocity as data streams in real-time, and variety as data comes from many different sources and formats. The document discusses big data analytics techniques to gain insights from large and complex datasets and provides examples of big data sources and applications.
The document describes an experiment comparing three big data analysis platforms: Apache Hive, Apache Spark, and R. Seven identical analyses of clickstream data were performed on each platform, and the time taken to complete each operation was recorded. The results showed that Spark was faster for queries involving transformations of big data, while R was faster for operations involving actions on big data. The document provides details on the hardware, software, data, and specific analytical tasks used in the experiment.
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.
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
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 .
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.
This document introduces big data by defining it as large, complex datasets that cannot be processed by traditional methods due to their size. It explains that big data comes from sources like online activity, social media, science, and IoT devices. Examples are given of the massive scales of data produced each day. The challenges of processing big data with traditional databases and software are illustrated through a fictional startup example. The document argues that new tools and approaches are needed to handle automatic scaling, replication, and fault tolerance. It presents Apache Hadoop and Spark as open-source big data tools that can process petabytes of data across thousands of nodes through distributed and scalable architectures.
Big data is high-volume, high-velocity, and high-variety data that is difficult to process using traditional data management tools. It is characterized by 3Vs: volume of data is growing exponentially, velocity as data streams in real-time, and variety as data comes from many different sources and formats. The document discusses big data analytics techniques to gain insights from large and complex datasets and provides examples of big data sources and applications.
The document describes an experiment comparing three big data analysis platforms: Apache Hive, Apache Spark, and R. Seven identical analyses of clickstream data were performed on each platform, and the time taken to complete each operation was recorded. The results showed that Spark was faster for queries involving transformations of big data, while R was faster for operations involving actions on big data. The document provides details on the hardware, software, data, and specific analytical tasks used in the experiment.
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.
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
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 .
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.
This document introduces big data by defining it as large, complex datasets that cannot be processed by traditional methods due to their size. It explains that big data comes from sources like online activity, social media, science, and IoT devices. Examples are given of the massive scales of data produced each day. The challenges of processing big data with traditional databases and software are illustrated through a fictional startup example. The document argues that new tools and approaches are needed to handle automatic scaling, replication, and fault tolerance. It presents Apache Hadoop and Spark as open-source big data tools that can process petabytes of data across thousands of nodes through distributed and scalable architectures.
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.
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 .
This is a gentle introduction to Hadoop and Big Data in general. It shows how MapReduce and HDFS work along with various features of Data Warehousing and Big Data.
It also shows how Scalability works and how Cloud Technologies can be leveraged to achieve it.
The document provides an overview of data mining and data warehousing concepts. It defines data mining as the process of analyzing large amounts of data to identify patterns and establish relationships. A data warehouse is described as a centralized repository of integrated data from multiple sources organized by subject to support analysis and decision making. The document also outlines the typical three-tier architecture of data warehouses, including extraction of data from source systems, transformation of data in an OLAP server, and analysis of data using client tools.
This document provides an introduction to data mining and knowledge discovery. It discusses how we are collecting vast amounts of data from various sources, including business transactions, scientific data, medical records, digital media, and more. However, simply storing this data is not enough - we need tools to analyze and understand it. Data mining is the process of extracting useful patterns and knowledge from large data sets. It involves cleaning, transforming, and modeling the data to uncover hidden insights. The goal is to help organizations make better decisions by discovering patterns in their information.
This document provides an overview of big data. It begins by defining big data and noting that it first emerged in the early 2000s among online companies like Google and Facebook. It then discusses the three key characteristics of big data: volume, velocity, and variety. The document outlines the large quantities of data generated daily by companies and sensors. It also discusses how big data is stored and processed using tools like Hadoop and MapReduce. Examples are given of how big data analytics can be applied across different industries. Finally, the document briefly discusses some risks and benefits of big data, as well as its impact on IT jobs.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
Big data refers to large volumes of structured and unstructured data that are difficult to process using traditional database and software techniques. It encompasses the 3Vs - volume, velocity, and variety. Hadoop is an open-source framework that stores and processes big data across clusters of commodity servers using the MapReduce algorithm. It allows applications to work with huge amounts of data in parallel. Organizations use big data and analytics to gain insights for reducing costs, optimizing offerings, and making smarter decisions across industries like banking, government, and education.
What Is Hadoop? | What Is Big Data & Hadoop | Introduction To Hadoop | Hadoop...Simplilearn
This presentation about Hadoop will help you understand what is Big Data, what is Hadoop, how Hadoop came into existence, what are the various components of Hadoop and an explanation on Hadoop use case. In the current time, there is a lot of data being generated every day and this massive amount of data cannot be stored, processed and analyzed using the traditional ways. That is why Hadoop can into existence as a solution for Big Data. Hadoop is a framework that manages Big Data storage in a distributed way and processes it parallelly. Now, let us get started and understand the importance of Hadoop and why we actually need it.
Below topics are explained in this Hadoop presentation:
1. The rise of Big Data
2. What is Big Data?
3. Big Data and its challenges
4. Hadoop as a solution
5. What is Hadoop?
6. Components of Hadoop
7. Use case of Hadoop
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document discusses big data and data science. It begins with definitions of big data and what differentiates it from traditional small data. It then covers the motivation and state of the big data market, as well as techniques, tools, and data science approaches used for working with big data. The document provides examples of research areas involved and risks to consider when mining big data. It concludes by discussing opportunities for applying big data analysis.
This document provides an overview of the big data technology stack, including the data layer (HDFS, S3, GPFS), data processing layer (MapReduce, Pig, Hive, HBase, Cassandra, Storm, Solr, Spark, Mahout), data ingestion layer (Flume, Kafka, Sqoop), data presentation layer (Kibana), operations and scheduling layer (Ambari, Oozie, ZooKeeper), and concludes with a brief biography of the author.
10 Popular Hadoop Technical Interview QuestionsZaranTech LLC
The document discusses 10 common questions that may be asked in a Hadoop technical interview. It provides definitions for big data and the four V's of big data (volume, variety, veracity, velocity). It also discusses how businesses use big data analytics to increase revenue, examples of companies that use Hadoop, the difference between structured and unstructured data, the concepts that Hadoop works on (HDFS and MapReduce), core Hadoop components, hardware requirements for running Hadoop, common input formats, and some common Hadoop tools. Overall, the document outlines essential information about big data and Hadoop that may be helpful to review for a technical interview.
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.
Big Data Tutorial - Marko Grobelnik - 25 May 2012Marko Grobelnik
The document discusses big data, including what it is, key factors enabling its growth like increased storage and processing power, and techniques for handling big data like distributed processing and NoSQL databases. It provides examples of tools and applications for big data and discusses challenges like ensuring patterns found in big data analysis are actually meaningful.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Record linkage is used to identify records from different data sources that represent the same real-world entity. It involves preprocessing data, reducing the search space using blocking methods, computing similarity functions to compare records, and applying decision models to classify record pairs. A common blocking method is the sorted neighborhood method, which sorts records by a blocking key and compares nearby records within a fixed window. The effectiveness of record linkage depends on selecting good blocking keys and similarity functions.
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment 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. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade
ClusterW 2012 vHadoop: A Scalable Hadoop Virtual Cluster Platform for MapRedu...Kejiang Ye
The document proposes a scalable Hadoop virtual cluster platform called vHadoop for large-scale MapReduce-based parallel machine learning. It analyzes the static and dynamic performance of vHadoop and uses it to perform parallel clustering algorithms on two datasets. Experimental results show the performance of benchmarks like WordCount and TeraSort on vHadoop, as well as the overhead of live migrating the Hadoop virtual cluster. Various clustering algorithms including Canopy, k-Means and MeanShift are also implemented and visualized on a synthetic time series dataset.
This document discusses density-based clustering and the DBSCAN algorithm. It defines density-based clustering as clustering based on density, where clusters are defined as density-connected points. DBSCAN discovers clusters of arbitrary shape by finding core points that have many neighboring points within a given radius (Eps) and connecting nearby border and core points. The algorithm iterates through points, forming clusters from core points and labeling other points as border or noise. It works well for clusters of varying shapes but can fail on varying densities or high dimensions.
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.
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 .
This is a gentle introduction to Hadoop and Big Data in general. It shows how MapReduce and HDFS work along with various features of Data Warehousing and Big Data.
It also shows how Scalability works and how Cloud Technologies can be leveraged to achieve it.
The document provides an overview of data mining and data warehousing concepts. It defines data mining as the process of analyzing large amounts of data to identify patterns and establish relationships. A data warehouse is described as a centralized repository of integrated data from multiple sources organized by subject to support analysis and decision making. The document also outlines the typical three-tier architecture of data warehouses, including extraction of data from source systems, transformation of data in an OLAP server, and analysis of data using client tools.
This document provides an introduction to data mining and knowledge discovery. It discusses how we are collecting vast amounts of data from various sources, including business transactions, scientific data, medical records, digital media, and more. However, simply storing this data is not enough - we need tools to analyze and understand it. Data mining is the process of extracting useful patterns and knowledge from large data sets. It involves cleaning, transforming, and modeling the data to uncover hidden insights. The goal is to help organizations make better decisions by discovering patterns in their information.
This document provides an overview of big data. It begins by defining big data and noting that it first emerged in the early 2000s among online companies like Google and Facebook. It then discusses the three key characteristics of big data: volume, velocity, and variety. The document outlines the large quantities of data generated daily by companies and sensors. It also discusses how big data is stored and processed using tools like Hadoop and MapReduce. Examples are given of how big data analytics can be applied across different industries. Finally, the document briefly discusses some risks and benefits of big data, as well as its impact on IT jobs.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
Big data refers to large volumes of structured and unstructured data that are difficult to process using traditional database and software techniques. It encompasses the 3Vs - volume, velocity, and variety. Hadoop is an open-source framework that stores and processes big data across clusters of commodity servers using the MapReduce algorithm. It allows applications to work with huge amounts of data in parallel. Organizations use big data and analytics to gain insights for reducing costs, optimizing offerings, and making smarter decisions across industries like banking, government, and education.
What Is Hadoop? | What Is Big Data & Hadoop | Introduction To Hadoop | Hadoop...Simplilearn
This presentation about Hadoop will help you understand what is Big Data, what is Hadoop, how Hadoop came into existence, what are the various components of Hadoop and an explanation on Hadoop use case. In the current time, there is a lot of data being generated every day and this massive amount of data cannot be stored, processed and analyzed using the traditional ways. That is why Hadoop can into existence as a solution for Big Data. Hadoop is a framework that manages Big Data storage in a distributed way and processes it parallelly. Now, let us get started and understand the importance of Hadoop and why we actually need it.
Below topics are explained in this Hadoop presentation:
1. The rise of Big Data
2. What is Big Data?
3. Big Data and its challenges
4. Hadoop as a solution
5. What is Hadoop?
6. Components of Hadoop
7. Use case of Hadoop
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document discusses big data and data science. It begins with definitions of big data and what differentiates it from traditional small data. It then covers the motivation and state of the big data market, as well as techniques, tools, and data science approaches used for working with big data. The document provides examples of research areas involved and risks to consider when mining big data. It concludes by discussing opportunities for applying big data analysis.
This document provides an overview of the big data technology stack, including the data layer (HDFS, S3, GPFS), data processing layer (MapReduce, Pig, Hive, HBase, Cassandra, Storm, Solr, Spark, Mahout), data ingestion layer (Flume, Kafka, Sqoop), data presentation layer (Kibana), operations and scheduling layer (Ambari, Oozie, ZooKeeper), and concludes with a brief biography of the author.
10 Popular Hadoop Technical Interview QuestionsZaranTech LLC
The document discusses 10 common questions that may be asked in a Hadoop technical interview. It provides definitions for big data and the four V's of big data (volume, variety, veracity, velocity). It also discusses how businesses use big data analytics to increase revenue, examples of companies that use Hadoop, the difference between structured and unstructured data, the concepts that Hadoop works on (HDFS and MapReduce), core Hadoop components, hardware requirements for running Hadoop, common input formats, and some common Hadoop tools. Overall, the document outlines essential information about big data and Hadoop that may be helpful to review for a technical interview.
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.
Big Data Tutorial - Marko Grobelnik - 25 May 2012Marko Grobelnik
The document discusses big data, including what it is, key factors enabling its growth like increased storage and processing power, and techniques for handling big data like distributed processing and NoSQL databases. It provides examples of tools and applications for big data and discusses challenges like ensuring patterns found in big data analysis are actually meaningful.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Record linkage is used to identify records from different data sources that represent the same real-world entity. It involves preprocessing data, reducing the search space using blocking methods, computing similarity functions to compare records, and applying decision models to classify record pairs. A common blocking method is the sorted neighborhood method, which sorts records by a blocking key and compares nearby records within a fixed window. The effectiveness of record linkage depends on selecting good blocking keys and similarity functions.
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment 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. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade
ClusterW 2012 vHadoop: A Scalable Hadoop Virtual Cluster Platform for MapRedu...Kejiang Ye
The document proposes a scalable Hadoop virtual cluster platform called vHadoop for large-scale MapReduce-based parallel machine learning. It analyzes the static and dynamic performance of vHadoop and uses it to perform parallel clustering algorithms on two datasets. Experimental results show the performance of benchmarks like WordCount and TeraSort on vHadoop, as well as the overhead of live migrating the Hadoop virtual cluster. Various clustering algorithms including Canopy, k-Means and MeanShift are also implemented and visualized on a synthetic time series dataset.
This document discusses density-based clustering and the DBSCAN algorithm. It defines density-based clustering as clustering based on density, where clusters are defined as density-connected points. DBSCAN discovers clusters of arbitrary shape by finding core points that have many neighboring points within a given radius (Eps) and connecting nearby border and core points. The algorithm iterates through points, forming clusters from core points and labeling other points as border or noise. It works well for clusters of varying shapes but can fail on varying densities or high dimensions.
This document summarizes a lecture on clustering and provides a sample MapReduce implementation of K-Means clustering. It introduces clustering, discusses different clustering algorithms like hierarchical and partitional clustering, and focuses on K-Means clustering. It also describes Canopy clustering, which can be used as a preliminary step to partition large datasets and parallelize computation for K-Means clustering. The document then outlines the steps to implement K-Means clustering on large datasets using MapReduce, including selecting canopy centers, assigning points to canopies, and performing the iterative K-Means algorithm in parallel.
The document discusses several density-based and grid-based clustering algorithms. DBSCAN is described as a density-based method that forms clusters as maximal sets of density-connected points. OPTICS extends DBSCAN to produce a special ordering of the database with respect to density-based clustering structure. DENCLUE uses density functions to allow mathematically describing arbitrarily shaped clusters. Grid-based methods like STING, WaveCluster, and CLIQUE partition space into a grid structure to perform fast clustering.
This document summarizes the DBSCAN clustering algorithm. DBSCAN finds clusters based on density, requiring only two parameters: Eps, which defines the neighborhood distance, and MinPts, the minimum number of points required to form a cluster. It can discover clusters of arbitrary shape. The algorithm works by expanding clusters from core points, which have at least MinPts points within their Eps-neighborhood. Points that are not part of any cluster are classified as noise. Applications include spatial data analysis, image segmentation, and automatic border detection in medical images.
Cluster analysis is an unsupervised learning technique used to group unlabeled data points into meaningful clusters. There are several approaches to cluster analysis including partitioning methods like k-means, hierarchical clustering methods like agglomerative nesting (AGNES), and density-based methods like DBSCAN. The quality of clusters is evaluated based on intra-cluster similarity and inter-cluster dissimilarity. Cluster analysis has applications in fields like pattern recognition, image processing, and market segmentation.
06 how to write a map reduce version of k-means clusteringSubhas Kumar Ghosh
The document discusses how to write a MapReduce version of K-means clustering. It involves duplicating the cluster centers across nodes so each data point can be processed independently in the map phase. The map phase outputs (ClusterID, Point) pairs assigning each point to its closest cluster. The reduce phase groups by ClusterID and calculates the new centroid for each cluster, outputting (ClusterID, Centroid) pairs. Each iteration is run as a MapReduce job with the library determining if convergence is reached between iterations.
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The document discusses big data and MapReduce frameworks like Hadoop. It provides an overview of MapReduce and how it allows distributed processing of large datasets using simple map and reduce functions. The document also covers several common design patterns for MapReduce jobs, including filtering, sorting, joins, and computing statistics.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
This document provides a summary of MapReduce algorithms. It begins with background on the author's experience blogging about MapReduce algorithms in academic papers. It then provides an overview of MapReduce concepts including the mapper and reducer functions. Several examples of recently published MapReduce algorithms are described for tasks like machine learning, finance, and software engineering. One algorithm is examined in depth for building a low-latency key-value store. Finally, recommendations are provided for designing MapReduce algorithms including patterns, performance, and cost/maintainability considerations. An appendix lists additional MapReduce algorithms from academic papers in areas such as AI, biology, machine learning, and mathematics.
This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
A very categorized presentation about big data analytics Various topics like Introduction to Big Data,Hadoop,HDFS Map Reduce, Mahout,K-means Algorithm,H-Base are explained very clearly in simple language for everyone to understand easily.
This document discusses big data, where it comes from, and how it is processed and analyzed. It notes that everything we do online now leaves a digital trace as data. This "big data" includes huge volumes of structured, semi-structured, and unstructured data from various sources like social media, sensors, and the internet of things. Traditional computing cannot handle such large datasets, so technologies like MapReduce, Hadoop, HDFS, and NoSQL databases were developed to distribute the work across clusters of machines and process the data in parallel.
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.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
This document discusses the concept of big data. It defines big data as massive volumes of structured and unstructured data that are difficult to process using traditional database techniques due to their size and complexity. It notes that big data has the characteristics of volume, variety, and velocity. The document also discusses Hadoop as an implementation of big data and how various industries are generating large amounts of data.
Hadoop was born out of the need to process Big Data.Today data is being generated liked never before and it is becoming difficult to store and process this enormous volume and large variety of data, In order to cope this Big Data technology comes in.Today Hadoop software stack is go-to framework for large scale,data intensive storage and compute solution for Big Data Analytics Applications.The beauty of Hadoop is that it is designed to process large volume of data in clustered commodity computers work in parallel.Distributing the data that is too large across the nodes in clusters solves the problem of having too large data sets to be processed onto the single machine.
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.
The document discusses big data and its key characteristics known as the 5Vs: volume, velocity, variety, variability, and value. It provides examples of how different companies and industries deal with large volumes of data from various sources in real-time. Big data technologies like Hadoop, HDFS, MapReduce, Cassandra, and MongoDB are helping companies analyze and gain insights from both structured and unstructured data across industries like retail, finance, and social media. Data scientists use tools, techniques and programming languages to understand trends and patterns in large, complex data sets.
Big Data
Hadoop
NoSQL databases and type: column oriented,document oriented, map based.
Map-reduce Example
Bigdata Analytics Case study
Case Study R
Retail and Finance Case Study
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, OathYahoo Developer Network
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Gerenral insurance Accounts IT and Investmentvijayk23x
The document provides an overview of topics that may be covered in accounting, IT and investment exams, including:
1. The exam questions will be split between investment, IT, accounting standards and ratios, and preparation of financial accounts.
2. IT topics include storage units, network types, protocols, programming languages, databases, data warehousing concepts like data marts, operational data stores, and dimensional modeling techniques like star and snowflake schemas.
3. Key concepts in machine learning, deep learning, big data, data lakes and artificial intelligence are also defined.
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
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
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 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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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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Big Data Analytics(concepts of hadoop mapreduce,mahout,k-means clustering,hbase)
1.
2. There are some things that are so big that
they have implications for everyone,
whether we want it or not.
Big Data is one of those things, and is
completely transforming the way we do
business and is impacting most other
parts of our lives.
3.
4. From the dawn of civilization until
2003, humankind generated five
exabytes of data. Now we produce
five exabytes every two days…and
the pace is accelerating
5. Activity Data
Conversation Data
Photo and Video Image Data
Sensor Data
The Internet of Things Data
6. Simple activities like listening to music or
reading a book are now generating data.
Digital music players and eBooks collect data
on our activities. Your smart phone collects
data on how you use it and your web
browser collects information on what you are
searching for. Your credit card company
collects data on where you shop and your
shop collects data on what you buy. It is hard
to imagine any activity that does not
generate data.
7. Our conversations are now digitally recorded.
It all started with emails but nowadays most
of our conversations leave a digital trail. Just
think of all the conversations we have on
social media sites like Facebook orTwitter.
Even many of our phone conversations are
now digitally recorded.
8. Just think about all the pictures we take on
our smart phones or digital cameras.We
upload and share 100s of thousands of them
on social media sites every second.The
increasing amounts of CCTV cameras take
video images and we up-load hundreds of
hours of video images toYouTube and other
sites every minute .
9. We are increasingly surrounded by sensors
that collect and share data.Take your smart
phone, it contains a global positioning sensor
to track exactly where you are every second
of the day, it includes an accelerometer to
track the speed and direction at which you
are travelling.We now have sensors in many
devices and products.
10. We now have smartTVs that are able to
collect and process data, we have smart
watches, smart fridges, and smart alarms.
The Internet ofThings, or Internet of
Everything connects these devices so that
e.g. the traffic sensors on the road send data
to your alarm clock which will wake you up
earlier than planned because the blocked
road means you have to leave earlier to make
your 9a.m meeting…
12. …refers to the vast amounts of data
generated every second.We are not talking
Terabytes but Zettabytes or Brontobytes. If
we take all the data generated in the world
between the beginning of time and 2008, the
same amount of data will soon be generated
every minute. New big data tools use
distributed systems so that we can store and
analyse data across databases that are dotted
around anywhere in the world.
13. …refers to the speed at which new data is
generated and the speed at which data
moves around. Just think of social media
messages going viral in seconds.Technology
allows us now to analyse the data while it is
being generated (sometimes referred to as
in-memory analytics), without ever putting it
into databases.
14. …refers to the different types of data we can
now use. In the past we only focused on
structured data that neatly fitted into tables or
relational databases, such as financial data. In
fact, 80% of the world’s data is unstructured
(text, images, video, voice, etc.)With big data
technology we can now analyse and bring
together data of different types such as
messages, social media conversations, photos,
sensor data, video or voice recordings.
15. …refers to the messiness or trustworthiness
of the data.With many forms of big data
quality and accuracy are less controllable
(just think ofTwitter posts with hash tags,
abbreviations, typos and colloquial speech as
well as the reliability and accuracy of content)
but technology now allows us to work with
this type of data.
16. LOGISTICAPPROACH OF BIG DATA FOR
CATEGORIZINGTECHNICAL SUPPORT
REQUESTS USING HADOOP AND MAHOUT
COMPONENTS.
17.
18. Social Media
Machine Log
Call Center Logs
Email
Financial Services transactions.
20. Revolution has created a series of
“RevoConnectRs for Hadoop” that will allow an
R programmer to manipulate Hadoop data
stores directly from HDFS and HBASE, and give
R programmers the ability to write MapReduce
jobs in R using Hadoop Streaming. RevoHDFS
provides connectivity from R to HDFS and
RevoHBase provides connectivity from R to
HBase. Additionally, RevoHStream allows
MapReduce jobs to be developed in R and
executed as Hadoop Streaming jobs.
21.
22. HDFS can be presented as a master/slave
architecture.Namenode is treated as master and
datanode the slave.Namenode is the server that
manages the filesystem namespace and adjust
the access to files by the client.It divides the
input data into blocks and announces which data
block will be stored in which datanode.Datanode
is the slave machine that stores the replicas of
the partition datasets and serves the data as the
request comes.It also performs block creation
and deletion
23. HDFS is managed with the master/slave
architecture included with the following
components:-
NAMENODE:-This is the master of the HDFS
system. It maintains the metadata and manages
the blogs that are present on datanodes.
DATANODE:-These are slaves that are deployed
on each machine and provide actual
storage.They are responsible for serving read
and write data request for the clients
24.
25. Map-reduce is a programming model for
processing and generating large datasets
.Users specify a map function that processes
a key value pair to generate a set of
intermediate key value pairs .
map(key1,value) -> list<key2,value2>
The reduce function that merges all
intermediate values associated with the same
intermediate key.
reduce(key2, list<value2>) -> list<value3>
26. The important innovation of map-reduce is the
ability to take a query over a dataset,divide it
,and run it in parallel over multiple nodes.
Distributing the computation solves the issue of
data too large to fit
onto a single machine. Combine this technique
with commodity Linux
servers and you have a cost-effective alternative
to massive computing
arrays.The advantage of map-reduce model is its
simplicity because only Map() and Reduce() to
be written by user.
27. Every organization’s data are diverse and particular to
their needs. However, there is much less diversity in the
kinds of analyses performed on that data.The Mahout
project is a library of Hadoop implementations of
common analytical computations. Use cases include user
collaborative filtering,user recommendations, clustering
and classification.
Mahout is an open source machine learning library built on
top of Hadoop to provide distributed analytics capabilities.
Mahout incorporates a wide range of data mining
techniques including collaborative filtering, classification
and clustering algorithms.
30. Clustering is the process of partitioning a group of data points into
a small number of clusters. For instance, the items in a
supermarket are clustered in categories (butter, cheese and milk
are grouped in dairy products). Of course this is a qualitative kind
of partitioning. A quantitative approach would be to measure
certain features of the products, say percentage of milk and
others, and products with high percentage of milk would be
grouped together. In general, we have n data points xi,i=1...nthat
have to be partitioned in k clusters.The goal is to assign a cluster
to each data point. K-means is a clustering method that aims to
find the positions ci,i=1...k of the clusters that minimize
the distance from the data points to the cluster. K-means
clustering solves
31.
32. There are several layers that sit on top of HDFS that
also provide additional capabilities and make working
with HDFS easier.One such implementation is
HBASE, Hadoop’s answer to providing database like
table structures.
Just like being able to work with HDFS from inside R,
access to HBASE helps open up the Hadoop
framework to the R programmer.Although R may not
be able to load a billion row-by-million-
column table, working with smaller subsets to
perform adhoc analysis can help lead to solutions that
work with the entire data set.
The H-Base data structure is based on LSMTrees.
33. The Log-Structured MergeTree:
The Log-Structured Merge-Tree (or LSM tree) is
a data structure with performance characteristics
that make it attractive for
providing indexed access to files with high insert
volume, such as transactional log data.
LSM trees, like other search trees, maintain key-value
pairs. LSM trees maintain data in two or more separate
structures, each of which is optimized for its respective
underlying storage medium.
34. All puts (insertions) are
appended to a write ahead
log (can be done fast on
HDFS, can be used to
restore the database in
case anything goes wrong)
An in memory data
structure (MemStore)
stores the most
recent puts (fast and
ordered)
From time to time
MemStore is flushed to
disk.
35. This results in a many small
files on HDFS.
HDFS better works with few
large files instead of many
small ones.
A get or scan potentially has
to look into all small files. So
fast random reads are not
possible as described so far.
That is why H-Base
constantly checks if it is
necessary to combine several
small files into one larger one
This process is called
compaction
36. There are two different kinds of compactions.
Minor Compactions merge few small ordered
files into one larger ordered one without
touching the data.
Major Compactions merge all files into one
file. During this process outdated or deleted
values are removed.
Bloom Filters (stored in the Metadata of the
files on HDFS) can be used for a fast exclusion
of files when looking for a specific key.
37. Every entry in a Table is indexed
by a RowKey
For every RowKey an unlimited
number of attributes can be
stored in Columns
There is no strict schema with
respect to the Columns.
New Columns can be added
during runtime
H-Base Tables are sparse.A
missing value doesn’t need any
space
Different versions can be stored
for every attribute. Each with a
different Timestamp.
Once a value is written to H-
Base it cannot be changed.
Instead another version with a
more recent Timestamp can be
added.
38. To delete a value from H-Base
a Tombstone value has to be
added.
The Columns are grouped
into ColumnFamilies.The Colum
nFamilies have to be defined at
table creation time and can’t be
changed afterwards.
H-Base is a distributed system. It
is guaranteed that
all values belonging to the
same RowKey and
ColumnFamily are stored
together.
39. Alternatively HBase can also be seen as a sparse,
multidimensional, sorted map with the following
structure:
(Table, RowKey, ColumnFamily, Column, Time
stamp) → Value
Or in an object oriented way:
Table ← SortedMap<RowKey, Row>
Row ← List<ColumnFamily>
ColumnFamily ← SortedMap<Column,
List<Entry>>
Entry ←Tuple<Timestamp,Value>
40. HBase supports the following operations:
Get: Returns the values for a given RowKey. Filters can
be used to restrict the results to specific
ColumnFamilies, Columns or versions.
Put: Adds a new entry.TheTimestamp can be set
automatically or manually.
Scan: Returns the values for a range of
RowKeys. Scans are very efficient in HBase. Filters can
also be used to narrow down the results. HBase 0.98.0
(which was released last week) also allows
backward scans.
Delete: Adds aTombstone marker.
41. HBase is a distributed database
The data is partitioned based on the
RowKeys into Regions.
Each Region contains a range of
RowKeys based on their binary
order.
A RegionServer can contain several
Regions.
All Regions contained in a
RegionServer share one write ahead
log (WAL).
Regions are automatically split if
they become too large.
Every Region creates a Log-
Structured MergeTree for every
ColumnFamily.That’s why fine
tuning like compression can be done
on ColumnFamily level.This should
be considered when defining the
ColumnFamilies.
42. HBase uses ZooKeeper to manage all
required services.
The assignment of Regions to
RegionServers and the splitting of Regions
is managed by a separate service, the
HMaster
The ROOT and the META table are two
special kinds of HBase tables which are
used for efficiently identifying which
RegionServer is responsible for a specific
RowKey in case of a read or write request.
When performing a get or scan, the client
asks ZooKeeper where to find the ROOT
Table.Then the client asks the ROOTTable
for the correct METATable. Finally it can
ask the METATable for the correct
RegionServer.
The client stores information about ROOT
and METATables to speed up future
lookups.
Using these three layers is efficient for a
practically unlimited number of
RegionServers.
43. Does HBase fulfill all “new” requirements?
Volume: By adding new servers to the cluster
HBase scales horizontally to an arbitrary amount
of data.
Variety:The sparse and flexible table structure is
optimal for multi-structured data. Only the
ColumnFamilies have to be predefined.
Velocity: HBase scales horizontally to read or
write requests of arbitrary speed by adding new
servers.The key to this is the LSM-Tree
Structure.