Amortized analysis allows analyzing the average performance of a sequence of operations on a data structure, even if some operations are expensive. There are three main methods for amortized analysis: aggregate analysis, accounting method, and potential method.
The accounting method assigns differing amortized costs to operations. When the amortized cost is higher than actual cost, the difference is stored as credit. Later operations may use accumulated credits when their amortized cost is lower than actual cost.
The potential method associates potential energy with the data structure as a whole. The amortized cost of an operation is the actual cost plus the change in potential. If potential never decreases, the total amortized cost bounds the total
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
The document discusses run-time environments in compiler design. It provides details about storage organization and allocation strategies at run-time. Storage is allocated either statically at compile-time, dynamically from the heap, or from the stack. The stack is used to store procedure activations by pushing activation records when procedures are called and popping them on return. Activation records contain information for each procedure call like local variables, parameters, and return values.
There's a big shift in both at the architecture and api level from Hadoop 1 vs Hadoop 2, particularly YARN and we had our first meetup to talk about this (http://www.meetup.com/Atlanta-YARN-User-Group/) on 10/13/2013.
Clustering is the process of grouping similar objects together. It allows data to be analyzed and summarized. There are several methods of clustering including partitioning, hierarchical, density-based, grid-based, and model-based. Hierarchical clustering methods are either agglomerative (bottom-up) or divisive (top-down). Density-based methods like DBSCAN and OPTICS identify clusters based on density. Grid-based methods impose grids on data to find dense regions. Model-based clustering uses models like expectation-maximization. High-dimensional data can be clustered using subspace or dimension-reduction methods. Constraint-based clustering allows users to specify preferences.
The document discusses different clustering techniques used for grouping large amounts of data. It covers partitioning methods like k-means and k-medoids that organize data into exclusive groups. It also describes hierarchical methods like agglomerative and divisive clustering that arrange data into nested groups or trees. Additionally, it mentions density-based and grid-based clustering and provides algorithms for different clustering approaches.
This document discusses directory structures and file system mounting in operating systems. It describes several types of directory structures including single-level, two-level, hierarchical, tree, and acyclic graph structures. It notes that directories organize files in a hierarchical manner and that mounting makes storage devices available to the operating system by reading metadata about the filesystem. Mounting attaches an additional filesystem to the currently accessible filesystem, while unmounting disconnects the filesystem.
Amortized analysis allows analyzing the average performance of a sequence of operations on a data structure, even if some operations are expensive. There are three main methods for amortized analysis: aggregate analysis, accounting method, and potential method.
The accounting method assigns differing amortized costs to operations. When the amortized cost is higher than actual cost, the difference is stored as credit. Later operations may use accumulated credits when their amortized cost is lower than actual cost.
The potential method associates potential energy with the data structure as a whole. The amortized cost of an operation is the actual cost plus the change in potential. If potential never decreases, the total amortized cost bounds the total
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
The document discusses run-time environments in compiler design. It provides details about storage organization and allocation strategies at run-time. Storage is allocated either statically at compile-time, dynamically from the heap, or from the stack. The stack is used to store procedure activations by pushing activation records when procedures are called and popping them on return. Activation records contain information for each procedure call like local variables, parameters, and return values.
There's a big shift in both at the architecture and api level from Hadoop 1 vs Hadoop 2, particularly YARN and we had our first meetup to talk about this (http://www.meetup.com/Atlanta-YARN-User-Group/) on 10/13/2013.
Clustering is the process of grouping similar objects together. It allows data to be analyzed and summarized. There are several methods of clustering including partitioning, hierarchical, density-based, grid-based, and model-based. Hierarchical clustering methods are either agglomerative (bottom-up) or divisive (top-down). Density-based methods like DBSCAN and OPTICS identify clusters based on density. Grid-based methods impose grids on data to find dense regions. Model-based clustering uses models like expectation-maximization. High-dimensional data can be clustered using subspace or dimension-reduction methods. Constraint-based clustering allows users to specify preferences.
The document discusses different clustering techniques used for grouping large amounts of data. It covers partitioning methods like k-means and k-medoids that organize data into exclusive groups. It also describes hierarchical methods like agglomerative and divisive clustering that arrange data into nested groups or trees. Additionally, it mentions density-based and grid-based clustering and provides algorithms for different clustering approaches.
This document discusses directory structures and file system mounting in operating systems. It describes several types of directory structures including single-level, two-level, hierarchical, tree, and acyclic graph structures. It notes that directories organize files in a hierarchical manner and that mounting makes storage devices available to the operating system by reading metadata about the filesystem. Mounting attaches an additional filesystem to the currently accessible filesystem, while unmounting disconnects the filesystem.
Coda (Constant Data Avaialabilty) is a distributed file system developed at Carnegie Mellon University . This presentation explains how it works and different aspects of it.
Hadoop is a distributed processing framework for large datasets. It utilizes HDFS for storage and MapReduce as its programming model. The Hadoop ecosystem has expanded to include many other tools. YARN was developed to address limitations in the original Hadoop architecture. It provides a common platform for various data processing engines like MapReduce, Spark, and Storm. YARN improves scalability, utilization, and supports multiple workloads by decoupling cluster resource management from application logic. It allows different applications to leverage shared Hadoop cluster resources.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
This document provides an introduction to parallel computing. It discusses serial versus parallel computing and how parallel computing involves simultaneously using multiple compute resources to solve problems. Common parallel computer architectures involve multiple processors on a single computer or connecting multiple standalone computers together in a cluster. Parallel computers can use shared memory, distributed memory, or hybrid memory architectures. The document outlines some of the key considerations and challenges in moving from serial to parallel code such as decomposing problems, identifying dependencies, mapping tasks to resources, and handling dependencies.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
The Connection Machine (CM) was a massively parallel supercomputer architecture developed in the 1980s and 1990s. It featured up to 65,536 simple processing elements connected via a hypercube network (CM-1, CM-2) or fat tree network (CM-5). The CM was developed by Thinking Machines Corporation and funded by DARPA to support neural network and artificial intelligence applications. The CM-5 in particular used off-the-shelf SPARC processors and custom vector coprocessors with a separate control network to coordinate work across the parallel system. The CM architecture pioneered many concepts still used in parallel computing today.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
The document describes the backtracking method for solving problems that require finding optimal solutions. Backtracking involves building a solution one component at a time and using bounding functions to prune partial solutions that cannot lead to an optimal solution. It then provides examples of applying backtracking to solve the 8 queens problem by placing queens on a chessboard with no attacks. The general backtracking method and a recursive backtracking algorithm are also outlined.
Lazy learning is a machine learning method where generalization of training data is delayed until a query is made, unlike eager learning which generalizes before queries. K-nearest neighbors and case-based reasoning are examples of lazy learners, which store training data and classify new data based on similarity. Case-based reasoning specifically stores prior problem solutions to solve new problems by combining similar past case solutions.
1. First-order logic uses quantifiers like ∀ (for all) and ∃ (there exists) to make general statements about objects in a domain.
2. ∀ statements are true if the statement holds for all possible objects, while ∃ statements are true if the statement holds for at least one object.
3. Logical statements can include multiple nested quantifiers to express more complex relationships between objects.
The document discusses the structure of file systems. It explains that a file system provides mechanisms for storing and accessing files and data. It uses a layered approach, with each layer responsible for specific tasks related to file management. The logical file system contains metadata and verifies permissions and paths. It maps logical file blocks to physical disk blocks using a file organization module, which also manages free space. The basic file system then issues I/O commands to access those physical blocks via device drivers, with I/O controls handling interrupts.
This document discusses multimedia data mining. It describes how multimedia data mining focuses on mining image, audio, and video data. Some key techniques discussed include similarity search to find similar multimedia objects, multidimensional analysis of multimedia data cubes, classification and prediction of multimedia data, and mining associations within and between multimedia objects.
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
The document discusses three common multithreading models: many-to-one, one-to-one, and many-to-many. It also outlines some high-level program structures for multithreaded programs like boss/workers, pipeline, up-calls, and using version stamps.
Buffer is a region of memory that temporarily holds data during transfer between devices or processes. There are several buffering techniques used in operating systems, including single buffering where a single buffer holds data during transfer, and double buffering where two buffers are used so one can be filled while the other is emptied. Block buffering reserves multiple buffers in memory to speed up transferring multiple blocks from disk to memory in parallel with CPU processing.
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
The document discusses distributed query processing and optimization in distributed database systems. It covers topics like query decomposition, distributed query optimization techniques including cost models, statistics collection and use, and algorithms for query optimization. Specifically, it describes the process of optimizing queries distributed across multiple database fragments or sites including generating the search space of possible query execution plans, using cost functions and statistics to pick the best plan, and examples of deterministic and randomized search strategies used.
This document discusses NoSQL and the CAP theorem. It begins with an introduction of the presenter and an overview of topics to be covered: What is NoSQL and the CAP theorem. It then defines NoSQL, provides examples of major NoSQL categories (document, graph, key-value, and wide-column stores), and explains why NoSQL is used, including to handle large, dynamic, and distributed data. The document also explains the CAP theorem, which states that a distributed data store can only satisfy two of three properties: consistency, availability, and partition tolerance. It provides examples of how to choose availability over consistency or vice versa. Finally, it concludes that both SQL and NoSQL have valid use cases and a combination
Hadoop DFS consists of HDFS for storage and MapReduce for processing. HDFS provides massive storage, fault tolerance through data replication, and high throughput access to data. It uses a master-slave architecture with a NameNode managing the file system namespace and DataNodes storing file data blocks. The NameNode ensures data reliability through policies that replicate blocks across racks and nodes. HDFS provides scalability, flexibility and low-cost storage of large datasets.
MapReduce facilitates concurrent processing by splitting petabytes of data into smaller chunks, and processing them in parallel on Hadoop commodity servers.
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
Coda (Constant Data Avaialabilty) is a distributed file system developed at Carnegie Mellon University . This presentation explains how it works and different aspects of it.
Hadoop is a distributed processing framework for large datasets. It utilizes HDFS for storage and MapReduce as its programming model. The Hadoop ecosystem has expanded to include many other tools. YARN was developed to address limitations in the original Hadoop architecture. It provides a common platform for various data processing engines like MapReduce, Spark, and Storm. YARN improves scalability, utilization, and supports multiple workloads by decoupling cluster resource management from application logic. It allows different applications to leverage shared Hadoop cluster resources.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
This document provides an introduction to parallel computing. It discusses serial versus parallel computing and how parallel computing involves simultaneously using multiple compute resources to solve problems. Common parallel computer architectures involve multiple processors on a single computer or connecting multiple standalone computers together in a cluster. Parallel computers can use shared memory, distributed memory, or hybrid memory architectures. The document outlines some of the key considerations and challenges in moving from serial to parallel code such as decomposing problems, identifying dependencies, mapping tasks to resources, and handling dependencies.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
The Connection Machine (CM) was a massively parallel supercomputer architecture developed in the 1980s and 1990s. It featured up to 65,536 simple processing elements connected via a hypercube network (CM-1, CM-2) or fat tree network (CM-5). The CM was developed by Thinking Machines Corporation and funded by DARPA to support neural network and artificial intelligence applications. The CM-5 in particular used off-the-shelf SPARC processors and custom vector coprocessors with a separate control network to coordinate work across the parallel system. The CM architecture pioneered many concepts still used in parallel computing today.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
The document describes the backtracking method for solving problems that require finding optimal solutions. Backtracking involves building a solution one component at a time and using bounding functions to prune partial solutions that cannot lead to an optimal solution. It then provides examples of applying backtracking to solve the 8 queens problem by placing queens on a chessboard with no attacks. The general backtracking method and a recursive backtracking algorithm are also outlined.
Lazy learning is a machine learning method where generalization of training data is delayed until a query is made, unlike eager learning which generalizes before queries. K-nearest neighbors and case-based reasoning are examples of lazy learners, which store training data and classify new data based on similarity. Case-based reasoning specifically stores prior problem solutions to solve new problems by combining similar past case solutions.
1. First-order logic uses quantifiers like ∀ (for all) and ∃ (there exists) to make general statements about objects in a domain.
2. ∀ statements are true if the statement holds for all possible objects, while ∃ statements are true if the statement holds for at least one object.
3. Logical statements can include multiple nested quantifiers to express more complex relationships between objects.
The document discusses the structure of file systems. It explains that a file system provides mechanisms for storing and accessing files and data. It uses a layered approach, with each layer responsible for specific tasks related to file management. The logical file system contains metadata and verifies permissions and paths. It maps logical file blocks to physical disk blocks using a file organization module, which also manages free space. The basic file system then issues I/O commands to access those physical blocks via device drivers, with I/O controls handling interrupts.
This document discusses multimedia data mining. It describes how multimedia data mining focuses on mining image, audio, and video data. Some key techniques discussed include similarity search to find similar multimedia objects, multidimensional analysis of multimedia data cubes, classification and prediction of multimedia data, and mining associations within and between multimedia objects.
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
The document discusses three common multithreading models: many-to-one, one-to-one, and many-to-many. It also outlines some high-level program structures for multithreaded programs like boss/workers, pipeline, up-calls, and using version stamps.
Buffer is a region of memory that temporarily holds data during transfer between devices or processes. There are several buffering techniques used in operating systems, including single buffering where a single buffer holds data during transfer, and double buffering where two buffers are used so one can be filled while the other is emptied. Block buffering reserves multiple buffers in memory to speed up transferring multiple blocks from disk to memory in parallel with CPU processing.
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
The document discusses distributed query processing and optimization in distributed database systems. It covers topics like query decomposition, distributed query optimization techniques including cost models, statistics collection and use, and algorithms for query optimization. Specifically, it describes the process of optimizing queries distributed across multiple database fragments or sites including generating the search space of possible query execution plans, using cost functions and statistics to pick the best plan, and examples of deterministic and randomized search strategies used.
This document discusses NoSQL and the CAP theorem. It begins with an introduction of the presenter and an overview of topics to be covered: What is NoSQL and the CAP theorem. It then defines NoSQL, provides examples of major NoSQL categories (document, graph, key-value, and wide-column stores), and explains why NoSQL is used, including to handle large, dynamic, and distributed data. The document also explains the CAP theorem, which states that a distributed data store can only satisfy two of three properties: consistency, availability, and partition tolerance. It provides examples of how to choose availability over consistency or vice versa. Finally, it concludes that both SQL and NoSQL have valid use cases and a combination
Hadoop DFS consists of HDFS for storage and MapReduce for processing. HDFS provides massive storage, fault tolerance through data replication, and high throughput access to data. It uses a master-slave architecture with a NameNode managing the file system namespace and DataNodes storing file data blocks. The NameNode ensures data reliability through policies that replicate blocks across racks and nodes. HDFS provides scalability, flexibility and low-cost storage of large datasets.
MapReduce facilitates concurrent processing by splitting petabytes of data into smaller chunks, and processing them in parallel on Hadoop commodity servers.
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
Introduction to map reduce s. jency jayastina II MSC COMPUTER SCIENCE BON SEC...jencyjayastina
The document discusses MapReduce, a programming model for processing large datasets in parallel across a distributed cluster. It describes how MapReduce works by specifying computation in terms of mapping and reducing functions. The underlying runtime system automatically parallelizes the computation, handles failures and communications. MapReduce is the processing engine of Apache Hadoop, which was derived from Google's MapReduce. It allows processing huge amounts of data through mapping and reducing steps. The mapping step converts data into key-value pairs, while the reducing step combines the output of mapping into smaller tuples. MapReduce is mainly used for parallel processing of large datasets stored in Hadoop clusters.
The document provides an overview of developing a big data strategy. It discusses defining a big data strategy by identifying opportunities and economic value of data, defining a big data architecture, selecting technologies, understanding data science, developing analytics, and institutionalizing big data. A good strategy explores these subject domains and aligns them to organizational objectives to accomplish a data-driven vision and direct the organization.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It has four main modules - Hadoop Common, HDFS, YARN and MapReduce. HDFS provides a distributed file system that stores data reliably across commodity hardware. MapReduce is a programming model used to process large amounts of data in parallel. Hadoop architecture uses a master-slave model, with a NameNode master and DataNode slaves. It provides fault tolerance, high throughput access to application data and scales to thousands of machines.
Hadoop is an open-source framework that uses clusters of commodity hardware to store and process big data using the MapReduce programming model. It consists of four main components: MapReduce for distributed processing, HDFS for storage, YARN for resource management and scheduling, and common utilities. HDFS stores large files as blocks across nodes for fault tolerance. MapReduce jobs are split into map and reduce phases to process data in parallel. YARN schedules resources and manages job execution. The common utilities provide libraries and scripts used by all Hadoop components. Major companies use Hadoop to analyze large amounts of data.
Hadoop is a framework for distributed storage and processing of large datasets across clusters of computers. It addresses problems like hardware failure and combining data after analysis. The core components are HDFS for distributed storage and MapReduce for distributed processing. HDFS stores data as blocks across nodes and handles replication for reliability. The Namenode manages the file system namespace and metadata, while Datanodes store and retrieve blocks. Hadoop supports reliable analysis of large datasets in a distributed manner through its scalable architecture.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
The document provides an overview of distributed systems and the Hadoop framework. It defines distributed systems as collections of interconnected computers that work together to achieve a common goal. Hadoop is introduced as an open-source distributed processing framework for massive datasets. Key components of Hadoop include HDFS for storage, YARN for resource management, MapReduce for processing, and common utilities. The document also explains how Hadoop works and its features such as scalability, fault tolerance, and flexible data processing.
This document discusses Hadoop and its core components HDFS and MapReduce. It provides an overview of how Hadoop addresses the challenges of big data by allowing distributed processing of large datasets across clusters of computers. Key points include: Hadoop uses HDFS for distributed storage and MapReduce for distributed processing; HDFS works on a master-slave model with a Namenode and Datanodes; MapReduce utilizes a map and reduce programming model to parallelize tasks. Fault tolerance is built into Hadoop to prevent single points of failure.
This document provides an overview of Hadoop and how it addresses the challenges of big data. It discusses how Hadoop uses a distributed file system (HDFS) and MapReduce programming model to allow processing of large datasets across clusters of computers. Key aspects summarized include how HDFS works using namenodes and datanodes, how MapReduce leverages mappers and reducers to parallelize processing, and how Hadoop provides fault tolerance.
This document provides an overview of Hadoop and how it addresses the challenges of big data. It discusses how Hadoop uses a distributed file system (HDFS) and MapReduce programming model to allow processing of large datasets across clusters of computers. Key aspects summarized include how HDFS works using namenodes and datanodes, how MapReduce leverages mappers and reducers to parallelize processing, and how Hadoop provides fault tolerance.
The document provides an overview of Apache Hadoop and how it addresses challenges related to big data. It discusses how Hadoop uses HDFS to distribute and store large datasets across clusters of commodity servers and uses MapReduce as a programming model to process and analyze the data in parallel. The core components of Hadoop - HDFS for storage and MapReduce for processing - allow it to efficiently handle large volumes and varieties of data across distributed systems in a fault-tolerant manner. Major companies have adopted Hadoop to derive insights from their big data.
This document discusses a proposed data-aware caching framework called Dache that could be used with big data applications built on MapReduce. Dache aims to cache intermediate data generated during MapReduce jobs to avoid duplicate computations. When tasks run, they would first check the cache for existing results before running the actual computations. The goal is to improve efficiency by reducing redundant work. The document outlines the objectives and scope of extending MapReduce with Dache, provides background on MapReduce and Hadoop, and concludes that initial experiments show Dache can eliminate duplicate tasks in incremental jobs.
Hadoop eco system with mapreduce hive and pigKhanKhaja1
This document provides an overview of MapReduce architecture and components. It discusses how MapReduce processes data using map and reduce tasks on key-value pairs. The JobTracker manages jobs by scheduling tasks on TaskTrackers. Data is partitioned and sorted during the shuffle and sort phase before being processed by reducers. Components like Hive, Pig, partitions, combiners, and HBase are described in the context of how they integrate with and optimize MapReduce processing.
Apache Hadoop is an open source software framework used to develop data processing applications which are executed in a distributed computing environment
Applications built using HADOOP are run on large data sets distributed across clusters of commodity computers. Commodity computers are cheap and widely available. These are mainly useful for achieving greater computational power at low cost.
This document provides information about Hadoop and its components. It discusses the history of Hadoop and how it has evolved over time. It describes key Hadoop components including HDFS, MapReduce, YARN, and HBase. HDFS is the distributed file system of Hadoop that stores and manages large datasets across clusters. MapReduce is a programming model used for processing large datasets in parallel. YARN is the cluster resource manager that allocates resources to applications. HBase is the Hadoop database that provides real-time random data access.
The document discusses cloud computing systems and MapReduce. It provides background on MapReduce, describing how it works and how it was inspired by functional programming concepts like map and reduce. It also discusses some limitations of MapReduce, noting that it is not designed for general-purpose parallel processing and can be inefficient for certain types of workloads. Alternative approaches like MRlite and DCell are proposed to provide more flexible and efficient distributed processing frameworks.
Similar to Hadoop, mapreduce and yarn networks (20)
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
1. Paper name : Big Data Analytics
Staff : Mrs M. Florence Dayana M. C. A., M.Phil., (Ph.D.)
Class : II- M.Sc.(Computer Science)
Semester : IV
Unit : V
Topic : Hadoop, MapReduce and YARN Frameworks
3. MAPREDUCE:
• MapReduce is a framework which can be used to write
applications to process very large amount of data, in parallel,
on large clusters of hardware in a reliable manner.
• MapReduce is a processing technique and a program model
for distributed computing systems that are based on java.
• The MapReduce algorithm is based on two important tasks.
They are Map and Reduce.
• Map takes a set of data and converts it into another set of
data, where the individual elements are broken down into
tuples.
• Reduce takes the output from a map as an input and
combines those data tuples into a smaller set of tuples.
6. Map stage :
The map’s job is to process the input data.
Generally the input data will be in the form of file or
directory and usually it is stored in the Hadoop
distributed file system (HDFS). The input file is passed to
the mapper function as each line. The mapper processes
the data and creates several small parts of data.
Reduce stage :
Reduce stage is the combination of the Shuffle
stage and the Reduce stage. Its main job is to process the
data that comes from the mapper. After processing, it
produces a another set of output, which will be stored in
the Hadoop distributed file system(HDFS).
7. YARN Framework:
• YARN stands for Yet Another Resource Manager
• It takes programming to the next level beyond
Java. YARN makes it interactive to let another
application HBase, Spark etc. to work on it.
• Different Yarn applications can co-exist with the
same cluster so MapReduce, HBase, Spark all can
run at the same time.
• Thus, it can bring a great benefits for
manageability and cluster utilization.
8.
9. SERIALIZATION:
• The process of translating structure of an data or
objects state into binary or textual form to transport
the data over network or to store on some persistent
storage is known as serialization.
• When the data is transported over network or
retrieved from the persistent storage, it needs to be
de-serialized again and vice versa.
• The process of serialization is termed
as marshalling.
• The process of deserialization is termed
as unmarshalling.