Hadoop Architecture
3 likes3,699 views
This presentation discusses the following topics: Introduction Components of Hadoop MapReduce Map Task Reduce Task Anatomy of a Map Reduce
1 of 11
Ad
Recommended
Electronic health records
Electronic health recordsDr.Senthilnayaki Ramasubbu This document discusses electronic health records (EHRs) and their components and benefits. It describes how EHRs contain comprehensive patient health information that can be shared electronically. Key parts of an EHR include clinical decision support systems, computerized physician order entry systems, and health information exchange capabilities. The implementation of EHRs can improve patient safety, enhance clinical outcomes, and reduce healthcare costs through increased efficiency and avoidance of errors. However, barriers to adoption include financial costs, workflow changes, and privacy/security concerns.
HDFS Architecture
HDFS ArchitectureJeff Hammerbacher The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.
Compiler Design Unit 1
Compiler Design Unit 1Jena Catherine Bel D This document provides an overview of compilers, including their history, components, and construction. It discusses the need for compilers to translate high-level programming languages into machine-readable code. The key phases of a compiler are described as scanning, parsing, semantic analysis, intermediate code generation, optimization, and code generation. Compiler construction relies on tools like scanner and parser generators.
Hadoop Overview & Architecture 
Hadoop Overview & Architecture EMC This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
Introduction to Recurrent Neural Network
Introduction to Recurrent Neural NetworkKnoldus Inc. The document provides an introduction to recurrent neural networks (RNNs). It discusses how RNNs differ from feedforward neural networks in that they have internal memory and can use their output from the previous time step as input. This allows RNNs to process sequential data like time series. The document outlines some common RNN types and explains the vanishing gradient problem that can occur in RNNs due to multiplication of small gradient values over many time steps. It discusses solutions to this problem like LSTMs and techniques like weight initialization and gradient clipping.
Spyware powerpoint
Spyware powerpointgalaxy201 Spyware refers to programs that use your internet connection to send information from your personal computer to another computer without your knowledge or permission. This information can include browsing habits, downloads, or personal data. Spyware is often installed secretly when a user downloads other software and can slow a computer's performance. Anti-spyware software can prevent spyware installation or detect and remove any spyware already installed. Major anti-virus companies now include anti-spyware features to protect against this type of unwanted program.
Cloud Architecture
Cloud ArchitectureArief Gunawan This document provides an overview of cloud architecture and cloud computing reference architecture. It discusses:
1. The scope covers defining functional requirements and reference architecture for cloud computing, including functional layers, blocks, and service architectures.
2. The cloud computing reference architecture includes layers like the user layer, access layer, services layer, resources and network layer, and cross-layer functions. It also describes functional blocks within these layers.
3. Requirements for cloud architecture are outlined, such as supporting standards, deployment models, and enabling services to appear like intranet services.
Latex Introduction for Beginners
Latex Introduction for Beginnersssuser9e8fa4 This document provides an introduction and overview of LaTeX. It discusses how LaTeX is a document preparation system used for typesetting technical documents and mathematical formulas. It outlines some of the key benefits of LaTeX, such as its ability to produce high quality output, cross-referencing capabilities, and automatic numbering of tables, figures, and contents. The document then covers various LaTeX topics like commands, editors, creating basic documents, text formatting, tables, images, and mathematical equations.
Hadoop
HadoopNishant Gandhi 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.
HADOOP TECHNOLOGY ppt
HADOOP TECHNOLOGY pptsravya raju The most well known technology used for Big Data is Hadoop.
It is actually a large scale batch data processing system
Hadoop Map Reduce
Hadoop Map ReduceVNIT-ACM Student Chapter Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Map reduce in BIG DATA
Map reduce in BIG DATAGauravBiswas9 MapReduce is a programming framework that allows for distributed and parallel processing of large datasets. It consists of a map step that processes key-value pairs in parallel, and a reduce step that aggregates the outputs of the map step. As an example, a word counting problem is presented where words are counted by mapping each word to a key-value pair of the word and 1, and then reducing by summing the counts of each unique word. MapReduce jobs are executed on a cluster in a reliable way using YARN to schedule tasks across nodes, restarting failed tasks when needed.
Introduction to Hadoop
Introduction to HadoopDr. C.V. Suresh Babu This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Hadoop And Their Ecosystem ppt
Hadoop And Their Ecosystem pptsunera pathan The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Big data and Hadoop
Big data and HadoopRahul Agarwal This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
Map Reduce
Map ReducePrashant Gupta A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
PPT on Hadoop
PPT on HadoopShubham Parmar The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
3. mining frequent patterns
3. mining frequent patternsAzad public school The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
Introduction to HDFS
Introduction to HDFSBhavesh Padharia The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
Big Data Analytics with Hadoop
Big Data Analytics with HadoopPhilippe Julio Hadoop, flexible and available architecture for large scale computation and data processing on a network of commodity hardware.
Testing Hadoop jobs with MRUnit
Testing Hadoop jobs with MRUnitEric Wendelin MRUnit is a testing library that makes it easier to test Hadoop jobs. It allows programmatically specifying test input and output, reducing the need for external test files. Tests can focus on individual map and reduce functions. MRUnit abstracts away much of the boilerplate test setup code, though it has some limitations like a lack of distributed testing. Overall though, the benefits of using MRUnit to test Hadoop jobs outweigh the problems.
Introduction to Hadoop and Hadoop component 
Introduction to Hadoop and Hadoop component rebeccatho This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
Mining Frequent Patterns, Association and Correlations
Mining Frequent Patterns, Association and CorrelationsJustin Cletus This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.
Introduction to Hadoop Technology
Introduction to Hadoop TechnologyManish Borkar This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
Introduction to Apache Hadoop Eco-System
Introduction to Apache Hadoop Eco-SystemMd. Hasan Basri (Angel) The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
Apache PIG
Apache PIGPrashant Gupta Apache Pig is a high-level platform for creating programs that runs on Apache Hadoop. The language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark.
Hadoop 
Hadoop ABHIJEET RAJ 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.
Cloud Computing: Hadoop
Cloud Computing: Hadoopdarugar Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop automatically manages data replication and platform failure to ensure very large data sets can be processed efficiently in a reliable, fault-tolerant manner. Common uses of Hadoop include log analysis, data warehousing, web indexing, machine learning, financial analysis, and scientific applications.
Hadoop eco system with mapreduce hive and pig
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.
Introduction to the Map-Reduce framework.pdf
Introduction to the Map-Reduce framework.pdfBikalAdhikari4 The document provides an introduction to the MapReduce programming model and framework. It describes how MapReduce is designed for processing large volumes of data in parallel by dividing work into independent tasks. Programs are written using functional programming idioms like map and reduce operations on lists. The key aspects are:
- Mappers process input records in parallel, emitting (key, value) pairs.
- A shuffle/sort phase groups values by key to same reducer.
- Reducers process grouped values to produce final output, aggregating as needed.
- This allows massive datasets to be processed across a cluster in a fault-tolerant way.
Ad
More Related Content
What's hot (20)
Hadoop
HadoopNishant Gandhi 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.
HADOOP TECHNOLOGY ppt
HADOOP TECHNOLOGY pptsravya raju The most well known technology used for Big Data is Hadoop.
It is actually a large scale batch data processing system
Hadoop Map Reduce
Hadoop Map ReduceVNIT-ACM Student Chapter Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Map reduce in BIG DATA
Map reduce in BIG DATAGauravBiswas9 MapReduce is a programming framework that allows for distributed and parallel processing of large datasets. It consists of a map step that processes key-value pairs in parallel, and a reduce step that aggregates the outputs of the map step. As an example, a word counting problem is presented where words are counted by mapping each word to a key-value pair of the word and 1, and then reducing by summing the counts of each unique word. MapReduce jobs are executed on a cluster in a reliable way using YARN to schedule tasks across nodes, restarting failed tasks when needed.
Introduction to Hadoop
Introduction to HadoopDr. C.V. Suresh Babu This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Hadoop And Their Ecosystem ppt
Hadoop And Their Ecosystem pptsunera pathan The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Big data and Hadoop
Big data and HadoopRahul Agarwal This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
Map Reduce
Map ReducePrashant Gupta A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
PPT on Hadoop
PPT on HadoopShubham Parmar The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
3. mining frequent patterns
3. mining frequent patternsAzad public school The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.
Introduction to HDFS
Introduction to HDFSBhavesh Padharia The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
Big Data Analytics with Hadoop
Big Data Analytics with HadoopPhilippe Julio Hadoop, flexible and available architecture for large scale computation and data processing on a network of commodity hardware.
Testing Hadoop jobs with MRUnit
Testing Hadoop jobs with MRUnitEric Wendelin MRUnit is a testing library that makes it easier to test Hadoop jobs. It allows programmatically specifying test input and output, reducing the need for external test files. Tests can focus on individual map and reduce functions. MRUnit abstracts away much of the boilerplate test setup code, though it has some limitations like a lack of distributed testing. Overall though, the benefits of using MRUnit to test Hadoop jobs outweigh the problems.
Introduction to Hadoop and Hadoop component
Introduction to Hadoop and Hadoop component rebeccatho This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
Mining Frequent Patterns, Association and Correlations
Mining Frequent Patterns, Association and CorrelationsJustin Cletus This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.
Introduction to Hadoop Technology
Introduction to Hadoop TechnologyManish Borkar This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
Introduction to Apache Hadoop Eco-System
Introduction to Apache Hadoop Eco-SystemMd. Hasan Basri (Angel) The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
Apache PIG
Apache PIGPrashant Gupta Apache Pig is a high-level platform for creating programs that runs on Apache Hadoop. The language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark.
Hadoop
Hadoop ABHIJEET RAJ 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.
Cloud Computing: Hadoop
Cloud Computing: Hadoopdarugar Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop automatically manages data replication and platform failure to ensure very large data sets can be processed efficiently in a reliable, fault-tolerant manner. Common uses of Hadoop include log analysis, data warehousing, web indexing, machine learning, financial analysis, and scientific applications.
Similar to Hadoop Architecture (20)
Hadoop eco system with mapreduce hive and pig
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.
Introduction to the Map-Reduce framework.pdf
Introduction to the Map-Reduce framework.pdfBikalAdhikari4 The document provides an introduction to the MapReduce programming model and framework. It describes how MapReduce is designed for processing large volumes of data in parallel by dividing work into independent tasks. Programs are written using functional programming idioms like map and reduce operations on lists. The key aspects are:
- Mappers process input records in parallel, emitting (key, value) pairs.
- A shuffle/sort phase groups values by key to same reducer.
- Reducers process grouped values to produce final output, aggregating as needed.
- This allows massive datasets to be processed across a cluster in a fault-tolerant way.
writing Hadoop Map Reduce programs
writing Hadoop Map Reduce programsjani shaik 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.
MapReduce.pptx
MapReduce.pptxssuserb8d5cb This document provides an overview of MapReduce and how it works. It discusses:
1) Traditional enterprise systems have centralized servers that create bottlenecks for processing large datasets, which MapReduce addresses by dividing tasks across multiple computers.
2) MapReduce contains two tasks - Map converts input data into key-value pairs, and Reduce combines the outputs from Map into a smaller set of pairs.
3) The MapReduce process involves input readers passing data to mappers, optional combiners, partitioners, sorting and shuffling to reducers, and output writers, allowing large datasets to be processed efficiently in parallel across clusters.
Hadoop – Architecture.pptx
Hadoop – Architecture.pptxSakthiVinoth78 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.
Big Data Analytics [email protected]
Big Data Analytics [email protected]WasyihunSema2 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 fault tolerance
Hadoop fault tolerancePallav Jha This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
Hadoop interview questions - Softwarequery.com
Hadoop interview questions - Softwarequery.comsoftwarequery Hadoop interview questions for freshers and experienced people. This is the best place for all beginners and Experts who are eager to learn Hadoop Tutorial from the scratch.
Read more here http://softwarequery.com/hadoop/
Hadoop
Hadoopdevakalyan143 Hadoop/MapReduce is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce, a programming model where input data is processed by "map" functions in parallel, and results are combined by "reduce" functions, to process and generate outputs from large amounts of data and nodes. The core components are the Hadoop Distributed File System for data storage, and the MapReduce programming model and framework. MapReduce jobs involve mapping data to intermediate key-value pairs, shuffling and sorting the data, and reducing to output results.
MAP REDUCE IN DATA SCIENCE.pptx
MAP REDUCE IN DATA SCIENCE.pptxHARIKRISHNANU13 Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through HDFS and distributed processing via MapReduce. HDFS handles storage and MapReduce provides a programming model for parallel processing of large datasets across a cluster. The MapReduce framework consists of a mapper that processes input key-value pairs in parallel, and a reducer that aggregates the output of the mappers by key.
module3part-1-bigdata-230301002404-3db4f2a4 (1).pdf
module3part-1-bigdata-230301002404-3db4f2a4 (1).pdfTSANKARARAO The document discusses MapReduce, a framework for processing large datasets in a distributed manner. It begins by explaining how MapReduce addresses issues around scaling computation across large networks. It then provides details on the key features and working of MapReduce, including how it divides jobs into map and reduce phases that operate in parallel on data blocks. Examples are given to illustrate how MapReduce can be used to count word frequencies in text and tally population statistics from a census.
Big Data.pptx
Big Data.pptxNelakurthyVasanthRed1 This document provides an overview of MapReduce and HBase in big data processing. It discusses how MapReduce distributes tasks across nodes in a cluster and uses map and reduce functions to process large datasets in parallel. It also explains how HBase can be used for storage with MapReduce, providing fast access and retrieval of large amounts of flexible, column-oriented data.
Join Algorithms in MapReduce
Join Algorithms in MapReduceShrihari Rathod The document discusses various join algorithms that can be used in MapReduce frameworks. It begins by introducing MapReduce and Hadoop frameworks and explaining the map and reduce phases. It then outlines the objectives of comparing join algorithms. The document goes on to describe several join algorithms - map-side join, reduce-side join, repartition join, broadcast join, trojan join, and replicated join. It explains the process for each algorithm and compares their advantages and issues. Finally, it provides a decision tree for selecting the optimal join algorithm based on factors like schema knowledge, data size, and replication efficiency.
MapReduce basics
MapReduce basicsHarisankar H Covers:
Distributed processing issues, MR programming model
Sample MR job
How MR can be implemented
Pros and cons of MR, tips for better performance
Map reduce prashant
Map reduce prashantPrashant Gupta This document discusses various concepts related to Hadoop MapReduce including combiners, speculative execution, custom counters, input formats, multiple inputs/outputs, distributed cache, and joins. It explains that a combiner acts as a mini-reducer between the map and reduce stages to reduce data shuffling. Speculative execution allows redundant tasks to improve performance. Custom counters can track specific metrics. Input formats handle input splitting and reading. Multiple inputs allow different mappers for different files. Distributed cache shares read-only files across nodes. Joins can correlate large datasets on a common key.
Unit3 MapReduce
Unit3 MapReduceIntegral university, India MapReduce is a programming model used for processing large datasets in a distributed computing environment. It consists of two main tasks - the Map task which converts input data into intermediate key-value pairs, and the Reduce task which combines these intermediate pairs into a smaller set of output pairs. The MapReduce framework operates on input and output in the form of key-value pairs, with the keys and values implemented as serializable Java objects. It divides jobs into map and reduce tasks executed in parallel on a cluster, with a JobTracker coordinating task assignment and tracking progress.
Ad
More from Dr. C.V. Suresh Babu (20)
Data analytics with R
Data analytics with RDr. C.V. Suresh Babu This presentation discusses the following topics:
Basic features of R
Exploring R GUI
Data Frames & Lists
Handling Data in R Workspace
Reading Data Sets & Exporting Data from R
Manipulating & Processing Data in R
Association rules
Association rulesDr. C.V. Suresh Babu Association rule mining is used to find relationships between items in transaction data. It identifies rules that can predict the occurrence of an item based on other items purchased together frequently. Some key metrics used to evaluate rules include support, which measures how frequently an itemset occurs; confidence, which measures how often items in the predicted set occur given items in the predictor set; and lift, which compares the confidence to expected confidence if items were independent. An example association rule evaluated is {Milk, Diaper} -> {Beer} with support of 0.4, confidence of 0.67, and lift of 1.11.
Clustering
ClusteringDr. C.V. Suresh Babu This document discusses clustering, which is the task of grouping data points into clusters so that points within the same cluster are more similar to each other than points in other clusters. It describes different types of clustering methods, including density-based, hierarchical, partitioning, and grid-based methods. It provides examples of specific clustering algorithms like K-means, DBSCAN, and discusses applications of clustering in fields like marketing, biology, libraries, insurance, city planning, and earthquake studies.
Classification
ClassificationDr. C.V. Suresh Babu Classification is a data analysis technique used to predict class membership for new observations based on a training set of previously labeled examples. It involves building a classification model during a training phase using an algorithm, then testing the model on new data to estimate accuracy. Some common classification algorithms include decision trees, Bayesian networks, neural networks, and support vector machines. Classification has applications in domains like medicine, retail, and entertainment.
Blue property assumptions.
Blue property assumptions.Dr. C.V. Suresh Babu The document discusses the assumptions and properties of ordinary least squares (OLS) estimators in linear regression analysis. It notes that OLS estimators are best linear unbiased estimators (BLUE) if the assumptions of the linear regression model are met. Specifically, it assumes errors have zero mean and constant variance, are uncorrelated, and are normally distributed. Violation of the assumption of constant variance is known as heteroscedasticity. The document outlines how heteroscedasticity impacts the properties of OLS estimators and their use in applications like econometrics.
Introduction to regression
Introduction to regressionDr. C.V. Suresh Babu This document provides an introduction to regression analysis. It discusses that regression analysis investigates the relationship between dependent and independent variables to model and analyze data. The document outlines different types of regressions including linear, polynomial, stepwise, ridge, lasso, and elastic net regressions. It explains that regression analysis is used for predictive modeling, forecasting, and determining the impact of variables. The benefits of regression analysis are that it indicates significant relationships and the strength of impact between variables.
DART
DARTDr. C.V. Suresh Babu This presentation briefly discusses about DART (Diagnostic Assistance Reference Tool) in Expert Systems
Mycin
MycinDr. C.V. Suresh Babu MYCIN was an early expert system developed at Stanford University in 1972 to assist physicians in diagnosing and selecting treatment for bacterial and blood infections. It used over 600 production rules encoding the clinical decision criteria of infectious disease experts to diagnose patients based on reported symptoms and test results. While it could not replace human diagnosis due to computing limitations at the time, MYCIN demonstrated that expert knowledge could be represented computationally and established a foundation for more advanced machine learning and knowledge base systems.
Expert systems
Expert systemsDr. C.V. Suresh Babu The document discusses expert systems, which are computer applications that solve complex problems at a human expert level. It describes the characteristics and capabilities of expert systems, why they are useful, and their key components - knowledge base, inference engine, and user interface. The document also outlines common applications of expert systems and the general development process.
Dempster shafer theory
Dempster shafer theoryDr. C.V. Suresh Babu The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
Bayes network
Bayes networkDr. C.V. Suresh Babu A Bayesian network is a probabilistic graphical model that represents conditional dependencies among random variables using a directed acyclic graph. It consists of nodes representing variables and directed edges representing causal relationships. Each node contains a conditional probability table that quantifies the effect of its parent nodes on that variable. Bayesian networks can be used to calculate the probability of events occurring based on the network structure and conditional probability tables, such as computing the probability of an alarm sounding given that no burglary or earthquake occurred but two neighbors called.
Bayes' theorem
Bayes' theoremDr. C.V. Suresh Babu This presentation gives a brief introduction about Bayes' Theorem and its applications in Artificial intelligence
Knowledge based agents
Knowledge based agentsDr. C.V. Suresh Babu This document discusses knowledge-based agents in artificial intelligence. It defines knowledge-based agents as agents that maintain an internal state of knowledge, reason over that knowledge, update their knowledge based on observations, and take actions. Knowledge-based agents have two main components: a knowledge base that stores facts about the world, and an inference system that applies logical rules to deduce new information from the knowledge base. The document also describes the architecture of knowledge-based agents and different approaches to designing them.
Rule based system
Rule based systemDr. C.V. Suresh Babu A rule-based system uses predefined rules to make logical deductions and choices to perform automated actions. It consists of a database of rules representing knowledge, a database of facts as inputs, and an inference engine that controls the process of deriving conclusions by applying rules to facts. A rule-based system mimics human decision making by applying rules in an "if-then" format to incoming data to perform actions, but unlike AI it does not learn or adapt on its own.
Formal Logic in AI
Formal Logic in AIDr. C.V. Suresh Babu This document discusses formal logic and its applications in AI and machine learning. It begins by explaining why logic is useful in complex domains or with little data. It then describes logic-based approaches to AI that use symbolic reasoning as an alternative to machine learning. The document proceeds to explain propositional logic and first-order logic, noting how first-order logic improves on propositional logic by allowing variables. It also mentions other logics and their applications in areas like automated discovery, inductive programming, and verification of computer systems and machine learning models.
Production based system
Production based systemDr. C.V. Suresh Babu The document discusses production systems, which are rule-based systems used in artificial intelligence to model intelligent behavior. A production system consists of a global database, set of production rules, and control system. The rules fire to modify the database based on conditions. Different control strategies are used to determine which rules fire. Production systems are modular and allow knowledge representation as condition-action rules. Examples of applications in problem solving are provided.
Game playing in AI
Game playing in AIDr. C.V. Suresh Babu The document discusses game playing in artificial intelligence. It describes how general game playing (GGP) involves designing AI that can play multiple games by learning the rules, rather than being programmed for a specific game. The document outlines how the minimax algorithm is commonly used for game playing, involving move generation and static evaluation functions to search game trees and determine the best move by maximizing or minimizing values at each level.
Diagnosis test of diabetics and hypertension by AI
Diagnosis test of diabetics and hypertension by AIDr. C.V. Suresh Babu A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
Ad
Recently uploaded (20)
Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.
Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.Dele Amefo Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.
Exploring Wayland: A Modern Display Server for the Future
Exploring Wayland: A Modern Display Server for the FutureICS Wayland is revolutionizing the way we interact with graphical interfaces, offering a modern alternative to the X Window System. In this webinar, we’ll delve into the architecture and benefits of Wayland, including its streamlined design, enhanced performance, and improved security features.
Download Wondershare Filmora Crack [2025] With Latest![Download Wondershare Filmora Crack [2025] With Latest](https://cdn.slidesharecdn.com/ss_thumbnails/neo4j-howkgsareshapingthefutureofgenerativeaiatawssummitlondonapril2024-240426125209-2d9db05d-250419-250428115407-a04afffa-thumbnail.jpg?width=560&fit=bounds)
Download Wondershare Filmora Crack [2025] With Latesttahirabibi60507 Copy & Past Link 👉👉
http://drfiles.net/
Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
Adobe After Effects Crack FREE FRESH version 2025
Adobe After Effects Crack FREE FRESH version 2025kashifyounis067 🌍📱👉COPY LINK & PASTE ON GOOGLE http://drfiles.net/ 👈🌍
Adobe After Effects is a software application used for creating motion graphics, special effects, and video compositing. It's widely used in TV and film post-production, as well as for creating visuals for online content, presentations, and more. While it can be used to create basic animations and designs, its primary strength lies in adding visual effects and motion to videos and graphics after they have been edited.
Here's a more detailed breakdown:
Motion Graphics:
.
After Effects is powerful for creating animated titles, transitions, and other visual elements to enhance the look of videos and presentations.
Visual Effects:
.
It's used extensively in film and television for creating special effects like green screen compositing, object manipulation, and other visual enhancements.
Video Compositing:
.
After Effects allows users to combine multiple video clips, images, and graphics to create a final, cohesive visual.
Animation:
.
It uses keyframes to create smooth, animated sequences, allowing for precise control over the movement and appearance of objects.
Integration with Adobe Creative Cloud:
.
After Effects is part of the Adobe Creative Cloud, a suite of software that includes other popular applications like Photoshop and Premiere Pro.
Post-Production Tool:
.
After Effects is primarily used in the post-production phase, meaning it's used to enhance the visuals after the initial editing of footage has been completed.
TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...
TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.
Explaining GitHub Actions Failures with Large Language Models Challenges, In...
Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185 GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
https://arxiv.org/abs/2501.16495
Requirements in Engineering AI- Enabled Systems: Open Problems and Safe AI Sy...
Requirements in Engineering AI- Enabled Systems: Open Problems and Safe AI Sy...Lionel Briand Keynote at RAISE workshop, ICSE 2025
Avast Premium Security Crack FREE Latest Version 2025
Avast Premium Security Crack FREE Latest Version 2025mu394968 🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍
Avast Premium Security is a paid subscription service that provides comprehensive online security and privacy protection for multiple devices. It includes features like antivirus, firewall, ransomware protection, and website scanning, all designed to safeguard against a wide range of online threats, according to Avast.
Key features of Avast Premium Security:
Antivirus: Protects against viruses, malware, and other malicious software, according to Avast.
Firewall: Controls network traffic and blocks unauthorized access to your devices, as noted by All About Cookies.
Ransomware protection: Helps prevent ransomware attacks, which can encrypt your files and hold them hostage.
Website scanning: Checks websites for malicious content before you visit them, according to Avast.
Email Guardian: Scans your emails for suspicious attachments and phishing attempts.
Multi-device protection: Covers up to 10 devices, including Windows, Mac, Android, and iOS, as stated by 2GO Software.
Privacy features: Helps protect your personal data and online privacy.
In essence, Avast Premium Security provides a robust suite of tools to keep your devices and online activity safe and secure, according to Avast.
Expand your AI adoption with AgentExchange
Expand your AI adoption with AgentExchangeFexle Services Pvt. Ltd. AgentExchange is Salesforce’s latest innovation, expanding upon the foundation of AppExchange by offering a centralized marketplace for AI-powered digital labor. Designed for Agentblazers, developers, and Salesforce admins, this platform enables the rapid development and deployment of AI agents across industries.
Email: [email protected]
Phone: +1(630) 349 2411
Website: https://www.fexle.com/blogs/agentexchange-an-ultimate-guide-for-salesforce-consultants-businesses/?utm_source=slideshare&utm_medium=pptNg
Landscape of Requirements Engineering for/by AI through Literature Review
Landscape of Requirements Engineering for/by AI through Literature ReviewHironori Washizaki Hironori Washizaki, "Landscape of Requirements Engineering for/by AI through Literature Review," RAISE 2025: Workshop on Requirements engineering for AI-powered SoftwarE, 2025.
FL Studio Producer Edition Crack 2025 Full Version
FL Studio Producer Edition Crack 2025 Full Versiontahirabibi60507 Copy & Past Link 👉👉
http://drfiles.net/
FL Studio is a Digital Audio Workstation (DAW) software used for music production. It's developed by the Belgian company Image-Line. FL Studio allows users to create and edit music using a graphical user interface with a pattern-based music sequencer.
Automation Techniques in RPA - UiPath Certificate
Automation Techniques in RPA - UiPath CertificateVICTOR MAESTRE RAMIREZ Automation Techniques in RPA - UiPath Certificate
Secure Test Infrastructure: The Backbone of Trustworthy Software Development
Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi A secure test infrastructure ensures that the testing process doesn’t become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
Get & Download Wondershare Filmora Crack Latest [2025]![Get & Download Wondershare Filmora Crack Latest [2025]](https://cdn.slidesharecdn.com/ss_thumbnails/revolutionizingresidentialwi-fi-250422112639-60fb726f-250429170801-59e1b240-thumbnail.jpg?width=560&fit=bounds)
Get & Download Wondershare Filmora Crack Latest [2025]saniaaftab72555 Copy & Past Link 👉👉
https://dr-up-community.info/
Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
Adobe Illustrator Crack FREE Download 2025 Latest Version
Adobe Illustrator Crack FREE Download 2025 Latest Versionkashifyounis067 🌍📱👉COPY LINK & PASTE ON GOOGLE http://drfiles.net/ 👈🌍
Adobe Illustrator is a powerful, professional-grade vector graphics software used for creating a wide range of designs, including logos, icons, illustrations, and more. Unlike raster graphics (like photos), which are made of pixels, vector graphics in Illustrator are defined by mathematical equations, allowing them to be scaled up or down infinitely without losing quality.
Here's a more detailed explanation:
Key Features and Capabilities:
Vector-Based Design:
Illustrator's foundation is its use of vector graphics, meaning designs are created using paths, lines, shapes, and curves defined mathematically.
Scalability:
This vector-based approach allows for designs to be resized without any loss of resolution or quality, making it suitable for various print and digital applications.
Design Creation:
Illustrator is used for a wide variety of design purposes, including:
Logos and Brand Identity: Creating logos, icons, and other brand assets.
Illustrations: Designing detailed illustrations for books, magazines, web pages, and more.
Marketing Materials: Creating posters, flyers, banners, and other marketing visuals.
Web Design: Designing web graphics, including icons, buttons, and layouts.
Text Handling:
Illustrator offers sophisticated typography tools for manipulating and designing text within your graphics.
Brushes and Effects:
It provides a range of brushes and effects for adding artistic touches and visual styles to your designs.
Integration with Other Adobe Software:
Illustrator integrates seamlessly with other Adobe Creative Cloud apps like Photoshop, InDesign, and Dreamweaver, facilitating a smooth workflow.
Why Use Illustrator?
Professional-Grade Features:
Illustrator offers a comprehensive set of tools and features for professional design work.
Versatility:
It can be used for a wide range of design tasks and applications, making it a versatile tool for designers.
Industry Standard:
Illustrator is a widely used and recognized software in the graphic design industry.
Creative Freedom:
It empowers designers to create detailed, high-quality graphics with a high degree of control and precision.
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software.
Presented at: The 33rd International Conference on Program Comprehension (ICPC '25)
Date of Conference: April 2025
Conference Location: Ottawa, Ontario, Canada
Preprint: https://arxiv.org/abs/2501.10037
Interactive odoo dashboards for sales, CRM , Inventory, Invoice, Purchase, Pr...
Interactive odoo dashboards for sales, CRM , Inventory, Invoice, Purchase, Pr...AxisTechnolabs Interactive Odoo Dashboard for various business needs can provide users with dynamic, visually appealing dashboards tailored to their specific requirements. such a module that could support multiple dashboards for different aspects of a business
✅Visit And Buy Now : https://bit.ly/3VojWza
✅This Interactive Odoo dashboard module allow user to create their own odoo interactive dashboards for various purpose.
App download now :
Odoo 18 : https://bit.ly/3VojWza
Odoo 17 : https://bit.ly/4h9Z47G
Odoo 16 : https://bit.ly/3FJTEA4
Odoo 15 : https://bit.ly/3W7tsEB
Odoo 14 : https://bit.ly/3BqZDHg
Odoo 13 : https://bit.ly/3uNMF2t
Try Our website appointment booking odoo app : https://bit.ly/3SvNvgU
👉Want a Demo ?📧 [email protected]
➡️Contact us for Odoo ERP Set up : 091066 49361
👉Explore more apps: https://bit.ly/3oFIOCF
👉Want to know more : 🌐 https://www.axistechnolabs.com/
#odoo #odoo18 #odoo17 #odoo16 #odoo15 #odooapps #dashboards #dashboardsoftware #odooerp #odooimplementation #odoodashboardapp #bestodoodashboard #dashboardapp #odoodashboard #dashboardmodule #interactivedashboard #bestdashboard #dashboard #odootag #odooservices #odoonewfeatures #newappfeatures #odoodashboardapp #dynamicdashboard #odooapp #odooappstore #TopOdooApps #odooapp #odooexperience #odoodevelopment #businessdashboard #allinonedashboard #odooproducts
Pixologic ZBrush Crack Plus Activation Key [Latest 2025] New Version![Pixologic ZBrush Crack Plus Activation Key [Latest 2025] New Version](https://cdn.slidesharecdn.com/ss_thumbnails/fashionevolution2-250322112409-f76abaa7-250428124909-b51264ff-250504160528-fc2bb1c5-thumbnail.jpg?width=560&fit=bounds)
Pixologic ZBrush Crack Plus Activation Key [Latest 2025] New Versionsaimabibi60507 Copy & Past Link👉👉
https://dr-up-community.info/
Pixologic ZBrush, now developed by Maxon, is a premier digital sculpting and painting software renowned for its ability to create highly detailed 3D models. Utilizing a unique "pixol" technology, ZBrush stores depth, lighting, and material information for each point on the screen, allowing artists to sculpt and paint with remarkable precision .
How can one start with crypto wallet development.pptx
How can one start with crypto wallet development.pptxlaravinson24 This presentation is a beginner-friendly guide to developing a crypto wallet from scratch. It covers essential concepts such as wallet types, blockchain integration, key management, and security best practices. Ideal for developers and tech enthusiasts looking to enter the world of Web3 and decentralized finance.
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa
Hadoop Architecture
- 1. Dr. C.V. Suresh Babu (CentreforKnowledgeTransfer) institute
- 2. Discussion Topics • Introduction • Components of Hadoop • MapReduce • Map Task • Reduce Task • Anatomy of a Map Reduce (CentreforKnowledgeTransfer) institute
- 3. Introduction • Hadoop is a framework written in Java that utilizes a large cluster of commodity hardware to maintain and store big size data. • Hadoop works on MapReduce Programming Algorithm that was introduced by Google. • Today lots of Big Brand Companys are using Hadoop in their Organization to deal with big data for eg. Facebook, Yahoo, Netflix, eBay, etc. (CentreforKnowledgeTransfer) institute
- 4. Components of Hadoop The Hadoop Architecture Mainly consists of 4 components. • MapReduce • HDFS(Hadoop distributed File System) • YARN(Yet Another Resource Framework) • Common Utilities or Hadoop Common (CentreforKnowledgeTransfer) institute
- 5. A Hadoop cluster consists of a single master and multiple slave nodes. The master node includes Job Tracker, Task Tracker, NameNode, and DataNode whereas the slave node includes DataNode and TaskTracker. (CentreforKnowledgeTransfer) institute
- 6. MapReduce • MapReduce nothing but just like an Algorithm or a data structure that is based on the YARN framework. • The major feature of MapReduce is to perform the distributed processing in parallel in a Hadoop cluster which Makes Hadoop working so fast. • When you are dealing with Big Data, serial processing is no more of any use. • MapReduce has mainly 2 tasks which are divided phase-wise: Map Task Reduce Task (CentreforKnowledgeTransfer) institute
- 7. Map Task Here, we can see that the Input is provided to the Map() function then it’s output is used as an input to the Reduce function and after that, we receive our final output. In first phase, Map is utilized and in next phase Reduce is utilized. (CentreforKnowledgeTransfer) institute
- 8. Map() • As we can see that an Input is provided to the Map(), now as we are using Big Data. The Input is a set of Data. • The Map() function here breaks this DataBlocks into Tuples that are nothing but a key-value pair. • These key-value pairs are now sent as input to the Reduce(). Reduce() • The Reduce() function then combines this broken Tuples or key-value pair based on its Key value and form set of Tuples, and perform some operation like sorting, summation type job, etc. which is then sent to the final Output Node. • Finally, the Output is Obtained. Note: The data processing is always done in Reducer depending upon the business requirement of that industry. This is How First Map() and then Reduce is utilized one by one. (CentreforKnowledgeTransfer) institute
- 9. Map Task • RecordReader The purpose of recoredreader is to break the records. It is responsible for providing key-value pairs in a Map() function. The key is actually is its locational information and value is the data associated with it. • Map: A map is nothing but a user-defined function whose work is to process the Tuples obtained from record reader. The Map() function either does not generate any key-value pair or generate multiple pairs of these tuples. • Combiner: Combiner is used for grouping the data in the Map workflow. It is similar to a Local reducer. The intermediate key-value that are generated in the Map is combined with the help of this combiner. Using a combiner is not necessary as it is optional. • Partitionar: Partitional is responsible for fetching key-value pairs generated in the Mapper Phases. The partitioner generates the shards corresponding to each reducer. Hashcode of each key is also fetched by this partition. Then partitioner performs it’s(Hashcode) modulus with the number of reducers(key.hashcode()%(number of reducers)). (CentreforKnowledgeTransfer) institute
- 10. Reduce Task • Shuffle and Sort: The Task of Reducer starts with this step, the process in which the Mapper generates the intermediate key-value and transfers them to the Reducer task is known as Shuffling. Using the Shuffling process the system can sort the data using its key value.Once some of the Mapping tasks are done Shuffling begins that is why it is a faster process and does not wait for the completion of the task performed by Mapper. • Reduce: The main function or task of the Reduce is to gather the Tuple generated from Map and then perform some sorting and aggregation sort of process on those key-value depending on its key element. • OutputFormat: Once all the operations are performed, the key-value pairs are written into the file with the help of record writer, each record in a new line, and the key and value in a space-separated manner. (CentreforKnowledgeTransfer) institute