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[Harvard CS264] 08b - MapReduce and Hadoop (Zak Stone, Harvard)npinto
This document provides an introduction and overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It outlines what Hadoop is, how its core components MapReduce and HDFS work, advantages like scalability and fault tolerance, disadvantages like complexity, and resources for getting started with Hadoop installations and programming.
This talk is about the usage of Hadoop at Last.fm, a community-driven music discovery website. We will go through the main types of data Last.fm stores in Hadoop, explain why we need Hadoop to store and process our data, give examples of what we do with it, and mention some of the additional tools from the Hadoop ecosystem on which we rely for getting these things done.
An introduction to Hadoop for large scale data analysisAbhijit Sharma
This document provides an overview of Hadoop and how it can be used for large scale data analysis. Some key points discussed include:
- Hadoop uses MapReduce, an programming model for processing large datasets in parallel across clusters of computers using a simple programming model.
- It also uses HDFS for reliable storage of very large files across clusters of commodity servers.
- Examples of how Hadoop can be used include distributed logging, search, analytics, and data mining of large datasets.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
Amazon-style shopping cart analysis using MapReduce on a Hadoop clusterAsociatia ProLinux
This document discusses using MapReduce on a Hadoop cluster to analyze shopping cart data similar to what Amazon analyzes. It begins with an agenda that includes deploying Hadoop and using MapReduce for machine learning. It then discusses the origins of Hadoop from the Nutch project and key facts about Hadoop architecture. Part 1 explains how to configure and deploy a Hadoop cluster. Part 2 demonstrates hands-on use of MapReduce to analyze sample data, providing example Mapper and Reducer Python scripts. It concludes with other real-world uses of MapReduce.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
[Harvard CS264] 08b - MapReduce and Hadoop (Zak Stone, Harvard)npinto
This document provides an introduction and overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It outlines what Hadoop is, how its core components MapReduce and HDFS work, advantages like scalability and fault tolerance, disadvantages like complexity, and resources for getting started with Hadoop installations and programming.
This talk is about the usage of Hadoop at Last.fm, a community-driven music discovery website. We will go through the main types of data Last.fm stores in Hadoop, explain why we need Hadoop to store and process our data, give examples of what we do with it, and mention some of the additional tools from the Hadoop ecosystem on which we rely for getting these things done.
An introduction to Hadoop for large scale data analysisAbhijit Sharma
This document provides an overview of Hadoop and how it can be used for large scale data analysis. Some key points discussed include:
- Hadoop uses MapReduce, an programming model for processing large datasets in parallel across clusters of computers using a simple programming model.
- It also uses HDFS for reliable storage of very large files across clusters of commodity servers.
- Examples of how Hadoop can be used include distributed logging, search, analytics, and data mining of large datasets.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
Amazon-style shopping cart analysis using MapReduce on a Hadoop clusterAsociatia ProLinux
This document discusses using MapReduce on a Hadoop cluster to analyze shopping cart data similar to what Amazon analyzes. It begins with an agenda that includes deploying Hadoop and using MapReduce for machine learning. It then discusses the origins of Hadoop from the Nutch project and key facts about Hadoop architecture. Part 1 explains how to configure and deploy a Hadoop cluster. Part 2 demonstrates hands-on use of MapReduce to analyze sample data, providing example Mapper and Reducer Python scripts. It concludes with other real-world uses of MapReduce.
- R is a free software environment for statistical computing and graphics. It has an active user community and supports graphical capabilities.
- R can import and export data, perform data manipulation and summaries. It provides various plotting functions and control structures to control program flow.
- Debugging tools in R include traceback, debug, browser and trace which help identify and fix issues in functions.
Heap sort uses a heap data structure that maintains the max-heap or min-heap property. It involves two main steps: 1) building the heap from the input array using the BUILD-MAX-HEAP procedure in O(n) time, and 2) repeatedly extracting the maximum/minimum element from the heap and inserting it into the sorted portion using the DELHEAP procedure, running in O(n log n) time overall. The key operation is MAX-HEAPIFY, which maintains the max-heap property in O(log n) time during heap operations like insertion and deletion.
This document discusses manipulating LiDAR data to derive tree heights and other metrics. It involves combining 9GB of LAS LiDAR point cloud files, clipping them to an area of interest, filtering out bad returns, tiling the data into cells, splitting by classification into ground and vegetation points, extracting the minimum and maximum Z coordinates in each cell to calculate height differences, and outputting the results as colored polygons in MapInfo format. The source data comes from LAS LiDAR and GeoBase national road network files.
This document provides an overview of using the dplyr package in R for data manipulation and basic statistics. It recaps loading and inspecting data, then covers key dplyr functions like filter() for subsetting rows, arrange() for reordering rows, select() for choosing columns, distinct() for unique rows, mutate() for transforming variables, and summarise() for creating summaries and grouping variables. The document demonstrates examples of these functions on sample data and encourages exploring more dplyr functions and applying them to real datasets.
Data engineering and analytics using pythonPurna Chander
This document provides an overview of data engineering and analytics using Python. It discusses Jupyter notebooks and commonly used Python modules for data science like Pandas, NumPy, SciPy, Matplotlib and Seaborn. It describes Anaconda distribution and the key features of Pandas including data loading, structures like DataFrames and Series, and core operations like filtering, mapping, joining, sorting, cleaning and grouping. It also demonstrates data visualization using Seaborn and a machine learning example of linear regression.
Python Pandas is a powerful library for data analysis and manipulation. It provides rich data structures and methods for loading, cleaning, transforming, and modeling data. Pandas allows users to easily work with labeled data and columns in tabular structures called Series and DataFrames. These structures enable fast and flexible operations like slicing, selecting subsets of data, and performing calculations. Descriptive statistics functions in Pandas allow analyzing and summarizing data in DataFrames.
The document discusses various data visualization techniques in R including bar plots, pie charts, histograms, kernel density plots, line charts, box plots, heat maps, and word clouds. It provides code examples for creating basic and customized visualizations of different types using the R programming language and exporting the visualizations to file formats like PDF, PNG, and JPEG.
This document summarizes a lab on data structures and algorithms that focuses on heaps and heap sort. It introduces heaps as almost complete binary trees stored in an array that obey the max-heap or min-heap property. Basic heap operations like insertion and deletion are described along with filtering an element up or down the heap to maintain the property. Heap sort is then introduced as using a heap to iteratively extract and place the minimum element. The objectives are to implement functions for getting a node's parent and children in a heap as well as implement and test heap sort.
ComputeFest 2012: Intro To R for Physical Sciencesalexstorer
This document provides an introduction to the R programming language presented by Alex Storer at ComputeFest 2012. It discusses why R should be used over other languages like MATLAB and Python, provides examples of basic R syntax and functions, and walks through an example of loading climate data and creating plots to visualize rainfall anomalies over time. The goal is to provide attendees with a foundation of R basics while working through a real data analysis problem.
This document discusses importing and interfacing CSV files with Python Pandas DataFrames. It explains that Pandas DataFrames allow for querying and calculations on tabular data, and CSV files are commonly used to store scientific data with columns separated by commas. It then demonstrates how to import CSV files into DataFrames using Pandas read_csv function, specifying options like the file path, column separator, and header row. It also shows how to export DataFrames to CSV files using the to_csv method.
This document discusses heap data structures and their application in sorting. It defines heaps as nearly complete binary trees that satisfy the heap property - for any node x, the parent is greater than or equal to x in a max-heap and less than or equal in a min-heap. Heaps can be represented using arrays in a compact manner. Heapsort uses a max-heap to sort an array by building a max-heap and repeatedly swapping the root with the last element.
Pig is a platform for analyzing large datasets using a high-level language called Pig Latin. Pig Latin scripts are compiled into MapReduce jobs for execution. Pig can load, filter, join, group, order, and store data. Common operations include LOAD to import data, FILTER to filter records, FOREACH to generate new fields, DISTINCT to remove duplicates, and STORE to export data. Pig also supports functions like AVG, MAX, MIN, SUM, and TOKENIZE to analyze data.
Heap Sort in Design and Analysis of algorithmssamairaakram
Brief description of Heap Sort and its types.it includes Binary Tree and its types. analysis and algorithm of Heap Sort. comparison b/w Heap,Qucik and Merge Sort.
Communication Patterns with Apache Spark-(Reza Zadeh, Stanford)Spark Summit
1) Spark programs involve creating RDDs from input data, transforming them lazily through operations like map and filter, and then launching actions like count to trigger computation across clusters.
2) Spark uses communication patterns like shuffle to perform reductions across partitions during operations like reduceByKey. It employs techniques like sorting and broadcasting to efficiently distribute data.
3) The Spark scheduler splits jobs into stages of tasks, coordinates execution on worker nodes, and retries failed tasks to provide fault tolerance across the cluster.
Heap data structures can be used for sorting and memory management. Heapsort uses a max heap to sort an array by repeatedly replacing the root with the last element and heapifying the reduced heap. Heaps are also used to manage memory dynamically by allocating and resizing memory blocks on the heap using functions like malloc() and realloc(). Priority queues, which can be implemented efficiently using binary heaps, are used for applications that require fast retrieval of the highest or lowest priority element, such as scheduling tasks.
Sawmill - Integrating R and Large Data CloudsRobert Grossman
This document discusses using R for large-scale data analysis on distributed data clouds. It recommends splitting large datasets into segments using MapReduce or UDFs, then building separate models for each segment in R. PMML can be used to combine the separate models into an ensemble model. The Sawmill framework is proposed to preprocess data in parallel, build models for each segment using R, and combine the models into a PMML file for deployment. Running R on each segment sequentially allows scaling to large datasets, with examples showing processing times for different numbers of segments.
A heap tree is a complete binary tree where each parent node has a value greater than or equal to its children. There are two types - max-heap and min-heap. A heap can be represented as an array where the root is at index 1 and children at 2i and 2i+1. Operations like insertion and deletion involve bubbling/sifting nodes up or down the tree to maintain the heap property. Heapsort uses a heap to sort an array by repeatedly extracting the max/min element.
The document discusses heap data structures and their use in priority queues and heapsort. It defines a heap as a complete binary tree stored in an array. Each node stores a value, with the heap property being that a node's value is greater than or equal to its children's values (for a max heap). Algorithms like Max-Heapify, Build-Max-Heap, Heap-Extract-Max, and Heap-Increase-Key are presented to maintain the heap property during operations. Priority queues use heaps to efficiently retrieve the maximum element, while heapsort sorts an array by building a max heap and repeatedly extracting elements.
Introduction to Map-Reduce Programming with HadoopDilum Bandara
This document provides an overview of MapReduce programming with Hadoop, including descriptions of HDFS architecture, examples of common MapReduce algorithms (word count, mean, sorting, inverted index, distributed grep), and how to write MapReduce clients and customize parts of the MapReduce job like input/output formats, partitioners, and distributed caching of files.
- R is a free software environment for statistical computing and graphics. It has an active user community and supports graphical capabilities.
- R can import and export data, perform data manipulation and summaries. It provides various plotting functions and control structures to control program flow.
- Debugging tools in R include traceback, debug, browser and trace which help identify and fix issues in functions.
Heap sort uses a heap data structure that maintains the max-heap or min-heap property. It involves two main steps: 1) building the heap from the input array using the BUILD-MAX-HEAP procedure in O(n) time, and 2) repeatedly extracting the maximum/minimum element from the heap and inserting it into the sorted portion using the DELHEAP procedure, running in O(n log n) time overall. The key operation is MAX-HEAPIFY, which maintains the max-heap property in O(log n) time during heap operations like insertion and deletion.
This document discusses manipulating LiDAR data to derive tree heights and other metrics. It involves combining 9GB of LAS LiDAR point cloud files, clipping them to an area of interest, filtering out bad returns, tiling the data into cells, splitting by classification into ground and vegetation points, extracting the minimum and maximum Z coordinates in each cell to calculate height differences, and outputting the results as colored polygons in MapInfo format. The source data comes from LAS LiDAR and GeoBase national road network files.
This document provides an overview of using the dplyr package in R for data manipulation and basic statistics. It recaps loading and inspecting data, then covers key dplyr functions like filter() for subsetting rows, arrange() for reordering rows, select() for choosing columns, distinct() for unique rows, mutate() for transforming variables, and summarise() for creating summaries and grouping variables. The document demonstrates examples of these functions on sample data and encourages exploring more dplyr functions and applying them to real datasets.
Data engineering and analytics using pythonPurna Chander
This document provides an overview of data engineering and analytics using Python. It discusses Jupyter notebooks and commonly used Python modules for data science like Pandas, NumPy, SciPy, Matplotlib and Seaborn. It describes Anaconda distribution and the key features of Pandas including data loading, structures like DataFrames and Series, and core operations like filtering, mapping, joining, sorting, cleaning and grouping. It also demonstrates data visualization using Seaborn and a machine learning example of linear regression.
Python Pandas is a powerful library for data analysis and manipulation. It provides rich data structures and methods for loading, cleaning, transforming, and modeling data. Pandas allows users to easily work with labeled data and columns in tabular structures called Series and DataFrames. These structures enable fast and flexible operations like slicing, selecting subsets of data, and performing calculations. Descriptive statistics functions in Pandas allow analyzing and summarizing data in DataFrames.
The document discusses various data visualization techniques in R including bar plots, pie charts, histograms, kernel density plots, line charts, box plots, heat maps, and word clouds. It provides code examples for creating basic and customized visualizations of different types using the R programming language and exporting the visualizations to file formats like PDF, PNG, and JPEG.
This document summarizes a lab on data structures and algorithms that focuses on heaps and heap sort. It introduces heaps as almost complete binary trees stored in an array that obey the max-heap or min-heap property. Basic heap operations like insertion and deletion are described along with filtering an element up or down the heap to maintain the property. Heap sort is then introduced as using a heap to iteratively extract and place the minimum element. The objectives are to implement functions for getting a node's parent and children in a heap as well as implement and test heap sort.
ComputeFest 2012: Intro To R for Physical Sciencesalexstorer
This document provides an introduction to the R programming language presented by Alex Storer at ComputeFest 2012. It discusses why R should be used over other languages like MATLAB and Python, provides examples of basic R syntax and functions, and walks through an example of loading climate data and creating plots to visualize rainfall anomalies over time. The goal is to provide attendees with a foundation of R basics while working through a real data analysis problem.
This document discusses importing and interfacing CSV files with Python Pandas DataFrames. It explains that Pandas DataFrames allow for querying and calculations on tabular data, and CSV files are commonly used to store scientific data with columns separated by commas. It then demonstrates how to import CSV files into DataFrames using Pandas read_csv function, specifying options like the file path, column separator, and header row. It also shows how to export DataFrames to CSV files using the to_csv method.
This document discusses heap data structures and their application in sorting. It defines heaps as nearly complete binary trees that satisfy the heap property - for any node x, the parent is greater than or equal to x in a max-heap and less than or equal in a min-heap. Heaps can be represented using arrays in a compact manner. Heapsort uses a max-heap to sort an array by building a max-heap and repeatedly swapping the root with the last element.
Pig is a platform for analyzing large datasets using a high-level language called Pig Latin. Pig Latin scripts are compiled into MapReduce jobs for execution. Pig can load, filter, join, group, order, and store data. Common operations include LOAD to import data, FILTER to filter records, FOREACH to generate new fields, DISTINCT to remove duplicates, and STORE to export data. Pig also supports functions like AVG, MAX, MIN, SUM, and TOKENIZE to analyze data.
Heap Sort in Design and Analysis of algorithmssamairaakram
Brief description of Heap Sort and its types.it includes Binary Tree and its types. analysis and algorithm of Heap Sort. comparison b/w Heap,Qucik and Merge Sort.
Communication Patterns with Apache Spark-(Reza Zadeh, Stanford)Spark Summit
1) Spark programs involve creating RDDs from input data, transforming them lazily through operations like map and filter, and then launching actions like count to trigger computation across clusters.
2) Spark uses communication patterns like shuffle to perform reductions across partitions during operations like reduceByKey. It employs techniques like sorting and broadcasting to efficiently distribute data.
3) The Spark scheduler splits jobs into stages of tasks, coordinates execution on worker nodes, and retries failed tasks to provide fault tolerance across the cluster.
Heap data structures can be used for sorting and memory management. Heapsort uses a max heap to sort an array by repeatedly replacing the root with the last element and heapifying the reduced heap. Heaps are also used to manage memory dynamically by allocating and resizing memory blocks on the heap using functions like malloc() and realloc(). Priority queues, which can be implemented efficiently using binary heaps, are used for applications that require fast retrieval of the highest or lowest priority element, such as scheduling tasks.
Sawmill - Integrating R and Large Data CloudsRobert Grossman
This document discusses using R for large-scale data analysis on distributed data clouds. It recommends splitting large datasets into segments using MapReduce or UDFs, then building separate models for each segment in R. PMML can be used to combine the separate models into an ensemble model. The Sawmill framework is proposed to preprocess data in parallel, build models for each segment using R, and combine the models into a PMML file for deployment. Running R on each segment sequentially allows scaling to large datasets, with examples showing processing times for different numbers of segments.
A heap tree is a complete binary tree where each parent node has a value greater than or equal to its children. There are two types - max-heap and min-heap. A heap can be represented as an array where the root is at index 1 and children at 2i and 2i+1. Operations like insertion and deletion involve bubbling/sifting nodes up or down the tree to maintain the heap property. Heapsort uses a heap to sort an array by repeatedly extracting the max/min element.
The document discusses heap data structures and their use in priority queues and heapsort. It defines a heap as a complete binary tree stored in an array. Each node stores a value, with the heap property being that a node's value is greater than or equal to its children's values (for a max heap). Algorithms like Max-Heapify, Build-Max-Heap, Heap-Extract-Max, and Heap-Increase-Key are presented to maintain the heap property during operations. Priority queues use heaps to efficiently retrieve the maximum element, while heapsort sorts an array by building a max heap and repeatedly extracting elements.
Introduction to Map-Reduce Programming with HadoopDilum Bandara
This document provides an overview of MapReduce programming with Hadoop, including descriptions of HDFS architecture, examples of common MapReduce algorithms (word count, mean, sorting, inverted index, distributed grep), and how to write MapReduce clients and customize parts of the MapReduce job like input/output formats, partitioners, and distributed caching of files.
Apache Spark is a fast, general engine for large-scale data processing. It supports batch, interactive, and stream processing using a unified API. Spark uses resilient distributed datasets (RDDs), which are immutable distributed collections of objects that can be operated on in parallel. RDDs support transformations like map, filter, and reduce and actions that return final results to the driver program. Spark provides high-level APIs in Scala, Java, Python, and R and an optimized engine that supports general computation graphs for data analysis.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It consists of HDFS for distributed storage and MapReduce for distributed processing. HDFS stores large files across multiple machines and provides high throughput access to application data. MapReduce allows processing of large datasets in parallel by splitting the work into independent tasks called maps and reduces. Companies use Hadoop for applications like log analysis, data warehousing, machine learning, and scientific computing on large datasets.
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Spark is a fast and general engine for large-scale data processing. It runs programs up to 100x faster than Hadoop in memory, and 10x faster on disk. Spark supports Scala, Java, Python and can run on standalone, YARN, or Mesos clusters. It provides high-level APIs for SQL, streaming, machine learning, and graph processing.
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.
This document provides an introduction to MapReduce and Hadoop, including an overview of computing PageRank using MapReduce. It discusses how MapReduce addresses challenges of parallel programming by hiding details of distributed systems. It also demonstrates computing PageRank on Hadoop through parallel matrix multiplication and implementing custom file formats.
Hadoop is a framework for distributed processing of large data sets across clusters of computers. It 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 provides reliable data storage and distributed processing of large data sets.
CSI conference PPT on Performance Analysis of Map/Reduce to compute the frequ...shravanthium111
This document summarizes a student presentation on analyzing the frequency of tweets using MapReduce. It discusses big data, Hadoop frameworks, HDFS, and how MapReduce works. It then describes the student's proposed approach of using Python to extract tweets from Twitter and implement MapReduce to count the frequency of dates in the tweets and output the results.
Hadoop is an open source framework for running large-scale data processing jobs across clusters of computers. It has two main components: HDFS for reliable storage and Hadoop MapReduce for distributed processing. HDFS stores large files across nodes through replication and uses a master-slave architecture. MapReduce allows users to write map and reduce functions to process large datasets in parallel and generate results. Hadoop has seen widespread adoption for processing massive datasets due to its scalability, reliability and ease of use.
Sam believed an apple a day keeps the doctor away. He cut an apple and used a blender to make juice, applying this process to various fruits. Sam got a job at JuiceRUs for his talent in making juice. Later, he implemented a parallel version of his juice-making process to handle large volumes of fruits, realizing several optimizations for efficiency and side effect prevention.
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.
This document summarizes the evolution of Hive, a data warehouse infrastructure built on top of Hadoop. It discusses Hive's origins at Facebook to manage large, unstructured data. Key points include Hive now functioning as a parallel SQL database using Hadoop for storage and execution. The document outlines new features in versions 0.6 and 0.7 like views, dynamic partitioning, and pluggable indexing. It also discusses Hive's roadmap for testing, performance improvements, and new capabilities.
This document discusses embarrassingly parallel problems and the MapReduce programming model. It provides examples of MapReduce functions and how they work. Key points include:
- Embarrassingly parallel problems can be easily split into independent parts that can be solved simultaneously without much communication. MapReduce is well-suited for these types of problems.
- MapReduce involves two functions - map and reduce. Map processes a key-value pair to generate intermediate key-value pairs, while reduce merges all intermediate values associated with the same intermediate key.
- Implementations like Hadoop handle distributed execution, parallelization, data partitioning, and fault tolerance. Users just provide map and reduce functions.
This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
The document describes the Hadoop ecosystem and its core components. It discusses HDFS, which stores large files across clusters and is made up of a NameNode and DataNodes. It also discusses MapReduce, which allows distributed processing of large datasets using a map and reduce function. Other components discussed include Hive, Pig, Impala, and Sqoop.
Hadoop Institutes: kelly technologies are the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
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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.
Processing massive amount of data with Map Reduce using Apache Hadoop - Indi...IndicThreads
This document provides an overview of MapReduce and Hadoop. It describes the Map and Reduce functions, explaining that Map applies a function to each element of a list and Reduce reduces a list to a single value. It gives examples of Map and Reduce using employee salary data. It then discusses Hadoop and its core components HDFS for distributed storage and MapReduce for distributed processing. Key aspects covered include the NameNode, DataNodes, input/output formats, and the job launch process. It also addresses some common questions around small files, large files, and accessing SQL data from Hadoop.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
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2. WHAT IS ?
Distributed computing frame work
For clusters of computers
Thousands of Compute Nodes
Petabytes of data
Open source, Java
Google’s MapReduce inspired Yahoo’s Hadoop.
Now part of Apache group
www.zenithit.co.uk
3. WHAT IS ?
The Apache Hadoop project develops open-source
software for reliable, scalable, distributed
computing. Hadoop includes:
Hadoop Common utilities
Avro: A data serialization system with scripting
languages.
Chukwa: managing large distributed systems.
HBase: A scalable, distributed database for large tables.
HDFS: A distributed file system.
Hive: data summarization and ad hoc querying.
MapReduce: distributed processing on compute clusters.
Pig: A high-level data-flow language for parallel
computation.
ZooKeeper: coordination service for distributed
applications.
www.zenithit.co.uk
5. MAP AND REDUCE
The idea of Map, and Reduce is 40+ year
old
Present in all Functional Programming
Languages.
See, e.g., APL, Lisp and ML
Alternate names for Map: Apply-All
Higher Order Functions
take function definitions as arguments, or
return a function as output
Map and Reduce are higher-order
functions.
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6. MAP: A HIGHER ORDER FUNCTION
F(x: int) returns r: int
Let V be an array of integers.
W = map(F, V)
W[i] = F(V[i]) for all I
i.e., apply F to every element of V
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8. REDUCE: A HIGHER ORDER FUNCTION
reduce also known as
fold, accumulate,
compress or inject
Reduce/fold takes in
a function and folds
it in between the
elements of a list.
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9. FOLD-LEFT IN HASKELL
Definition
foldl f z [] = z
foldl f z (x:xs) = foldl f (f z x) xs
Examples
foldl (+) 0 [1..5] ==15
foldl (+) 10 [1..5] == 25
foldl (div) 7 [34,56,12,4,23] == 0
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10. FOLD-RIGHT IN HASKELL
Definition
foldr f z [] = z
foldr f z (x:xs) = f x (foldr f z xs)
Example
foldr (div) 7 [34,56,12,4,23] == 8
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12. WORD COUNT EXAMPLE
Read text files and count how often words occur.
The input is text files
The output is a text file
each line: word, tab, count
Map: Produce pairs of (word, count)
Reduce: For each word, sum up the counts.
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13. GREP EXAMPLE
Search input files for a given pattern
Map: emits a line if pattern is matched
Reduce: Copies results to output
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14. INVERTED INDEX EXAMPLE
Generate an inverted index of words from a given set
of files
Map: parses a document and emits <word, docId>
pairs
Reduce: takes all pairs for a given word, sorts the
docId values, and emits a <word, list(docId)> pair
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16. EXECUTION ON CLUSTERS
1. Input files split (M splits)
2. Assign Master & Workers
3. Map tasks
4. Writing intermediate data to disk (R regions)
5. Intermediate data read & sort
6. Reduce tasks
7. Return
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17. MAP/REDUCE CLUSTER IMPLEMENTATION
split 0
split 1
split 2
split 3
split 4
Output 0
Output 1
Input
files
Output
files
M map
tasks
R reduce
tasks
Intermediate
files
Several map or
reduce tasks can
run on a single
computer
Each intermediate
file is divided into R
partitions, by
partitioning function
Each reduce task
corresponds to one
partition
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19. FAULT RECOVERY
Workers are pinged by master periodically
Non-responsive workers are marked as failed
All tasks in-progress or completed by failed worker become
eligible for rescheduling
Master could periodically checkpoint
Current implementations abort on master failure
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