Mapreduce일자리

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    967 mapreduce 건의 일자리 확인, 급여 기준: USD

    given a dataset and using only MapReduce framework and python, find the following: • The difference between the maximum and the minimum for each day in the month • The daily minimum • the daily mean and variance • the correlation matrix that describes the monthly correlation among set of columns Using Mahout and python, do the following: • Implement the K-Means clustering algorithm • Find the optimum number (K) of clusters for the K-mean clustering • Plot the elbow graph for K-mean clustering • Compare the different clusters you obtained with different distance measures

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    Hello All, The objective of this subject is to learn how to design a distributed solution of a Big Data problem with help of MapReduce and Hadoop. In fact, MapReduce is a software framework for spreading a single computing job across multiple computers. It is assumed that these jobs take too long to run on a single computer, so you run them on multiple computers to shorten the time. Please stay auto bidders Thank You

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    4 건의 입찰

    Hello All, The objective of this subject is to learn how to design a distributed solution of a Big Data problem with help of MapReduce and Hadoop. In fact, MapReduce is a software framework for spreading a single computing job across multiple computers. It is assumed that these jobs take too long to run on a single computer, so you run them on multiple computers to shorten the time. Please stay auto bidders Thank You

    $105 (Avg Bid)
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    5 건의 입찰

    Hello All, The objective of this subject is to learn how to design a distributed solution of a Big Data problem with help of MapReduce and Hadoop. In fact, MapReduce is a software framework for spreading a single computing job across multiple computers. It is assumed that these jobs take too long to run on a single computer, so you run them on multiple computers to shorten the time. Please stay auto bidders Thank You

    $122 (Avg Bid)
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    Hello All, The objective of this subject is to learn how to design a distributed solution of a Big Data problem with help of MapReduce and Hadoop. In fact, MapReduce is a software framework for spreading a single computing job across multiple computers. It is assumed that these jobs take too long to run on a single computer, so you run them on multiple computers to shorten the time. Please stay auto bidders Thank You

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    6 건의 입찰

    Hello All, The objective of this subject is to learn how to design a distributed solution of a Big Data problem with help of MapReduce and Hadoop. In fact, MapReduce is a software framework for spreading a single computing job across multiple computers. It is assumed that these jobs take too long to run on a single computer, so you run them on multiple computers to shorten the time. Please stay auto bidders Thank You

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    3 건의 입찰

    The objective of this assignment is to learn how to design a distributed solution of a Big Data problem with help of MapReduce and Hadoop. In fact, MapReduce is a software framework for spreading a single computing job across multiple computers. It is assumed that these jobs take too long to run on a single computer, so you run them on multiple computers to shorten the time.

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    1. Implement the straggler solution using the approach below a) Develop a method to detect slow tasks (stragglers) in the Hadoop MapReduce framework using Progress Score (PS), Progress Rate (PR) and Remaining Time (RT) metrics b) Develop a method of selecting idle nodes to replicate detected slow tasks using the CPU time and Memory Status (MS) of the idle nodes. c) Develop a method for scheduling the slow tasks to appropriate idle nodes using CPU time and Memory Status of the idle nodes. 2. A good report on the implementation with graphics 3. A recorded execution process Use any certified data to test the efficiency of the methods

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    Big data project 종료 left

    identify differences in implementations using Spark versus MapReduce, and understand LSH through implementing portions of the algorithm. Your task is to find hospitals with similar characteristics in the impact of COVID-19. Being able to quickly find similar hospitals can be useful for connecting hospitals experiencing difficulties and finding the characteristics of hospitals that have dealt better with the pandemic

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    I have an input text file and a mapper and reducer file which outputs the total count of each word in the text file. I would like to have the mapper and reducer file output only the top 20 words (and their count) with the highest count. The files use and I wanna be able to run them in hadoop.

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    i want map reduce framework need to be implemented in scala

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    I will have couple of simple questions regarding: NLP, FSA, MapReduce, Regular expression, N-Gram. Please let me know if you have expertise in these topics.

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    1) Describe how to implement the following queries in MapReduce: SELECT , , , , FROM Employee as emp, Agent as a WHERE = AND = ; SELECT lo_quantity, COUNT(lo_extendedprice) FROM lineorder, dwdate WHERE lo_orderdate = d_datekey AND d_yearmonth = 'Feb1995' AND lo_discount = 6 GROUP BY lo_quantity; SELECT d_month, AVG(d_year) FROM dwdate GROUP BY d_month ORDER BY AVG(d_year) Consider a Hadoop job that processes an input data file of size equal to 179 disk blocks (179 different blocks, not considering HDFS replication factor). The mapper in this job requires 1 minute to read and fully process a single block of data. Reducer requires 1 second (not minute) to produce an answer for one key worth of values and there are a total of

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    I can successfully run the Mapreduce job on the server. But when I want to send this job as yarn remote client with java(via yarn Rest api), I get the following error. I want to submit this job successfully via Remote Client(Yarn Rest Api.)

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    Write a MapReduce program with python to implement BFS. , and shell script are needed according to the detailed instructions in the uploaded file.

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    ...to you how you pick necessary features and build the training that creates matching courses for job profiles. These are the suggested steps you should follow : Step 1: Setup a Hadoop cluster where the data sets should be stored on the set of Hadoop data nodes. Step 2: Implement a content based recommendation system using MapReduce, i.e. given a job description you should be able to suggest a set of applicable courses. Step 3: Execute the training step of your MapReduce program using the data set stored in the cluster. You can use a subset of the data depending on the system capacity of your Hadoop cluster. You have to use an appropriate subset of features in the data set for effective training. Step 4: Test your recommendation system using a set of requests that execute ...

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    The write-up should include the main problem that can be subdivided into 3 or 4 subproblems. If I'm satisfied, we discuss further on implementation.

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    Using mapreduce recommend the best courses for up-skilling based on a given job description. You can use the data set to train the system and pick some job descriptions not in the training set to test. It is left up to you how you pick necessary features and build the training that creates matching courses for job profiles. Project submission- 1. Code files with comments for your MapReduce implementation of training and query steps 2. . Document the design of your logic including training, query and feature engineering. data csv is too big will share separately

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    Hi Sri Varadan Designers, I noticed your profile and would like to offer you my project. We can discuss any details over chat. I have task to do in Mapreduce in hadoop

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    I want to run pouchdb-node on AWS Lambda. Source code: Detailed Requirements: - Deploy pouchdb-node to AWS Lambda. - Use EFS in storage layer. - Ok to limit concurrency to 1 to avoid race conditions. - Expose via Lambda HTTPS Endpoints (no API Gateway) - The basic PUT / GET functions, replication, and MapReduce must all work Project Deliverables: - Deployment script which packages pouchdb-node and deploys it to AWS using SAM or CloudFormation. Development Process: - I will not give access to my AWS Accounts. - You develop on your own environment and give me completed solution.

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    ...to you how you pick necessary features and build the training that creates matching courses for job profiles. These are the suggested steps you should follow : Step 1: Setup a Hadoop cluster where the data sets should be stored on the set of Hadoop data nodes. Step 2: Implement a content based recommendation system using MapReduce, i.e. given a job description you should be able to suggest a set of applicable courses. Step 3: Execute the training step of your MapReduce program using the data set stored in the cluster. You can use a subset of the data depending on the system capacity of your Hadoop cluster. You have to use an appropriate subset of features in the data set for effective training. Step 4: Test your recommendation system using a set of requests that execute ...

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    ...metrics to show which is a better method. OR ii) Improvement on the methodology used in (a) that will produce a better result. 2. Find a suitable paper on replication of data in hadoop mapreduce framework. a) Implement the methodology used in the paper b) i) Write a program to split identified intermediate results from (1 b(i)) appropriately into 64Mb/128Mb and compare with 2(a) using same metrics to show which is a better method. OR ii) Improvement on the methodology used in 2(a) that will produce a better result 3. Find a suitable paper on allocation strategy of data/tasks to nodes in Hadoop Mapreduce framework. a) Implement the methodology used in the paper b) i) Write a program to reallocate the splits from (2 (b(i)) above to nodes by considering the capability ...

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    ... SQL Concepts, Data Modelling Techniques & Data Engineering Concepts is a must Hands on experience in ETL process, Performance optimization techniques is a must. Candidate should have taken part in Architecture design and discussion. Minimum of 2 years of experience in working with batch processing/ real-time systems using various technologies like Databricks, HDFS, Redshift, Hadoop, Elastic MapReduce on AWS, Apache Spark, Hive/Impala and HDFS, Pig, Kafka, Kinesis, Elasticsearch and NoSQL databases Minimum of 2 years of experience working in Datawarehouse or Data Lake Projects in a role beyond just Data consumption. Minimum of 2 years of extensive working knowledge in AWS building scalable solutions. Equivalent level of experience in Azure or Google Cloud is also acceptable M...

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    Hi Mohd. I hope you are well, I have some Big Data exercises (hive, pig, sed and mapreduce) I would like to know if you can help me

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    consiste en desarrollar un esquema maestro-trabajador, como el esquema de procesamiento mas habitual en los entornos de computacion distribuida, parecido a modelos tan famosos como mapReduce

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    1) Develop an aggregate of these reviews using your knowledge of Hadoop and MapReduce in Microsoft HDInsight. a) Follow the same approach as the Big Data Analytics Workshop (using the wordcount method in HDInsight) to determine the contributory words for each level of rating. b) Present the workflow of using HDInsight (you may use screen captures) along with a summary of findings and any insights for each level of rating. MapReduce documentation for HDInsight is available here 2) Azure data bricks for some insights Provide the following: a) A screen capture of the completed model diagram and any decision you made in training the model. For example, rationale for some of the components used, how many records have been used for training and how many for testing. b) A set of ...

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    consiste en desarrollar un esquema maestro-trabajador, como el esquema de procesamiento mas habitual en los entornos de computacion distribuida, parecido a modelos tan famosos como mapReduce

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    I am looking for a java developer who is -familiar with hadoop architecture and mapreduce scheduling -familiar with modifying the open source packages

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    Big data project 종료 left

    ...7910/DVN/HG7NV7 4. Design, implement and run an Oozie workflow to find out a. the 3 airlines with the highest and lowest probability, respectively, of being on schedule; b. the 3 airports with the longest and shortest average taxi time per flight (both in and out), respectively; and c. the most common reason for flight cancellations. • Requirements: 1. Your workflow must contain at least three MapReduce jobs that run in fully distributed mode. 2. Run your workflow to analyze the entire data set (total 22 years from 1987 to 2008) at one time on two VMs first and then gradually increase the system scale to the maximum allowed number of VMs for at least 5 increment steps, and measure each corresponding workflow execution time. 3. Run your workflow to analyze the data in a prog...

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    ...7910/DVN/HG7NV7 4. Design, implement and run an Oozie workflow to find out a. the 3 airlines with the highest and lowest probability, respectively, of being on schedule; b. the 3 airports with the longest and shortest average taxi time per flight (both in and out), respectively; and c. the most common reason for flight cancellations. • Requirements: 1. Your workflow must contain at least three MapReduce jobs that run in fully distributed mode. 2. Run your workflow to analyze the entire data set (total 22 years from 1987 to 2008) at one time on two VMs first and then gradually increase the system scale to the maximum allowed number of VMs for at least 5 increment steps, and measure each corresponding workflow execution time. 3. Run your workflow to analyze the data in a prog...

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    ...7910/DVN/HG7NV7 4. Design, implement and run an Oozie workflow to find out a. the 3 airlines with the highest and lowest probability, respectively, of being on schedule; b. the 3 airports with the longest and shortest average taxi time per flight (both in and out), respectively; and c. the most common reason for flight cancellations. • Requirements: 1. Your workflow must contain at least three MapReduce jobs that run in fully distributed mode. 2. Run your workflow to analyze the entire data set (total 22 years from 1987 to 2008) at one time on two VMs first and then gradually increase the system scale to the maximum allowed number of VMs for at least 5 increment steps, and measure each corresponding workflow execution time. 3. Run your workflow to analyze the data in a prog...

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    data project 종료 left

    ...7910/DVN/HG7NV7 4. Design, implement and run an Oozie workflow to find out a. the 3 airlines with the highest and lowest probability, respectively, of being on schedule; b. the 3 airports with the longest and shortest average taxi time per flight (both in and out), respectively; and c. the most common reason for flight cancellations. • Requirements: 1. Your workflow must contain at least three MapReduce jobs that run in fully distributed mode. 2. Run your workflow to analyze the entire data set (total 22 years from 1987 to 2008) at one time on two VMs first and then gradually increase the system scale to the maximum allowed number of VMs for at least 5 increment steps, and measure each corresponding workflow execution time. 3. Run your workflow to analyze the data in a prog...

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    data progect 종료 left

    ...7910/DVN/HG7NV7 4. Design, implement and run an Oozie workflow to find out a. the 3 airlines with the highest and lowest probability, respectively, of being on schedule; b. the 3 airports with the longest and shortest average taxi time per flight (both in and out), respectively; and c. the most common reason for flight cancellations. • Requirements: 1. Your workflow must contain at least three MapReduce jobs that run in fully distributed mode. 2. Run your workflow to analyze the entire data set (total 22 years from 1987 to 2008) at one time on two VMs first and then gradually increase the system scale to the maximum allowed number of VMs for at least 5 increment steps, and measure each corresponding workflow execution time. 3. Run your workflow to analyze the data in a prog...

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    Familiarity with Hadoop ecosystem and its components: obviously, a must! Ability to write reliable, manageable, and high-performance code Expertise knowledge of Hadoop HDFS, Hive, Pig, Flume and Sqoop. Working experience in HQL Experience of writing Pig Latin and MapReduce jobs Good knowledge of the concepts of Hadoop. Analytical and problem-solving skills; the implementation of these skills in Big Data domain Understanding of data loading tools such as Flume, Sqoop etc Good knowledge of database principles, practices, structures, and theories

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    Using ansible, harvest twitter data with geo coordinates using twitter API and put into a couchDB. The CouchDB setup may be a single node or based on a cluster setup. The cloud based solution should use 4 VMs with 8 virtual CPUs and 500Gb of volume storage. The data is then combined with other useful geographic data to produce some visualization summary results using MapReduce.

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    Write a MapReduce program to analyze the income data extracted from the 1990 U.S. Census data and determine whether most Americans make more than $50,000 or $50,000 or less a year in 1990. Provide the number of people who made more than $50,000 and the number of people who made $50,000 or less. Download data from http://archive.ics.uci.edu/ml/datasets/Census+Income

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    1 Explain the concept of Big Data and its importance in a modern economy 2 Explain the core architecture and algorithms underpinning big data processing 3 Analyse and visualize large data sets using a range of statistical and big data technologies 4 Critically evaluate, select and employ appropriate tools and technologies for the development of big data applications

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    봉인형 NDA (비밀 유지 계약서)
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    Big Data task with the use of python and hadoop using mapreduce techniques

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    Hadoop, Implementation of MapReduce application

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    Parsing, Cleaning, and Profiling of the attached file by removing hashtags, emoticons, or any redundant data which is not useful for analysis. And MapReduce output will be on HDFS like the image attached named "Output" but should be clean. Tasks: Dataset: Programming: MapReduce with Java Data profiling: Write MapReduce java code to characterize (profile) the data in each column. Data cleaning: Cleaning and Profiling the tweets by removing hashtags, emoticons, or any redundant data which is not useful for analysis. Write MapReduce java code to ETL (extract, transform, load) data source. Drop some unimportant columns, Normalize data in a column, and Detect badly formatted rows.

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    ...con l’architettura utilizzata in tutta l’azienda. Competenze richieste - Laurea in Informatica, Information Technology o equivalente esperienza tecnica. - Almeno 3 anni di esperienza professionale. - Profonda conoscenza ed esperienza in statistica. - Previa esperienza in programmazione, preferibilmente in Python, Kafka o Java e volontà di apprende nuovi linguaggi. - Competenze su Hadoop v2, MapReduce, HDFS. - Buona conoscenza dei Big Data querying tools. - Esperienza con Spark. -Esperienza nel processare grandi quantità di dati, sia strutturati che non, inclusa l’integrazione di dati che provengono da fonti diverse. - Esperienza con NoSQL databases, come Cassandra o MongoDB. - Esperienza con vari sistemi di messagistica, come Kafka o RabbitMQ Du...

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    I need some help with a small task completing some beginning steps in Hadoop with python. Come to the chat and I can explain more. It will not take long, the only thing you need is virtualbox and some som python & Hadoop knowledge.

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    Cleaning and Profiling the tweets by removing hashtags, emoticons, or any redundant data which is not useful for analysis. Organize the use... or any redundant data which is not useful for analysis. Organize the user_location column in a common standard format. Dataset has been attached. Or you can get it from the link below: Tasks: Data profiling: Write MapReduce java code to characterize (profile) the data in each column. Data cleaning: Cleaning and Profiling the tweets by removing hashtags, emoticons, or any redundant data which is not useful for analysis. Write MapReduce java code to ETL (extract, transform, load) data source. Drop some unimportant columns, Normalize data in a column, and Detect badly formatted rows.

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    Detailed summary must contain the main theme of the paper, the approach considered for the work, limitation, current trend in this area and your own judgement on the weakness of the paper. The article is attached separately with this assignment. Summary must include the following: - Understand the contribution of the paper - Understand the technologies - Analyse the current Trend with respect to each paper - Identify the drawback of the paper - Any alternative improvement - Follow IEEE reference style Must be: Excellent in explanation of problem understanding, explanation of Technologies, explanation of Scope of the work, explanation of limitation of the work, explanation of improvements

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    Configure hadoop and perform word count on an input file by using mapreduce on multiple nodes (for example - 1 master and 2 slave nodes).Compare the results obtained by changing the block size each time.

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    The most vital thing in applying the MapReduce framework to real-world problems is to identify what the keys and values are. While there are more advanced approaches, the following hint is a naïve method for inspiring your creativity. You can use the candidate median strings (of a total of 65536) as the keys, and the total matching distances of the respective candidates as the values. That means you will not get the keys from input but generate the keys (i.e., enumerating the candidate median strings) through your code on the fly. Your Map function outputs each median string paired with its total matching distance; your Reduce function reverses each key/value pair such as <k,v>  <v,k>. The output of Reduce will be a sorted list of the reversed pairs and the first...

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    I need help with Hadoop, map reduce and spark. deadline is 24 hrs. please see attached files.

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    ...be used for the application created. The approach involves identifying trading signals in financial time series and capturing the risk associated to these. Such an assessment might support a subsequent evaluation of a trading strategy. Requirements: You must use: (i) Google App Engine, (ii) AWS Lambda, and (iii) one of the other scalable services in AWS: Elastic Compute Cloud (EC2), Elastic MapReduce (EMR) or – should you wish to explore – EC2 Container Service (ECS). Subsequent mentions of scalable services in this document mean Lambda plus your choice of (EC2 or EMR or ECS). ii. Your system must offer a persistent front-end through which the user will initialise (create or ‘warm up’, as necessary) and terminate (as necessary to remove any possible conti...

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    This project is mainly about tracking people which is like a social network friendship recommendation algorithm using MapReduce

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    LetterCount MR 종료 left

    1. Write a MapReduce program to find the frequency of each letter, case insensitive, in any input user-specified files. For example, "The quick brown fox jumps over the lazy dog" as input should generate the following output (letter,count) pairs: (T, 2), (H, 1), (E, 3), etc. 2. Your program also must find the total count of letters, case insensitive, from the input. Generate one extra output pair whose key is the string "total" and whose value is the total count of all letters. 3. Test your program against the 3 attached input files. 4. The input and output will be read/written from/into HDFS. 5. Your program must consist of a single file, namely, LetterCount.java. Declare the mapper and reducer classes as inner classes.

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    mapreduce 커뮤니티 게시글 (최상위)