Pig is a tool for analyzing large datasets. It consists of a compiler that turns user input into a series of MapReduce programs. This allows users to focus on data analysis rather than writing MapReduce programs. Pig Latin is the language used, which compiles user scripts into directed acyclic graphs that are optimized and compiled into MapReduce jobs. Pig can read and write to HDFS as well as local storage. It has two execution modes - local mode for debugging on a local machine and cluster mode for running on Hadoop clusters using MapReduce.
Apache Scoop - Import with Append mode and Last Modified mode Rupak Roy
Familiar with scoop advanced functions like import with append and last modified mode.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
This document provides an introduction to big data and Hadoop. It defines big data as massive amounts of structured and unstructured data that is too large for traditional databases to handle. Hadoop is an open-source framework for storing and processing big data across clusters of commodity hardware. Key components of Hadoop include HDFS for storage, MapReduce for parallel processing, and an ecosystem of tools like Hive, Pig, and Spark. The document outlines the architecture of Hadoop, including the roles of the master node, slave nodes, and clients. It also explains concepts like rack awareness, MapReduce jobs, and how files are stored in HDFS in blocks across nodes.
Understand and implement the terminology of why partitioning the table is important and the Hive Query Language (HQL)
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Apache MapReduce is a programming model and software framework for processing vast amounts of data in parallel. It works by breaking jobs into map and reduce tasks that can be executed in parallel on large clusters. The map tasks take input data and convert it into intermediate key-value pairs, and the reduce tasks combine these intermediate outputs to produce the final results. As an example, a MapReduce job is presented that analyzes weather data to find the maximum recorded temperature for each year, by having mappers extract the year and temperature from records and reducers find the maximum temperature for each year.
Apache Pig is a platform for analyzing large datasets that operates on top of Hadoop. It provides a high-level language called Pig Latin that allows users to express data analysis programs, which Pig then compiles into MapReduce jobs for execution. The main components of the Pig architecture are the Pig Latin parser and optimizer, which generate a logical plan, and the compiler, which converts this into a physical execution plan of MapReduce jobs. Pig aims to simplify big data analysis for users by hiding the complexity of MapReduce.
Pig is a tool for analyzing large datasets. It consists of a compiler that turns user input into a series of MapReduce programs. This allows users to focus on data analysis rather than writing MapReduce programs. Pig Latin is the language used, which compiles user scripts into directed acyclic graphs that are optimized and compiled into MapReduce jobs. Pig can read and write to HDFS as well as local storage. It has two execution modes - local mode for debugging on a local machine and cluster mode for running on Hadoop clusters using MapReduce.
Apache Scoop - Import with Append mode and Last Modified mode Rupak Roy
Familiar with scoop advanced functions like import with append and last modified mode.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
This document provides an introduction to big data and Hadoop. It defines big data as massive amounts of structured and unstructured data that is too large for traditional databases to handle. Hadoop is an open-source framework for storing and processing big data across clusters of commodity hardware. Key components of Hadoop include HDFS for storage, MapReduce for parallel processing, and an ecosystem of tools like Hive, Pig, and Spark. The document outlines the architecture of Hadoop, including the roles of the master node, slave nodes, and clients. It also explains concepts like rack awareness, MapReduce jobs, and how files are stored in HDFS in blocks across nodes.
Understand and implement the terminology of why partitioning the table is important and the Hive Query Language (HQL)
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Apache MapReduce is a programming model and software framework for processing vast amounts of data in parallel. It works by breaking jobs into map and reduce tasks that can be executed in parallel on large clusters. The map tasks take input data and convert it into intermediate key-value pairs, and the reduce tasks combine these intermediate outputs to produce the final results. As an example, a MapReduce job is presented that analyzes weather data to find the maximum recorded temperature for each year, by having mappers extract the year and temperature from records and reducers find the maximum temperature for each year.
Apache Pig is a platform for analyzing large datasets that operates on top of Hadoop. It provides a high-level language called Pig Latin that allows users to express data analysis programs, which Pig then compiles into MapReduce jobs for execution. The main components of the Pig architecture are the Pig Latin parser and optimizer, which generate a logical plan, and the compiler, which converts this into a physical execution plan of MapReduce jobs. Pig aims to simplify big data analysis for users by hiding the complexity of MapReduce.
The document discusses Pig Latin scripts and how to execute them. It provides examples of multi-line and single-line comments in Pig Latin scripts. It also describes how to execute Pig Latin scripts locally or using MapReduce and how to execute scripts that reside in HDFS. Finally, it summarizes key differences between Pig Latin and SQL and describes common relational operators used in Pig Latin.
Get acquainted with a distributed, reliable tool/service for collecting a large amount of streaming data to centralized storage with their architecture.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
take care!
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 provides an overview and summary of Hadoop and related big data technologies:
- It describes what big data is in terms of volume, velocity, and variety of data. Hadoop is a framework for distributed storage and processing of large datasets across clusters of computers.
- Core Hadoop components like HDFS for storage and MapReduce for processing are introduced. Popular Hadoop tools like Pig, Hive, Sqoop and testing approaches are also summarized briefly.
- The document provides examples of using MapReduce jobs, PigLatin scripts, loading data into Hive tables and exporting data between HDFS and MySQL using Sqoop. It highlights key differences between Hive and Pig as well.
Hadoop - Introduction to map reduce programming - Reunião 12/04/2014soujavajug
Aaron Myers introduces MapReduce and Hadoop. MapReduce is a distributed programming paradigm that allows processing of large datasets across clusters. It works by splitting data, distributing it across nodes, processing it in parallel using map and reduce functions, and collecting the results. Hadoop is an open source software framework for distributed storage and processing of big data using MapReduce. It includes HDFS for storage and Hadoop MapReduce for distributed computing. Developers write MapReduce jobs in Java by implementing map and reduce functions.
This document provides an overview of Hadoop MapReduce concepts including:
- The MapReduce paradigm with mappers processing input splits in parallel during the map phase and reducers processing grouped intermediate outputs in parallel during the reduce phase.
- Key classes involved include the main driver class, mapper class, reducer class, input format class, output format class, and job configuration class.
- An example word count job is described that counts the number of occurrences of each word by emitting (word, 1) pairs from mappers and summing the counts by word from reducers.
- The timeline of a MapReduce job including map and reduce phases is covered along with details of map and reduce task execution.
The Next generation of Hadoop version from the Apache Software Foundation with a detailed comparison of Map-Reduce V1 versus Yarn and the Architecture with important updates
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Map Reduce is a parallel and distributed approach developed by Google for processing large data sets. It has two key components - the Map function which processes input data into key-value pairs, and the Reduce function which aggregates the intermediate output of the Map into a final result. Input data is split across multiple machines which apply the Map function in parallel, and the Reduce function is applied to aggregate the outputs.
Well illustrated with definitions of Apache Hive with its architecture workflows plus with the types of data available for Apache Hive
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Mastering Hadoop Map Reduce - Custom Types and Other Optimizationsscottcrespo
Mastering Hadoop Map Reduce was a presentation I gave to Orlando Data Science on April 23, 2015. The presentation provides a clear overview of how Hadoop Map Reduce works, and then dives into more advanced topics of how to optimize runtime performance and implement custom data types.
The examples are written in Python and Java, and the presentation walks through how to create an n-gram count map reduce program using custom data types.
You can get the full source code for the examples on my Github! http://www.github.com/scottcrespo/ngrams
This document provides an introduction to distributed programming and Apache Hadoop. It discusses sequential, asynchronous, concurrent, and distributed programming. It then describes how Hadoop works as an open source framework for distributed applications, using a cluster of nodes to scale linearly and process large amounts of data reliably in a simple way. Key concepts covered include MapReduce programming, Hadoop installation and usage, and working with files in the Hadoop Distributed File System.
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.
Pig is a tool that makes Hadoop programming easier for non-Java users. It provides a high-level declarative query language called Pig Latin that is compiled into MapReduce jobs. Pig Latin allows users to express data analysis logic without dealing with low-level MapReduce code. It was developed at Yahoo! to help users focus on analyzing large datasets rather than writing Java code. Pig includes features like automatic optimization and extensibility to make it smarter and more useful for developers.
The document introduces MapReduce, describing how it allows for parallel processing of large datasets. MapReduce works by splitting data into smaller chunks that are processed (mapped) in parallel by worker nodes, and then combining (reducing) the results. The document outlines the Map and Reduce functions, and discusses how Hadoop is an open-source implementation of MapReduce that allows distributed processing of semi-structured data across clusters of machines.
Pig is a tool used to process large datasets and automate ETL processes for unstructured data. It uses a procedural language called Pig Latin. Pig relations are non-persistent and can be executed in local, MapReduce, or Tez modes. Key operators in Pig include LOAD, STORE, DUMP, and DESCRIBE.
The document discusses MapReduce programs for analyzing weather data. It describes:
1) The MapReduce framework which breaks jobs into map and reduce tasks to process large datasets in parallel across clusters.
2) A sample weather dataset from NOAA containing records with temperature and other weather readings from stations.
3) An example MapReduce program to find the maximum recorded temperature each year from the data using map tasks to extract temperatures and reduce tasks to find the yearly maximum values.
This document discusses data types and formats used in Hadoop MapReduce. It covers basic data types like IntWritable and Text that support serialization and comparability. It also describes common file formats like XML, JSON, SequenceFiles, Avro, Parquet, and how to implement custom formats like CSV. Input/output classes are discussed along with how different formats can be used in MapReduce jobs.
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.
MapReduce is a programming model for processing large datasets in parallel. It works by breaking the dataset into independent chunks which are processed by the map function, and then grouping the output of the maps into partitions to be processed by the reduce function. Hadoop uses MapReduce to provide fault tolerance by restarting failed tasks and monitoring the JobTracker and TaskTrackers. MapReduce programs can be written in languages other than Java using Hadoop Streaming.
The WordCount and Sort examples demonstrate basic MapReduce algorithms in Hadoop. WordCount counts the frequency of words in a text document by having mappers emit (word, 1) pairs and reducers sum the counts. Sort uses an identity mapper and reducer to simply sort the input files by key. Both examples read from and write to HDFS, and can be run on large datasets to benchmark a Hadoop cluster's sorting performance.
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.
The document discusses Pig Latin scripts and how to execute them. It provides examples of multi-line and single-line comments in Pig Latin scripts. It also describes how to execute Pig Latin scripts locally or using MapReduce and how to execute scripts that reside in HDFS. Finally, it summarizes key differences between Pig Latin and SQL and describes common relational operators used in Pig Latin.
Get acquainted with a distributed, reliable tool/service for collecting a large amount of streaming data to centralized storage with their architecture.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
take care!
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 provides an overview and summary of Hadoop and related big data technologies:
- It describes what big data is in terms of volume, velocity, and variety of data. Hadoop is a framework for distributed storage and processing of large datasets across clusters of computers.
- Core Hadoop components like HDFS for storage and MapReduce for processing are introduced. Popular Hadoop tools like Pig, Hive, Sqoop and testing approaches are also summarized briefly.
- The document provides examples of using MapReduce jobs, PigLatin scripts, loading data into Hive tables and exporting data between HDFS and MySQL using Sqoop. It highlights key differences between Hive and Pig as well.
Hadoop - Introduction to map reduce programming - Reunião 12/04/2014soujavajug
Aaron Myers introduces MapReduce and Hadoop. MapReduce is a distributed programming paradigm that allows processing of large datasets across clusters. It works by splitting data, distributing it across nodes, processing it in parallel using map and reduce functions, and collecting the results. Hadoop is an open source software framework for distributed storage and processing of big data using MapReduce. It includes HDFS for storage and Hadoop MapReduce for distributed computing. Developers write MapReduce jobs in Java by implementing map and reduce functions.
This document provides an overview of Hadoop MapReduce concepts including:
- The MapReduce paradigm with mappers processing input splits in parallel during the map phase and reducers processing grouped intermediate outputs in parallel during the reduce phase.
- Key classes involved include the main driver class, mapper class, reducer class, input format class, output format class, and job configuration class.
- An example word count job is described that counts the number of occurrences of each word by emitting (word, 1) pairs from mappers and summing the counts by word from reducers.
- The timeline of a MapReduce job including map and reduce phases is covered along with details of map and reduce task execution.
The Next generation of Hadoop version from the Apache Software Foundation with a detailed comparison of Map-Reduce V1 versus Yarn and the Architecture with important updates
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Map Reduce is a parallel and distributed approach developed by Google for processing large data sets. It has two key components - the Map function which processes input data into key-value pairs, and the Reduce function which aggregates the intermediate output of the Map into a final result. Input data is split across multiple machines which apply the Map function in parallel, and the Reduce function is applied to aggregate the outputs.
Well illustrated with definitions of Apache Hive with its architecture workflows plus with the types of data available for Apache Hive
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Mastering Hadoop Map Reduce - Custom Types and Other Optimizationsscottcrespo
Mastering Hadoop Map Reduce was a presentation I gave to Orlando Data Science on April 23, 2015. The presentation provides a clear overview of how Hadoop Map Reduce works, and then dives into more advanced topics of how to optimize runtime performance and implement custom data types.
The examples are written in Python and Java, and the presentation walks through how to create an n-gram count map reduce program using custom data types.
You can get the full source code for the examples on my Github! http://www.github.com/scottcrespo/ngrams
This document provides an introduction to distributed programming and Apache Hadoop. It discusses sequential, asynchronous, concurrent, and distributed programming. It then describes how Hadoop works as an open source framework for distributed applications, using a cluster of nodes to scale linearly and process large amounts of data reliably in a simple way. Key concepts covered include MapReduce programming, Hadoop installation and usage, and working with files in the Hadoop Distributed File System.
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.
Pig is a tool that makes Hadoop programming easier for non-Java users. It provides a high-level declarative query language called Pig Latin that is compiled into MapReduce jobs. Pig Latin allows users to express data analysis logic without dealing with low-level MapReduce code. It was developed at Yahoo! to help users focus on analyzing large datasets rather than writing Java code. Pig includes features like automatic optimization and extensibility to make it smarter and more useful for developers.
The document introduces MapReduce, describing how it allows for parallel processing of large datasets. MapReduce works by splitting data into smaller chunks that are processed (mapped) in parallel by worker nodes, and then combining (reducing) the results. The document outlines the Map and Reduce functions, and discusses how Hadoop is an open-source implementation of MapReduce that allows distributed processing of semi-structured data across clusters of machines.
Pig is a tool used to process large datasets and automate ETL processes for unstructured data. It uses a procedural language called Pig Latin. Pig relations are non-persistent and can be executed in local, MapReduce, or Tez modes. Key operators in Pig include LOAD, STORE, DUMP, and DESCRIBE.
The document discusses MapReduce programs for analyzing weather data. It describes:
1) The MapReduce framework which breaks jobs into map and reduce tasks to process large datasets in parallel across clusters.
2) A sample weather dataset from NOAA containing records with temperature and other weather readings from stations.
3) An example MapReduce program to find the maximum recorded temperature each year from the data using map tasks to extract temperatures and reduce tasks to find the yearly maximum values.
This document discusses data types and formats used in Hadoop MapReduce. It covers basic data types like IntWritable and Text that support serialization and comparability. It also describes common file formats like XML, JSON, SequenceFiles, Avro, Parquet, and how to implement custom formats like CSV. Input/output classes are discussed along with how different formats can be used in MapReduce jobs.
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.
MapReduce is a programming model for processing large datasets in parallel. It works by breaking the dataset into independent chunks which are processed by the map function, and then grouping the output of the maps into partitions to be processed by the reduce function. Hadoop uses MapReduce to provide fault tolerance by restarting failed tasks and monitoring the JobTracker and TaskTrackers. MapReduce programs can be written in languages other than Java using Hadoop Streaming.
The WordCount and Sort examples demonstrate basic MapReduce algorithms in Hadoop. WordCount counts the frequency of words in a text document by having mappers emit (word, 1) pairs and reducers sum the counts. Sort uses an identity mapper and reducer to simply sort the input files by key. Both examples read from and write to HDFS, and can be run on large datasets to benchmark a Hadoop cluster's sorting performance.
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.
Hadoop is an open source framework for distributed storage and processing of vast amounts of data across clusters of computers. It uses a master-slave architecture with a single JobTracker master and multiple TaskTracker slaves. The JobTracker schedules tasks like map and reduce jobs on TaskTrackers, which each run task instances in separate JVMs. It monitors task progress and reschedules failed tasks. Hadoop uses MapReduce programming model where the input is split and mapped in parallel, then outputs are shuffled, sorted, and reduced to form the final results.
This document provides an overview of MapReduce concepts including:
1. It describes the anatomy of MapReduce including the map and reduce phases, intermediate data, and final outputs.
2. It explains key MapReduce terminology like jobs, tasks, task attempts, and the roles of the master and slave nodes.
3. It discusses MapReduce data types, input formats, record readers, partitioning, sorting, and output formats.
1. The document discusses concepts related to managing big data using Hadoop including data formats, analyzing data with MapReduce, scaling out, data flow, Hadoop streaming, and Hadoop pipes.
2. Hadoop allows for distributed processing of large datasets across clusters of computers using a simple programming model. It scales out to large clusters of commodity hardware and manages data processing and storage automatically.
3. Hadoop streaming and Hadoop pipes provide interfaces for running MapReduce jobs using any programming language, such as Python or C++, instead of just Java. This allows developers to use the language of their choice.
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.
In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.
MapReduce is a programming model for processing large datasets in a distributed environment. It consists of a map function that processes input key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same key. It allows for parallelization of computations across large clusters. Example applications include word count, sorting, and indexing web links. Hadoop is an open source implementation of MapReduce that runs on commodity hardware.
This document summarizes key Hadoop configuration parameters that affect MapReduce job performance and provides suggestions for optimizing these parameters under different conditions. It describes the MapReduce workflow and phases, defines important parameters like dfs.block.size, mapred.compress.map.output, and mapred.tasktracker.map/reduce.tasks.maximum. It explains how to configure these parameters based on factors like cluster size, data and task complexity, and available resources. The document also discusses other performance aspects like temporary space, JVM tuning, and reducing reducer initialization overhead.
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.
MapReduce is a programming model for processing large datasets in a distributed system. It involves a map step that performs filtering and sorting, and a reduce step that performs summary operations. Hadoop is an open-source framework that supports MapReduce. It orchestrates tasks across distributed servers, manages communications and fault tolerance. Main steps include mapping of input data, shuffling of data between nodes, and reducing of shuffled data.
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.
The document provides an overview of MapReduce, including:
1) MapReduce is a programming model and implementation that allows for large-scale data processing across clusters of computers. It handles parallelization, distribution, and reliability.
2) The programming model involves mapping input data to intermediate key-value pairs and then reducing by key to output results.
3) Example uses of MapReduce include word counting and distributed searching of text.
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.
The document discusses MapReduce, a programming model used in distributed computing. It has two main phases - the Map phase and the Reduce phase. In the Map phase, data is processed and broken down into key-value pairs. The Reduce phase combines these pairs into smaller sets of data. MapReduce programs execute in three stages - the Map stage processes input data in parallel, the Shuffle stage consolidates records from Mappers, and the Reduce stage aggregates output to summarize the dataset. Hadoop is an open-source software that uses this model with a master-worker architecture and the HDFS distributed file system.
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
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/
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document provides an overview of MapReduce and Apache Hadoop. It discusses the history and components of Hadoop, including HDFS and MapReduce. It then walks through an example MapReduce job, the WordCount algorithm, to illustrate how MapReduce works. The WordCount example counts the frequency of words in documents by having mappers emit <word, 1> pairs and reducers sum the counts for each word.
Hierarchical Clustering - Text Mining/NLPRupak Roy
Documented Hierarchical clustering using Hclust for text mining, natural language processing.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Clustering K means and Hierarchical - NLPRupak Roy
Classify to cluster the natural language processing via K means, Hierarchical and more.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Network Analysis using 3D interactive plots along with their steps for implementation.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Widely accepted steps for sentiment analysis.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Detailed Pattern Search using regular expressions using grepl, grep, grepexpr and Replace with sub, gsub and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Detailed documented with the definition of text mining along with challenges, implementing modeling techniques, word cloud and much more.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Bundled with the documentation to the introduction of Apache Hbase to the configuration.
Let me know if anything is required. Happy to help.
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Installing Apache Hive, internal and external table, import-export Rupak Roy
Perform Hive installation with internal and external table import-export and much more
Let me know if anything is required. Happy to help.
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Automate the complete big data process from import to export data from HDFS to RDBMS like sql with apache sqoop
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Get acquainted with the differences in scoop, the added advantages with hands-on implementation
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Enhance analysis with detailed examples of Relational Operators - II includes Foreash, Filter, Join, Co-Group, Union and much more.
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Passing Parameters using File and Command LineRupak Roy
Explore well versed other functions, flatten operator and other available options to pass parameters
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Get to know the implementation of apache Pig relational operators like order, limit, distinct, groupby.
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This document discusses various ways to reference and select fields or columns from a Pig dataset:
- Fields can be referenced by position (e.g. $0, $1) or by name. When the schema is unknown, position is safer.
- The entire range of fields can be selected using .. syntax (e.g. $0..$3).
- Fields can be cast to different types (e.g. (chararray)$4) during selection.
- Filters should reference fields by position rather than name when the schema is unknown, to avoid errors from missing or misplaced values.
Pig Latin, Data Model with Load and Store FunctionsRupak Roy
Documented with the two data types of PiG Data Model including Complex PIG data types in detail.
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Get to know the configuration with Hadoop installation types and also handling of the HDFS files.
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This document provides an example of creating geospatial plots in R using ggmap() and ggplot2. It includes 3 steps: 1) Get the map using get_map(), 2) Plot the map using ggmap(), and 3) Plot the dataset on the map using ggplot2 objects like geom_point(). The example loads crime and neighborhood datasets, filters the data, gets a map of Seattle, and plots crime incidents and dangerous neighborhoods on the map. It demonstrates various geospatial plotting techniques like adjusting point transparency, adding density estimates, labeling points, and faceting by crime type.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
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Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Fueling AI with Great Data with Airbyte WebinarZilliz
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
3. Terminology Explanations:
Input format: defines the amount of tasks the individual
maptask will process i.e. the input splits.
Record Reader: reads and converts the data one line at a time
into key value pairs from the input spit for the Mapper function.
By default the Map function reads data in text input format.
Another feature of the record reader is whenever the HDFs splits
the data into blocks of 64mb(default) and it doesn’t consider the
type of data while creating a logical split to load the file into
HDFS. So the first block might terminate a logical record for
example in the middle of a line or a row of a text file.
In such case the record reader ensures if there is any break in a
logical record it will get the remaining part from the next
block and makes it a part of input split.
Driver class function binds the Map and the Reduce Function
and initiates the process.
Rupak Roy
4. A Combiner is also knows as Semi- reducer that helps
aggregating the segregate data of map key-value outputs
which helps in increase in performance by reducing the
amount of data being sent over the network.
Example: instead of sending 3 key value pairs like
<bob,1>
<bob,1>
<bob,1>
It will simply send the aggregated key value pairs like
<bob,3>
Combiner is still an optional class, since it has some limitations
like it doesn’t works with arithmetic functions like mean,
median, mode.
Rupak Roy
5. Example 1:
Max of (12,6,4,9) is 12
With combiner:
Map job1 = max(12,6) = 12
Map job2 = max(4,9) = 9
Reducer = max(12,9)=12
Example 2:
mean of (12,6,4,9) is 7.75
With combiner:
Map job1= mean(12,6)=9
Map job2 = mean(4,9)=6.5
Reducer= mean(9,6.5)= 15.5 which is wrong.
Combiner
Rupak Roy
6. Partitioner partitions the output of map
keyvalue outputs. Or simply we can say
partitioner divides the data for the available
number of reducers to process.
Output Format: defines the location of the
processed data to be stored.
Record Writer: this is the last phase where every
key –value pair output from the Reducer is
forward to its Output Format defined location.
Rupak Roy
9. How to run MapReduce Jar File
Save the MapReduce Programming in Java .jar file.
Then copy/store the .jar file in HDFS
next run the .jar file
hadoop jar test.jar Demo /user/data/input /user/data/output
i.e. hadoop jar file.jar DriverProgramName(Demo) /sourceDirectory /destinationDirectiory
Rupak Roy
10. Output files of MapReduce job
_Success: On the successful completion of a job,
the MapReduce runtime creates a _Success file.
This file is used for applications that need to see if
the results are successfully completed or not. One
such example is job scheduling systems like OOZIE
_logs: it will contain all the log details of the event.
part-m-00000: the ‘m’ stands for Map-only jobs i.e.
only mapper is used to complete the job
part-r-00000: the ‘r’ stands for Reducer jobs i.e the
reducer is also used to complete the job
Rupak Roy
11. Next
We will learn a high level language call PIG
for analyzing massive amount of data.
Rupak Roy