This document provides an overview of big data concepts and Hadoop. It discusses the four V's of big data - volume, velocity, variety, and veracity. It then describes how Hadoop uses MapReduce and HDFS to process and store large datasets in a distributed, fault-tolerant and scalable manner across commodity hardware. Key components of Hadoop include the HDFS file system and MapReduce framework for distributed processing of large datasets in parallel.
The document discusses HDFS architecture and components. It describes how HDFS uses NameNodes and DataNodes to store and retrieve file data in a distributed manner across clusters. The NameNode manages the file system namespace and regulates access to files by clients. DataNodes store file data in blocks and replicate them for fault tolerance. The document outlines the write and read workflows in HDFS and how NameNodes and DataNodes work together to manage data storage and access.
The slides were created for one University Program on Apache Hadoop + Apache Apex workshop.
It explains almost all the hdfs related commands in details along with the examples.
The document provides an overview of the Hadoop Distributed File System (HDFS). It describes HDFS's master-slave architecture with a single NameNode master and multiple DataNode slaves. The NameNode manages filesystem metadata and data placement, while DataNodes store data blocks. The document outlines HDFS components like the SecondaryNameNode, DataNodes, and how files are written and read. It also discusses high availability solutions, operational tools, and the future of HDFS.
The Hadoop Distributed File System (HDFS) has a master/slave architecture with a single NameNode that manages the file system namespace and regulates client access, and multiple DataNodes that store and retrieve blocks of data files. The NameNode maintains metadata and a map of blocks to files, while DataNodes store blocks and report their locations. Blocks are replicated across DataNodes for fault tolerance following a configurable replication factor. The system uses rack awareness and preferential selection of local replicas to optimize performance and bandwidth utilization.
A simple replication-based mechanism has been used to achieve high data reliability of Hadoop Distributed File System (HDFS). However, replication based mechanisms have high degree of disk storage requirement since it makes copies of full block without consideration of storage size. Studies have shown that erasure-coding mechanism can provide more storage space when used as an alternative to replication. Also, it can increase write throughput compared to replication mechanism. To improve both space efficiency and I/O performance of the HDFS while preserving the same data reliability level, we propose HDFS+, an erasure coding based Hadoop Distributed File System. The proposed scheme writes a full block on the primary DataNode and then performs erasure coding with Vandermonde-based Reed-Solomon algorithm that divides data into m data fragments and encode them into n data fragments (n>m), which are saved in N distinct DataNodes such that the original object can be reconstructed from any m fragments. The experimental results show that our scheme can save up to 33% of storage space while outperforming the original scheme in write performance by 1.4 times. Our scheme provides the same read performance as the original scheme as long as data can be read from the primary DataNode even under single-node or double-node failure. Otherwise, the read performance of the HDFS+ decreases to some extent. However, as the number of fragments increases, we show that the performance degradation becomes negligible.
Hadoop DFS consists of HDFS for storage and MapReduce for processing. HDFS provides massive storage, fault tolerance through data replication, and high throughput access to data. It uses a master-slave architecture with a NameNode managing the file system namespace and DataNodes storing file data blocks. The NameNode ensures data reliability through policies that replicate blocks across racks and nodes. HDFS provides scalability, flexibility and low-cost storage of large datasets.
Hadoop is a distributed processing framework for large data sets across clusters of commodity hardware. It has two main components: HDFS for reliable data storage, and MapReduce for distributed processing of large data sets. Hadoop can scale from single servers to thousands of machines, handling data measuring petabytes with very high throughput. It provides reliability even if individual machines fail, and is easy to set up and manage.
Coordinating Metadata Replication: Survival Strategy for Distributed SystemsKonstantin V. Shvachko
Hadoop Summit, April 2014
Amsterdam, Netherlands
Just as the survival of living species depends on the transfer of essential knowledge within the community and between generations, the availability and reliability of a distributed computer system relies upon consistent replication of core metadata between its components. This presentation will highlight the implementation of a replication technique for the namespace of the Hadoop Distributed File System (HDFS). In HDFS, the namespace represented by the NameNode is decoupled from the data storage layer. While the data layer is conventionally replicated via block replication, the namespace remains a performance and availability bottleneck. Our replication technique relies on quorum-based consensus algorithms and provides an active-active model of high availability for HDFS where metadata requests (reads and writes) can be load-balanced between multiple instances of the NameNode. This session will also cover how the same techniques are extended to provide replication of metadata and data between geographically distributed data centers, providing global disaster recovery and continuous availability. Finally, we will review how consistent replication can be applied to advance other systems in the Apache Hadoop stack; e.g., how in HBase coordinated updates of regions selectively replicated on multiple RegionServers improve availability and overall cluster throughput.
The document provides an overview of the Hadoop Distributed File System (HDFS). It describes key HDFS concepts including its design goals, block and rack awareness, file write and read processes, checkpointing, and safe mode operation. HDFS allows for reliable storage of very large files across commodity hardware and provides high throughput access to application data.
HDFS allows storing large amounts of data across multiple machines by splitting files into blocks and replicating those blocks for reliability. It addresses challenges of big data like volume, velocity, and variety by providing a distributed storage solution that scales horizontally. Traditional systems are limited by network bandwidth, storage capacity of individual machines, and single points of failure. HDFS introduces a scalable architecture with a master NameNode and slave DataNodes that stores data blocks, addressing these issues through data distribution and fault tolerance.
The document discusses the Hadoop Distributed File System (HDFS), which was created by Doug Cutting to address the need for large-scale data processing. HDFS is designed for streaming data across commodity hardware and uses a master/slave architecture with one NameNode master and multiple DataNodes. The NameNode manages the file system namespace and regulates access to files by clients via the DataNodes, which store data blocks and ensure replication for fault tolerance.
The document describes HDFS's implementation of file truncation, which allows reducing a file's length. It evolved HDFS's write-once semantics to support data mutation. Truncate uses the lease and block recovery framework to truncate block replicas in-place, except when snapshots exist, where it uses "copy-on-truncate" to preserve the snapshot. The truncate operation returns immediately after updating metadata, while block adjustments occur in the background.
Ravi Namboori Hadoop & HDFS ArchitectureRavi namboori
HDFS Architecture: An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients.
Here we can see the figure explaining about all by a cisco evangelist Ravi Namboori.
The document starts with the introduction for Hadoop and covers the Hadoop 1.x / 2.x services (HDFS / MapReduce / YARN).
It also explains the architecture of Hadoop, the working of Hadoop distributed file system and MapReduce programming model.
Dynamic Namespace Partitioning with Giraffa File SystemDataWorks Summit
Giraffa is a distributed file system that utilizes features of HDFS and HBase. It stores file and directory metadata in an HBase table to allow for dynamic namespace partitioning across region servers. File data continues to be stored in HDFS data nodes to leverage HDFS's efficient data streaming. The goal is to build upon existing Hadoop components like HDFS and HBase to create a scalable file system without introducing single points of failure, while minimizing changes to existing systems.
In this session you will learn:
History of Hadoop
Hadoop Ecosystem
Hadoop Animal Planet
What is Hadoop?
Distinctions of Hadoop
Hadoop Components
The Hadoop Distributed Filesystem
Design of HDFS
When Not to use Hadoop?
HDFS Concepts
Anatomy of a File Read
Anatomy of a File Write
Replication & Rack awareness
Mapreduce Components
Typical Mapreduce Job
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
The document summarizes Hadoop HDFS, which is a distributed file system designed for storing large datasets across clusters of commodity servers. It discusses that HDFS allows distributed processing of big data using a simple programming model. It then explains the key components of HDFS - the NameNode, DataNodes, and HDFS architecture. Finally, it provides some examples of companies using Hadoop and references for further information.
The document provides an overview of the Hadoop architecture including its core components like HDFS for distributed storage, MapReduce for distributed processing, and an explanation of how data is stored in blocks and replicated across nodes in the cluster. Key aspects of HDFS such as the namenode, datanodes, and secondary namenode functions are described as well as how Hadoop implementations like Pig and Hive provide interfaces for data processing.
Storage Systems for big data - HDFS, HBase, and intro to KV Store - RedisSameer Tiwari
There is a plethora of storage solutions for big data, each having its own pros and cons. The objective of this talk is to delve deeper into specific classes of storage types like Distributed File Systems, in-memory Key Value Stores, Big Table Stores and provide insights on how to choose the right storage solution for a specific class of problems. For instance, running large analytic workloads, iterative machine learning algorithms, and real time analytics.
The talk will cover HDFS, HBase and brief introduction to Redis
Hadoop Distributed File System (HDFS) is a distributed file system that stores large datasets across commodity hardware. It is highly fault tolerant, provides high throughput, and is suitable for applications with large datasets. HDFS uses a master/slave architecture where a NameNode manages the file system namespace and DataNodes store data blocks. The NameNode ensures data replication across DataNodes for reliability. HDFS is optimized for batch processing workloads where computations are moved to nodes storing data blocks.
Hadoop Distributed File System (HDFS) evolves from a MapReduce-centric storage system to a generic, cost-effective storage infrastructure where HDFS stores all data of inside the organizations. The new use case presents a new sets of challenges to the original HDFS architecture. One challenge is to scale the storage management of HDFS - the centralized scheme within NameNode becomes a main bottleneck which limits the total number of files stored. Although a typical large HDFS cluster is able to store several hundred petabytes of data, it is inefficient to handle large amounts of small files under the current architecture.
In this talk, we introduce our new design and in-progress work that re-architects HDFS to attack this limitation. The storage management is enhanced to a distributed scheme. A new concept of storage container is introduced for storing objects. HDFS blocks are stored and managed as objects in the storage containers instead of being tracked only by NameNode. Storage containers are replicated across DataNodes using a newly-developed high-throughput protocol based on the Raft consensus algorithm. Our current prototype shows that under the new architecture the storage management of HDFS scales 10x better, demonstrating that HDFS is capable of storing billions of files.
This document provides an overview and lessons learned from Hadoop. It discusses why Hadoop is used, how MapReduce and HDFS work, tips for integration and operations, and the outlook for the Hadoop community moving forward with real-time capabilities and refined APIs. Key takeaways include only using Hadoop if necessary, fully understanding your data pipeline, and "unboxing the black box" of Hadoop.
Distributed Computing with Apache Hadoop is a technology overview that discusses:
1) Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of commodity hardware.
2) Hadoop addresses limitations of traditional distributed computing with an architecture that scales linearly by adding more nodes, moves computation to data instead of moving data, and provides reliability even when hardware failures occur.
3) Core Hadoop components include the Hadoop Distributed File System for storage, and MapReduce for distributed processing of large datasets in parallel on multiple machines.
Hadoop Operations - Best Practices from the FieldDataWorks Summit
This document discusses best practices for Hadoop operations based on analysis of support cases. Key learnings include using HDFS ACLs and snapshots to prevent accidental data deletion and improve recoverability. HDFS improvements like pausing block deletion and adding diagnostics help address incidents around namespace mismatches and upgrade failures. Proper configuration of hardware, JVM settings, and monitoring is also emphasized.
The NameNode was experiencing high load and instability after being restarted. Graphs showed unknown high load between checkpoints on the NameNode. DataNode logs showed repeated 60000 millisecond timeouts in communication with the NameNode. Thread dumps revealed NameNode server handlers waiting on the same lock, indicating a bottleneck. Source code analysis pointed to repeated block reports from DataNodes to the NameNode as the likely cause of the high load.
This document discusses benchmarking Hadoop and big data systems. It provides an overview of common Hadoop benchmarks including microbenchmarks like TestDFSIO, TeraSort, and NNBench which test individual Hadoop components. It also describes BigBench, a benchmark modeled after TPC-DS that aims to test a more complete big data analytics workload using techniques like MapReduce, Hive, and Mahout across structured, semi-structured, and unstructured data. The document emphasizes using Hadoop distributions for administration and both microbenchmarks and full benchmarks like BigBench for evaluation.
The document discusses the Virtual Data Connector project which aims to leverage Apache Atlas and Apache Ranger to provide unified metadata and access governance across data sources. Key points include:
- The project aims to address challenges of understanding, governing, and controlling access to distributed data through a centralized metadata catalog and policies.
- Apache Atlas provides a scalable metadata repository while Apache Ranger enables centralized access governance. The project will integrate these using a virtualization layer.
- Enhancements to Atlas and Ranger are proposed to better support the project's goals around a unified open metadata platform and metadata-driven governance.
- An initial minimum viable product will be built this year with the goal of an open, collaborative ecosystem around shared
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
The document discusses big data and distributed computing. It provides examples of the large amounts of data generated daily by organizations like the New York Stock Exchange and Facebook. It explains how distributed computing frameworks like Hadoop use multiple computers connected via a network to process large datasets in parallel. Hadoop's MapReduce programming model and HDFS distributed file system allow users to write distributed applications that process petabytes of data across commodity hardware clusters.
The document provides an introduction to Hadoop and HDFS (Hadoop Distributed File System). It discusses key concepts such as:
- HDFS stores large datasets across commodity hardware in a fault-tolerant manner and provides scalable storage and access.
- HDFS has a master/slave architecture with a NameNode that manages metadata and DataNodes that store data blocks.
- Data is replicated across DataNodes for reliability, with one replica on a local rack and two on remote racks by default.
- Hadoop allows processing of large datasets in parallel across clusters and is well-suited for massive amounts of structured and unstructured data.
The document provides an introduction to the key concepts of Big Data including Hadoop, HDFS, and MapReduce. It defines big data as large volumes of data that are difficult to process using traditional methods. Hadoop is introduced as an open-source framework for distributed storage and processing of large datasets across clusters of computers. HDFS is described as Hadoop's distributed file system that stores data across clusters and replicates files for reliability. MapReduce is a programming model where data is processed in parallel across clusters using mapping and reducing functions.
The document provides an overview of big data and Hadoop, discussing what big data is, current trends and challenges, approaches to solving big data problems including distributed computing, NoSQL, and Hadoop, and introduces HDFS and the MapReduce framework in Hadoop for distributed storage and processing of large datasets.
This document provides an overview of Big Data and Hadoop. It defines Big Data as large volumes of structured, semi-structured, and unstructured data that is too large to process using traditional databases and software. It provides examples of the large amounts of data generated daily by organizations. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop including HDFS for distributed storage and fault tolerance, and MapReduce for distributed processing, are described at a high level. Common use cases for Hadoop by large companies are also mentioned.
This document provides an overview of big data and Hadoop. It defines big data using the 3Vs - volume, variety, and velocity. It describes Hadoop as an open-source software framework for distributed storage and processing of large datasets. The key components of Hadoop are HDFS for storage and MapReduce for processing. HDFS stores data across clusters of commodity hardware and provides redundancy. MapReduce allows parallel processing of large datasets. Careers in big data involve working with Hadoop and related technologies to extract insights from large and diverse datasets.
This document provides an overview of big data, including:
- Defining big data as large datasets that can reveal patterns when analyzed computationally.
- Describing the 3 Vs of big data - volume, velocity, and variety. It discusses how big data comes from many sources and is characterized by its large size and fast generation.
- Introducing Hadoop as an open-source software framework for distributed storage and processing of big data across clusters of commodity servers. Key Hadoop components HDFS and MapReduce are outlined.
Big data refers to large volumes of data that are diverse in type and are produced rapidly. It is characterized by the V's: volume, velocity, variety, veracity, and value. Hadoop is an open-source software framework for distributed storage and processing of big data across clusters of commodity servers. It has two main components: HDFS for storage and MapReduce for processing. Hadoop allows for the distributed processing of large data sets across clusters in a reliable, fault-tolerant manner. The Hadoop ecosystem includes additional tools like HBase, Hive, Pig and Zookeeper that help access and manage data. Understanding Hadoop is a valuable skill as many companies now rely on big data and Hadoop technologies.
Big data is characterized by 3 V's - volume, velocity, and variety. It refers to large and complex datasets that are difficult to process using traditional database management tools. Key technologies to handle big data include distributed file systems, Apache Hadoop, data-intensive computing, and tools like MapReduce. Common tools used are infrastructure management tools like Chef and Puppet, monitoring tools like Nagios and Ganglia, and analytics platforms like Netezza and Greenplum.
This document provides an introduction to Hadoop and big data concepts. It discusses what big data is, the four V's of big data (volume, velocity, variety, and veracity), different data types (structured, semi-structured, unstructured), how data is generated, and the Apache Hadoop framework. It also covers core Hadoop components like HDFS, YARN, and MapReduce, common Hadoop users, the difference between Hadoop and RDBMS systems, Hadoop cluster modes, the Hadoop ecosystem, HDFS daemons and architecture, and basic Hadoop commands.
Hadoop - Architectural road map for Hadoop Ecosystemnallagangus
This document provides an overview of an architectural roadmap for implementing a Hadoop ecosystem. It begins with definitions of big data and Hadoop's history. It then describes the core components of Hadoop, including HDFS, MapReduce, YARN, and ecosystem tools for abstraction, data ingestion, real-time access, workflow, and analytics. Finally, it discusses security enhancements that have been added to Hadoop as it has become more mainstream.
Vikram Andem Big Data Strategy @ IATA Technology Roadmap IT Strategy Group
Vikram Andem, Senior Manager, United Airlines, A case for Bigdata Program and Strategy @ IATA Technology Roadmap 2014, October 13th, 2014, Montréal, Canada
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
This document provides an overview of big data and Apache Hadoop. It defines big data as large and complex datasets that are difficult to process using traditional database management tools. It discusses the sources and growth of big data, as well as the challenges of capturing, storing, searching, sharing, transferring, analyzing and visualizing big data. It describes the characteristics and categories of structured, unstructured and semi-structured big data. The document also provides examples of big data sources and uses Hadoop as a solution to the challenges of distributed systems. It gives a high-level overview of Hadoop's core components and characteristics that make it suitable for scalable, reliable and flexible distributed processing of big data.
Introduction to Big Data Hadoop Training Online by www.itjobzone.bizITJobZone.biz
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Hadoop is a framework for distributed storage and processing of large datasets across clusters of commodity hardware. It includes HDFS, a distributed file system, and MapReduce, a programming model for large-scale data processing. HDFS stores data reliably across clusters and allows computations to be processed in parallel near the data. The key components are the NameNode, DataNodes, JobTracker and TaskTrackers. HDFS provides high throughput access to application data and is suitable for applications handling large datasets.
The document provides an introduction to big data and Hadoop. It defines big data as large datasets that cannot be processed using traditional computing techniques due to the volume, variety, velocity, and other characteristics of the data. It discusses traditional data processing versus big data and introduces Hadoop as an open-source framework for storing, processing, and analyzing large datasets in a distributed environment. The document outlines the key components of Hadoop including HDFS, MapReduce, YARN, and Hadoop distributions from vendors like Cloudera and Hortonworks.
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
This document discusses big data and Hadoop. It defines big data as large amounts of unstructured data that would be too costly to store and analyze in a traditional database. It then describes how Hadoop provides a solution to this challenge through distributed and parallel processing across clusters of commodity hardware. Key aspects of Hadoop covered include HDFS for reliable storage, MapReduce for distributed computing, and how together they allow scalable analysis of very large datasets. Popular users of Hadoop like Amazon, Yahoo and Facebook are also mentioned.
Hadoop is a framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses HDFS for fault-tolerant storage and MapReduce as a programming model for distributed computing. HDFS stores data across clusters of machines and replicates it for reliability. MapReduce allows processing of large datasets in parallel by splitting work into independent tasks. Hadoop provides reliable and scalable storage and analysis of very large amounts of data.
07 logistic regression and stochastic gradient descentSubhas Kumar Ghosh
This document provides an overview of logistic regression using stochastic gradient descent. It explains that logistic regression can be used for classification problems where the output is discrete. The key aspects covered include:
- Logistic regression estimates the logit (log odds) of the probability rather than the probability directly, using a linear function of the input features.
- It learns a hyperplane that separates the classes by choosing weights to maximize the likelihood of the training data.
- Stochastic gradient descent can be used as an optimization technique to learn the weights by minimizing the negative log likelihood.
- An example is provided of using the Mahout machine learning library to build a logistic regression model for classification using features from a donut-
06 how to write a map reduce version of k-means clusteringSubhas Kumar Ghosh
The document discusses how to write a MapReduce version of K-means clustering. It involves duplicating the cluster centers across nodes so each data point can be processed independently in the map phase. The map phase outputs (ClusterID, Point) pairs assigning each point to its closest cluster. The reduce phase groups by ClusterID and calculates the new centroid for each cluster, outputting (ClusterID, Centroid) pairs. Each iteration is run as a MapReduce job with the library determining if convergence is reached between iterations.
K-Means clustering is an algorithm that partitions data points into k clusters based on their distances from initial cluster center points. It is commonly used for classification applications on large datasets and can be parallelized by duplicating cluster centers and processing each data point independently. Mahout provides implementations of K-Means clustering and other algorithms that can operate on distributed datasets stored in Hadoop SequenceFiles.
This document provides examples and explanations of key concepts in Hive Query Language (HQL) including how to create and populate tables, load data into Hive, write queries, and descriptions of managed vs external tables, partitions, and buckets. It also summarizes Hive architecture, clients, metastore configurations, and HiveQL capabilities compared to SQL standards.
Hive provides an SQL-like interface to query and analyze large datasets stored in Hadoop. It allows users to model data as tables and analyze the data using SQL queries without needing to learn MapReduce programming. Hive generates MapReduce jobs behind the scenes to parallelize the processing and generate results. The system works by storing metadata about the tables in a metastore and then using this metadata to generate MapReduce jobs for queries. This allows Hive to provide a more programmer-friendly interface compared to raw MapReduce for working with large datasets.
HBase is a distributed, column-oriented database built on top of HDFS that can handle large datasets across a cluster. It uses a map-reduce model where data is stored as multidimensional sorted maps across nodes. Data is first written to a write-ahead log and memory, then flushed to disk files and compacted for efficiency. Client applications access HBase programmatically through APIs rather than SQL. Map-reduce jobs on HBase use input, mapper, reducer, and output classes to process table data in parallel across regions.
Pig is a data flow language that sits on top of Hadoop and allows users to quickly process large volumes of data across many servers simultaneously. It supports relational features like joins, groups, and aggregates, making it well-suited for extract, transform, load (ETL) tasks. Common ETL use cases for Pig include time-sensitive data loads from various sources into databases, and processing multiple data sources to gain insights into customer behavior. While Pig can handle ETL tasks, it is also capable of sampling large datasets for analysis and providing analytical insights beyond basic ETL functions.
This document discusses user defined functions (UDFs) in Apache Pig. It provides examples of different types of UDFs including EvalFunc, FilterFunc, and LoadFunc. For EvalFunc, it shows how to write a simple function to uppercase text and how to return complex types. For FilterFunc, it demonstrates an IsEmpty function. For LoadFunc, it outlines the key interfaces and methods needed to implement a custom loader using a regular expression example.
The document describes an example of using Pig Latin to analyze weather data. It loads a data file with year, temperature, and quality fields for different years. It then filters the data, groups it by year, and uses a MAX function to calculate the maximum recorded temperature for each year. This provides a concise high-level summary of the key steps and goals described in the document.
Apache Pig is a platform for analyzing large datasets that consists of a high-level data flow language called Pig Latin and an infrastructure for evaluating Pig Latin programs. Pig Latin scripts are compiled into sequences of MapReduce jobs that can run on Hadoop for large scale parallel processing. Pig aims to provide a simpler programming model than raw MapReduce while still allowing for optimization and parallelization of queries. Pig programs can be run interactively using the Grunt shell or by specifying a Pig Latin script to execute.
Naive Bayes classifiers are a simple yet effective method for sentiment analysis and text classification problems. They work by calculating the probability of a document belonging to a certain class based on the presence of individual words or features, assuming conditional independence between features given the class. This allows probabilities to be estimated efficiently from training data. While the independence assumption is often unrealistic, naive Bayes classifiers generally perform well compared to more sophisticated approaches. The document discusses various techniques for preprocessing text like tokenization, stemming, part-of-speech tagging, and negation handling to improve the accuracy of naive Bayes classifiers for sentiment analysis tasks.
This document provides performance optimization tips for Hadoop jobs, including recommendations around compression, speculative execution, number of maps/reducers, block size, sort size, JVM tuning, and more. It suggests how to configure properties like mapred.compress.map.output, mapred.map/reduce.tasks.speculative.execution, and dfs.block.size based on factors like cluster size, job characteristics, and data size. It also identifies antipatterns to avoid like processing thousands of small files or using many maps with very short runtimes.
This document outlines a training plan that will introduce performance optimization tips, naive bayes classifiers for sentiment analysis, Pig for data operations and user defined functions, Pig ETL features, and exercises involving using MapReduce with Python for sentiment analysis of movie reviews using naive bayes classification and analyzing Apache server logs with Pig. The training will conclude with an introduction for the third day of material.
The document discusses using MapReduce to efficiently calculate pairwise document similarity in large document collections. It describes building an inverted index that maps each term to the documents that contain it and associated term weights. This index is generated by mappers emitting term keys and (document, weight) value tuples, which reducers write to disk. Pairwise similarity is then calculated by mappers generating key tuples for all document pairs from a term's postings, with values of the product of weights, representing individual term contributions. Reducers sum these contributions to generate the final similarity score for each pair.
YARN is the next generation of MapReduce that splits the JobTracker into separate daemons for resource management and job scheduling. The ResourceManager is responsible for arbitrating resources among applications and each application has its own ApplicationMaster for negotiating resources and monitoring tasks. The NodeManager on each node monitors resource usage and reports to the ResourceManager. YARN allows various distributed applications beyond MapReduce by having application-specific ApplicationMasters manage tasks.
This document discusses different techniques for chaining together multiple MapReduce jobs to solve more complex problems in Hadoop - JobClient, JobControl, and ChainMapper. JobClient allows running jobs sequentially by configuring the output of one as the input to the next. JobControl provides dependencies between jobs and manages their execution. ChainMapper chains multiple mappers within a single Map task, reducing disk I/O by passing data between mappers without writing to disk.
- Time series data consists of data points measured at successive time intervals and is commonly found in domains like finance, science, and increasingly across other industries as sensors become more prevalent.
- While traditional RDBMS approaches have limitations for analyzing high-resolution time series data due to scaling and performance issues, MapReduce provides an alternative approach for distributed processing and analysis of large time series datasets.
- To calculate a simple moving average on time series data in MapReduce, records can be sorted during the shuffle phase using a composite key of the stock symbol and timestamp, allowing data to arrive at reducers already sorted and avoiding expensive sorting operations.
The document discusses combiners and partitioners in MapReduce frameworks. It explains that combiners allow for local aggregation of map output key-value pairs before shuffling to reducers. This can significantly reduce the amount of data transferred between maps and reduces. For a combiner to be effective, the reduce operation must be commutative and associative so the local aggregations can be merged. The document provides examples of operations like sum() and max() that qualify for use as combiners. It also discusses factors like serialization overhead that should be considered when deciding whether a combiner will provide benefits for a given job.
The MapReduce job begins when a client program uploads configuration files to HDFS and notifies the JobTracker. The JobTracker assigns map tasks to idle TaskTrackers and the tasks extract input data, invoke the user-provided map function, and output intermediate key-value pairs. When the map tasks complete, reduce tasks are assigned to TaskTrackers to download intermediate data and invoke the reduce function to generate the final output. The framework is resilient to failures and can re-execute failed tasks as needed.
The document provides information about MapReduce jobs including:
- The number of maps is determined by input size and partitioning. The number of reducers is set by the user.
- Reducers receive sorted, grouped data from maps via shuffle and sort. They apply the reduce function to grouped keys/values.
- The optimal number of reducers depends on nodes and tasks. More reducers improve load balancing but increase overhead.
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Introduction to Data Science
1.1 What is Data Science, importance of data science,
1.2 Big data and data Science, the current Scenario,
1.3 Industry Perspective Types of Data: Structured vs. Unstructured Data,
1.4 Quantitative vs. Categorical Data,
1.5 Big Data vs. Little Data, Data science process
1.6 Role of Data Scientist
Getting Started with Interactive Brokers API and Python.pdfRiya Sen
In the fast-paced world of finance, automation is key to staying ahead of the curve. Traders and investors are increasingly turning to programming languages like Python to streamline their strategies and enhance their decision-making processes. In this blog post, we will delve into the integration of Python with Interactive Brokers, one of the leading brokerage platforms, and explore how this dynamic duo can revolutionize your trading experience.
Combined supervised and unsupervised neural networks for pulse shape discrimi...Samuel Jackson
Our methodology for pulse shape discrimination is split into two steps. Firstly, we learn a model to discriminate between pulses using "clean" low-rate examples by removing pile-up & saturated events. In addition to traditional tail sum discrimination, we investigate three different choices for discrimination between γ-pulses, fast, thermal neutrons. We consider clustering the pulses directly using Gaussian Mixture Modelling (GMM), using variational autoencoders to learn a representation of the pulses and then clustering the learned representation (VAE+GMM) and using density ratio estimation to discriminate between a mixed (γ + neutron) and pure (γ only) sources using a multi-layer perceptron (MLP) as a supervised learning problem.
Secondly, we aim to classify and recover pile-up events in the < 150 ns regime by training a single unified multi-label MLP. To frame the problem as a multi-label supervised learning method, we first simulate pile-up events with known components. Then, using the simulated data and combining it with single event data, we train a final multi-label MLP to output a binary code indicating both how many and which type of events are present within an event window.
Big Data and Analytics Shaping the future of PaymentsRuchiRathor2
The payments industry is experiencing a data-driven revolution powered by big data and analytics.
Here's a glimpse into 5 ways this dynamic duo is transforming how we pay.
In essence, big data and analytics are playing a pivotal role in building a future filled with faster, more secure, and convenient payment methods for everyone.
Overview of Statistical software such as ODK, surveyCTO,and CSPro
2. Software installation(for computer, and tablet or mobile devices)
3. Create a data entry application
4. Create the data dictionary
5. Create the data entry forms
6. Enter data
7. Add Edits to the Data Entry Application
8. CAPI questions and texts
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataSamuel Jackson
We present our work to improve data accessibility and performance for data-intensive tasks within the fusion research community. Our primary goal is to develop services that facilitate efficient access for data-intensive applications while ensuring compliance with FAIR principles [1], as well as adoption of interoperable tools, methods and standards.
The major outcome of our work is the successful creation and deployment of a data service for the MAST (Mega Ampere Spherical Tokamak) experiment [2], leading to substantial enhancements in data discoverability, accessibility, and overall data retrieval performance, particularly in scenarios involving large-scale data access. Our work follows the principles of Analysis-Ready, Cloud Optimised (ARCO) data [3] by using cloud optimised data formats for fusion data.
Our system consists of a query-able metadata catalogue, complemented with an object storage system for publicly serving data from the MAST experiment. We will show how our solution integrates with the Pandata stack [4] to enable data analysis and processing at scales that would have previously been intractable, paving the way for data-intensive workflows running routinely with minimal pre-processing on the part of the researcher. By using a cloud-optimised file format such as zarr [5] we can enable interactive data analysis and visualisation while avoiding large data transfers. Our solution integrates with common python data analysis libraries for large, complex scientific data such as xarray [6] for complex data structures and dask [7] for parallel computation and lazily working with larger that memory datasets.
The incorporation of these technologies is vital for advancing simulation, design, and enabling emerging technologies like machine learning and foundation models, all of which rely on efficient access to extensive repositories of high-quality data. Relying on the FAIR guiding principles for data stewardship not only enhances data findability, accessibility, and reusability, but also fosters international cooperation on the interoperability of data and tools, driving fusion research into new realms and ensuring its relevance in an era characterised by advanced technologies in data science.
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) https://doi.org/10.1038/sdata.2016.18
[2] M Cox, The Mega Amp Spherical Tokamak, Fusion Engineering and Design, Volume 46, Issues 2–4, 1999, Pages 397-404, ISSN 0920-3796, https://doi.org/10.1016/S0920-3796(99)00031-9
[3] Stern, Charles, et al. "Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production." Frontiers in Climate 3 (2022): 782909.
[4] Bednar, James A., and Martin Durant. "The Pandata Scalable Open-Source Analysis Stack." (2023).
[5] Alistair Miles (2024) ‘zarr-developers/zarr-python: v2.17.1’. Zenodo. doi: 10.5281/zenodo.10790679
[6] Hoyer, S. & Hamman, J., (20
How AI is Revolutionizing Data Collection.pdfPromptCloud
Artificial Intelligence (AI) is transforming the landscape of data collection, making it more efficient, accurate, and insightful than ever before. With AI, businesses can automate the extraction of vast amounts of data from diverse sources, analyze patterns in real-time, and gain deeper insights with minimal human intervention. This revolution in data collection enables companies to make faster, data-driven decisions, enhance their competitive edge, and unlock new opportunities for growth.
AI-powered tools can handle complex and dynamic web content, adapt to changes in website structures, and even understand the context of data through natural language processing. This means that data collection is not only faster but also more precise, reducing the time and effort required for manual data extraction. Furthermore, AI can process unstructured data, such as social media posts and customer reviews, providing valuable insights into customer sentiment and market trends.
Embrace the future of data collection with AI and stay ahead of the curve. Learn more about how PromptCloud’s AI-driven web scraping solutions can transform your data strategy. https://www.promptcloud.com/contact/
2. Big-data
Four parameters:
–Velocity: Streaming data and large volume data movement.
–Volume: Scale from terabytes to zettabytes.
–Variety: Manage the complexity of multiple relational and non-relational data types and schemas.
–Voracity: Produced data has to be consumed fast before it becomes meaningless.
3. Not just internet companies
Big Data Shouldn’t Be a SiloMust be an integrated part of enterprise information architecture
4. Data >> Information >> Business Value
Retail–By combining data feeds on inventory, transactional records, social media and online trends, retailers can make real-time decisions around product and inventory mix adjustments, marketing and promotions, pricing or product quality issues.
Financial Services–By combining data across various groups and services like financial markets, money manager and lending, financial services companies can gain a comprehensive view of their individual customers and markets.
Government–By collecting and analyzing data across agencies, location and employee groups, the government can reduce redundancies, identify productivity gaps, pinpoint consumer issues and find procurement savings across agencies.
Healthcare–Big data in healthcare could be used help improve hospital operations, provide better tracking of procedural outcomes, help accelerate research and grow knowledge bases more rapidly. According to a 2011 Cleveland Clinic study, leveraging big data is estimated to drive $300B in annual value through an 8% systematic cost savings.
5. Single-core, single processor
Single-core, multi-processor
Single- core
Multi-core, single processor
Multi-core, multi-processor
Multi-core
Cluster of processors (single or multi-core) with shared memory
Cluster of processors with distributed memory
Cluster
Broadly, the approach in HPC is to distribute the work across a cluster of machines, which access a shared file-system, hosted by a SAN.
Grid of clusters
Embarrassingly parallel processing
MapReduce, distributed file system
Cloud computing
Pipelined Instruction level
Concurrent Thread level
Service Object level
Indexed File level
Mega Block level
Virtual System Level
Data size: small
Data size: large
Reference: Bina Ramamurthy 2011
Processing Granularity
6. How to Process BigData?
Need to process large datasets (>100TB)
–Just reading 100TB of data can be overwhelming
–Takes ~11 days to read on a standard computer
–Takes a day across a 10Gbit link (very high end storage solution)
–On a single node (@50MB/s) –23days
–On a 1000 node cluster ��33min
7. Examples
•Web logs;
•RFID;
•sensor networks;
•social networks;
•social data (due to thesocial data revolution),
•Internet text and documents;
•Internet search indexing;
•call detail records;
•astronomy,
•atmospheric science,
•genomics,
•biogeochemical,
•biological, and
•other complex and/or interdisciplinary scientific research;
•military surveillance;
•medical records;
•photography archives;
•video archives; and
•large-scale e-commerce.
8. Not so easy…
Moving data from storage cluster to computation cluster is not feasible
In large clusters
–Failure is expected, rather than exceptional.
–In large clusters, computers fail every day
–Data is corrupted or lost
–Computations are disrupted
–The number of nodes in a cluster may not be constant.
–Nodes can be heterogeneous.
Very expensive to build reliability into each application
–A programmer worries about errors, data motion, communication…
–Traditional debugging and performance tools don’t apply
Need a common infrastructure and standard set of tools to handle this complexity
–Efficient, scalable, fault-tolerant and easy to use
9. Why is Hadoop and MapReduceneeded?
The answer to this questions comes from another trend in disk drives:
–seek time is improving more slowly than transfer rate.
Seeking is the process of moving the disk’s head to a particular place on the disk to read or write data.
It characterizes the latency of a disk operation, whereas the transfer rate corresponds to a disk’s bandwidth.
If the data access pattern is dominated by seeks, it will take longer to read or write large portions of the dataset than streaming through it, which operates at the transfer rate.
10. Why is Hadoop and MapReduceneeded?
On the other hand, for updating a small proportion of records in a database, a traditional B-Tree (the data structure used in relational databases, which is limited by the rate it can perform seeks) works well.
For updating the majority of a database, a B-Tree is less efficient than MapReduce, which uses Sort/Merge to rebuild the database.
MapReducecan be seen as a complement to an RDBMS.
MapReduceis a good fit for problems that need to analyze the whole dataset, in a batch fashion, particularly for ad hoc analysis.
12. Hadoop distributions
Apache™ Hadoop™
Apache Hadoop-based Services for Windows Azure
Cloudera’sDistribution Including Apache Hadoop (CDH)
HortonworksData Platform
IBM InfoSphereBigInsights
Platform Symphony MapReduce
MapRHadoop Distribution
EMC GreenplumMR (using MapR’sM5 Distribution)
ZettasetData Platform
SGI Hadoop Clusters (uses Clouderadistribution)
Grand Logic JobServer
OceanSyncHadoop Management Software
Oracle Big Data Appliance (uses Clouderadistribution)
13. What’s up with the names?
When naming software projects, Doug Cutting seems to have been inspired by his family.
Luceneis his wife’s middle name, and her maternal grandmother’s first name.
His son, as a toddler, used Nutchas the all- purpose word for meal and later named a yellow stuffed elephant Hadoop.
Doug said he “was looking for a name that wasn’t already a web domain and wasn’t trademarked, so I tried various words that were in my life but not used by anybody else. Kids are pretty good at making up words.”
14. Hadoop features
Distributed Framework for processing and storing data generally on commodity hardware.
Completely Open Source.
Written in Java
–Runs on Linux, Mac OS/X, Windows, and Solaris.
–Client apps can be written in various languages.
•Scalable: store and process petabytes, scale by adding Hardware
•Economical: 1000’s of commodity machines
•Efficient: run tasks where data is located
•Reliable: data is replicated, failed tasks are rerun
•Primarily used for batch data processing, not real-time / user facing applications
15. Components of Hadoop
•HDFS(Hadoop Distributed File System)
–ModeledonGFS
–Reliable,HighBandwidthfilesystemthatcan
store TB' and PB's data.
•Map-Reduce
–UsingMap/ReducemetaphorfromLisplanguage
–Adistributedprocessingframeworkparadigmthat
process the data stored onto HDFS in key-value .
DFS
Processing Framework
Client 1
Client 2
Input
data
Output
data
Map
Map
Map
Reduce
Reduce
Input
Map
Shuffle & Sort
Reduce
Output
16. •Very Large Distributed File System
–10K nodes, 100 million files, 10 PB
–Linearly scalable
–Supports Large files (in GBs or TBs)
•Economical
–Uses Commodity Hardware
–Nodes fail every day. Failure is expected, rather than exceptional.
–The number of nodes in a cluster is not constant.
•Optimized for Batch Processing
HDFS
17. HDFS Goals
•Highly fault-tolerant
–runs on commodity HW, which can fail frequently
•High throughput of data access
–Streaming access to data
•Large files
–Typical file is gigabytes to terabytes in size
–Support for tens of millions of files
•Simple coherency
–Write-once-read-many access model
18. HDFS: Files and Blocks
•Data Organization
–Data is organized into files and directories
–Files are divided into uniform sized large blocks
–Typically 128MB
–Blocks are distributed across cluster nodes
•Fault Tolerance
–Blocks are replicated (default 3) to handle hardware failure
–Replication based on Rack-Awareness for performance and fault tolerance
–Keeps checksums of data for corruption detection and recovery
–Client reads both checksum and data from DataNode. If checksum fails, it tries other replicas
19. HDFS: Files and Blocks
•High Throughput:
–Client talks to both NameNodeand DataNodes
–Data is not sent through the NameNode.
–Throughput of file system scales nearly linearly with the number of nodes.
•HDFS exposes block placement so that computation can be migrated to data
20. HDFS Components
•NameNode
–Manages the file namespace operation like opening, creating, renaming etc.
–File name to list blocks + location mapping
–File metadata
–Authorization and authentication
–Collect block reports from DataNodeson block locations
–Replicate missing blocks
–Keeps ALL namespace in memory plus checkpoints & journal
•DataNode
–Handles block storage on multiple volumes and data integrity.
–Clients access the blocks directly from data nodes for read and write
–Data nodes periodically send block reports to NameNode
–Block creation, deletion and replication upon instruction from the NameNode.
23. Map Reduce -Introduction
•Parallel Job processing framework
•Written in java
•Close integration with HDFS
•Provides :
–Auto partitioning of job into sub tasks
–Auto retry on failures
–Linear Scalability
–Locality of task execution
–Plugin based framework for extensibility
24. Map-Reduce
•MapReduceprograms are executed in two main phases, called
–mapping and
–reducing.
•In the mapping phase, MapReducetakes the input data and feeds each data element to the mapper.
•In the reducing phase, the reducer processes all the outputs from the mapper and arrives at a final result.
•The mapper is meant to filter and transform the input into something
•That the reducer can aggregate over.
•MapReduceuses lists and (key/value) pairs as its main data primitives.
25. Map-Reduce
Map-Reduce Program
–Based on two functions: Map and Reduce
–Every Map/Reduce program must specify a Mapper and optionally a Reducer
–Operate on key and value pairs
Map-Reduce works like a Unix pipeline:
cat input | grep| sort | uniq-c | cat > output
Input| Map| Shuffle & Sort | Reduce| Output
cat /var/log/auth.log* | grep“session opened” | cut -d’ ‘ -f10 | sort | uniq-c > ~/userlist
Map function: Takes a key/value pair and generates a set of intermediate key/value pairs map(k1, v1) -> list(k2, v2)
Reduce function: Takes intermediate values and associates them with the same intermediate key reduce(k2, list(v2)) -> list (k3, v3)
27. Hadoop and its elements
HDFS
.
.
.
File 1
File 2
File 3
File N-2
File N-1
File N
Input
files
Splits
Mapper
Machine -1
Machine -2
Machine -M
Split 1
Split 2
Split 3
Split M-2
Split M-1
Split M
Map 1
Map 2
Map 3
Map M-2
Map M-1
Map M
Combiner 1
Combiner C
(Kay, Value)
pairs
Record Reader
combiner
.
.
.
Partition 1
Partition 2
Partition P-1
Partition P
Partitionar
Reducer
HDFS
.
.
.
File 1
File 2
File 3
File O-2
File O-1
File O
Reducer 1
Reducer 2
Reducer R-1
Reducer R
Input
Output
Machine -x
28. Hadoop Eco-system
•Hadoop Common: The common utilities that support the other Hadoop subprojects.
•Hadoop Distributed File System (HDFS™): A distributed file system that provides high- throughput access to application data.
•Hadoop MapReduce: A software framework for distributed processing of large data sets on compute clusters.
•Other Hadoop-related projects at Apache include:
–Avro™: A data serialization system.
–Cassandra™: A scalable multi-master database with no single points of failure.
–Chukwa™: A data collection system for managing large distributed systems.
–HBase™: A scalable, distributed database that supports structured data storage for large tables.
–Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.
–Mahout™: A Scalable machine learning and data mining library.
–Pig™: A high-level data-flow language and execution framework for parallel computation.
–ZooKeeper™: A high-performance coordination service for distributed applications.
29. Exercise –task
You have timeseriesdata (timestamp, ID, value) collected from 10,000 sensors in every millisecond. Your central system stores this data, and allow more than 500 people to concurrently access this data and execute queries on them. While last one month data is accessed more frequently, some analytics algorithm built model using historical data as well.
•Task:
–Provide an architecture of such system to meet following goals
–Fast
–Available
–Fair
–Or, provide analytics algorithm and data-structure design considerations (e.g. k- means clustering, or regression) on this data set of worth 3 months.
•Group / individual presentation