Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
Hadoop is a distributed processing framework for large datasets. It utilizes HDFS for storage and MapReduce as its programming model. The Hadoop ecosystem has expanded to include many other tools. YARN was developed to address limitations in the original Hadoop architecture. It provides a common platform for various data processing engines like MapReduce, Spark, and Storm. YARN improves scalability, utilization, and supports multiple workloads by decoupling cluster resource management from application logic. It allows different applications to leverage shared Hadoop cluster resources.
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Introduction To Hadoop | What Is Hadoop And Big Data | Hadoop Tutorial For Be...Simplilearn
This presentation about Hadoop will help you learn the basics of Hadoop and its components. First, you will see what is Big Data and the significant challenges in it. Then, you will understand how Hadoop solved those challenges. You will have a glance at the History of Hadoop, what is Hadoop, the different companies using Hadoop, the applications of Hadoop in different companies, etc. Finally, you will learn the three essential components of Hadoop – HDFS, MapReduce, and YARN, along with their architecture. Now, let us get started with Introduction to Hadoop.
Below topics are explained in this Hadoop presentation:
1. Big Data and its challenges
2. Hadoop as a solution
3. History of Hadoop
4. What is Hadoop
5. Applications of Hadoop
6. Components of Hadoop
7. Hadoop Distributed File System
8. Hadoop MapReduce
9. Hadoop YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/introduction-to-big-data-and-hadoop-certification-training.
This document provides an introduction to Hadoop, including its motivation and key components. It discusses the scale of cloud computing that Hadoop addresses, and describes the core Hadoop technologies - the Hadoop Distributed File System (HDFS) and MapReduce framework. It also briefly introduces the Hadoop ecosystem, including other related projects like Pig, HBase, Hive and ZooKeeper. Sample code is walked through to illustrate MapReduce programming. Key aspects of HDFS like fault tolerance, scalability and data reliability are summarized.
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.
Hive is a data warehouse infrastructure tool that allows users to query and analyze large datasets stored in Hadoop. It uses a SQL-like language called HiveQL to process structured data stored in HDFS. Hive stores metadata about the schema in a database and processes data into HDFS. It provides a familiar interface for querying large datasets using SQL-like queries and scales easily to large datasets.
This document provides an overview of Apache Hadoop and HBase. It begins with an introduction to why big data is important and how Hadoop addresses storing and processing large amounts of data across commodity servers. The core components of Hadoop, HDFS for storage and MapReduce for distributed processing, are described. An example MapReduce job is outlined. The document then introduces the Hadoop ecosystem, including Apache HBase for random read/write access to data stored in Hadoop. Real-world use cases of Hadoop at companies like Yahoo, Facebook and Twitter are briefly mentioned before addressing questions.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Hadoop Training | Hadoop Training For Beginners | Hadoop Architecture | Hadoo...Simplilearn
The document provides information about Hadoop training. It discusses the need for Hadoop in today's data-heavy world. It then describes what Hadoop is, its ecosystem including HDFS for storage and MapReduce for processing. It also discusses YARN and provides a bank use case. It further explains the architecture and working of HDFS and MapReduce in processing large datasets in parallel across clusters.
The document discusses the Hadoop ecosystem, which includes core Apache Hadoop components like HDFS, MapReduce, YARN, as well as related projects like Pig, Hive, HBase, Mahout, Sqoop, ZooKeeper, Chukwa, and HCatalog. It provides overviews and diagrams explaining the architecture and purpose of each component, positioning them as core functionality that speeds up Hadoop processing and makes Hadoop more usable and accessible.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for data storage and MapReduce as a programming model for distributed computing. HDFS stores data reliably across machines in a Hadoop cluster as blocks and achieves high fault tolerance through replication. MapReduce allows processing of large datasets in parallel by dividing the work into independent tasks called Maps and Reduces. Hadoop has seen widespread adoption for applications involving massive datasets and is used by companies like Yahoo!, Facebook and Amazon.
This document provides an overview and introduction to NoSQL databases. It begins with an agenda that explores key-value, document, column family, and graph databases. For each type, 1-2 specific databases are discussed in more detail, including their origins, features, and use cases. Key databases mentioned include Voldemort, CouchDB, MongoDB, HBase, Cassandra, and Neo4j. The document concludes with references for further reading on NoSQL databases and related topics.
The document discusses a presentation about practical problem solving with Hadoop and Pig. It provides an agenda that covers introductions to Hadoop and Pig, including the Hadoop distributed file system, MapReduce, performance tuning, and examples. It discusses how Hadoop is used at Yahoo, including statistics on usage. It also provides examples of how Hadoop has been used for applications like log processing, search indexing, and machine learning.
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
The document discusses various Hadoop technologies including HDFS, MapReduce, Pig/Hive, HBase, Flume, Oozie, Zookeeper, and HBase. HDFS provides reliable storage across multiple machines by replicating data on different nodes. MapReduce is a framework for processing large datasets in parallel. Pig and Hive provide high-level languages for analyzing data stored in Hadoop. Flume collects log data as it is generated. Oozie manages Hadoop jobs. Zookeeper allows distributed coordination. HBase provides a fault-tolerant way to store large amounts of sparse data.
Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
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.
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.
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.
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.
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.
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 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.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a programming model called MapReduce where developers write mapping and reducing functions that are automatically parallelized and executed on a large cluster. Hadoop also includes HDFS, a distributed file system that stores data across nodes providing high bandwidth. Major companies like Yahoo, Google and IBM use Hadoop to process large amounts of data from users and applications.
HDFS is a distributed file system designed for storing very large data files across commodity servers or clusters. It works on a master-slave architecture with one namenode (master) and multiple datanodes (slaves). The namenode manages the file system metadata and regulates client access, while datanodes store and retrieve block data from their local file systems. Files are divided into large blocks which are replicated across datanodes for fault tolerance. The namenode monitors datanodes and replicates blocks if their replication drops below a threshold.
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.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
This document introduces Pig, an open source platform for analyzing large datasets that sits on top of Hadoop. It provides an example of using Pig Latin to find the top 5 most visited websites by users aged 18-25 from user and website data. Key points covered include who uses Pig, how it works, performance advantages over MapReduce, and upcoming new features. The document encourages learning more about Pig through online documentation and tutorials.
Hadoop is one of the booming and innovative data analytics technology which can effectively handle Big Data problems and achieve the data security. It is an open source and trending technology which involves in data collection, data processing and data analytics using HDFS (Hadoop Distributed File System) and MapReduce algorithms.
The document provides an overview of Apache Hadoop and how it addresses challenges related to big data. It discusses how Hadoop uses HDFS to distribute and store large datasets across clusters of commodity servers and uses MapReduce as a programming model to process and analyze the data in parallel. The core components of Hadoop - HDFS for storage and MapReduce for processing - allow it to efficiently handle large volumes and varieties of data across distributed systems in a fault-tolerant manner. Major companies have adopted Hadoop to derive insights from their big data.
The document provides an introduction to the Hadoop ecosystem. It discusses the history of Hadoop, originating from Google's paper on MapReduce and Google File System. It describes some of the core components of Hadoop including HDFS for storage, MapReduce for distributed processing, and additional components like Hive, Pig, and HBase. It also discusses different Hadoop distributions from companies like Cloudera, Hortonworks, MapR, and others that package and support Hadoop deployments.
The document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop addresses the growing volume, variety and velocity of big data through its core components: HDFS for storage, and MapReduce for distributed processing. Key features of Hadoop include scalability, flexibility, reliability and economic viability for large-scale data analytics.
M. Florence Dayana - Hadoop Foundation for Analytics.pptxDr.Florence Dayana
Hadoop Foundation for Analytics
History of Hadoop
Features of Hadoop
Key Advantages of Hadoop
Why Hadoop
Versions of Hadoop
Eco Projects
Essential of Hadoop ecosystem
RDBMS versus Hadoop
Key Aspects of Hadoop
Components of Hadoop
This document discusses building big data solutions using Microsoft's HDInsight platform. It provides an overview of big data and Hadoop concepts like MapReduce, HDFS, Hive and Pig. It also describes HDInsight and how it can be used to run Hadoop clusters on Azure. The document concludes by discussing some challenges with Hadoop and the broader ecosystem of technologies for big data beyond just Hadoop.
This document provides information about Hadoop and its components. It discusses the history of Hadoop and how it has evolved over time. It describes key Hadoop components including HDFS, MapReduce, YARN, and HBase. HDFS is the distributed file system of Hadoop that stores and manages large datasets across clusters. MapReduce is a programming model used for processing large datasets in parallel. YARN is the cluster resource manager that allocates resources to applications. HBase is the Hadoop database that provides real-time random data access.
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
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The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins at Google and Yahoo
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Facebook and Amazon use Hadoop to process massive amounts of data
The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins from Google and Apache projects
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Yahoo, Facebook, and Amazon use Hadoop for applications like log processing, searches, and advertisement targeting
Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q2tcloudcomputing-tw
The presentation is designed for those interested in Hadoop technology, and can enhance your knowledge in Hadoop, such as community history, current development status, features of services, distributed computing framework and scenario of big data development in Enterprise.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and additional tools like Hive, Pig, HBase, Zookeeper, Flume, Sqoop and Oozie that make up its ecosystem. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a master-slave architecture with the NameNode as master and DataNodes as slaves. The NameNode manages file system metadata and the DataNodes store data blocks. Hadoop also includes a MapReduce engine where the JobTracker splits jobs into tasks that are processed by TaskTrackers on each node. Hadoop saw early adoption from companies handling big data like Yahoo!, Facebook and Amazon and is now widely used for applications like advertisement targeting, search, and security analytics.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It has four main modules - Hadoop Common, HDFS, YARN and MapReduce. HDFS provides a distributed file system that stores data reliably across commodity hardware. MapReduce is a programming model used to process large amounts of data in parallel. Hadoop architecture uses a master-slave model, with a NameNode master and DataNode slaves. It provides fault tolerance, high throughput access to application data and scales to thousands of machines.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop includes a storage part called HDFS for reliable data storage, and a processing part called MapReduce that processes data in parallel on a large cluster. Hadoop also includes additional projects like Hive, Pig, HBase, Zookeeper, Oozie, and Sqoop that together form a powerful data processing ecosystem.
Customer Feedback Analytics for Starbucks Nishant Gandhi
Northeastern University class 7250 Big Data Architecture and Governance Assignment work.
Big Data Project proposal by taking the case study of Starbucks
Guest Lecture: Introduction to Big Data at Indian Institute of TechnologyNishant Gandhi
This document provides an introduction to big data, including definitions of big data and why it is important. It discusses characteristics of big data like volume, velocity, variety and veracity. It provides examples of big data applications in various industries like GE, Boeing, social media, finance, CERN, journalism, politics and more. It also introduces NoSQL and the CAP theorem, and concludes that big data is changing business and technology by enabling new insights from data to reduce costs and optimize operations.
This document discusses processing large graphs. It introduces graph processing with MapReduce and Apache Giraph. MapReduce algorithms for finding triangles and connected components in graphs are described. The limitations of MapReduce for graph processing are discussed. Alternative graph processing technologies including Neo4j, a graph database, are presented. A movie recommendation use case is demonstrated using Neo4j to find similar users and recommend unseen movies.
Graph Coloring Algorithms on Pregel Model using HadoopNishant Gandhi
This document describes research conducted on implementing graph coloring algorithms on the Pregel model using Hadoop. Five graph coloring algorithms were studied: Local Maxima First (LMF), Local Minima-Maxima First (LMMF), Local Largest Degree First (LLDF), Local Smallest-Largest Degree First (LSLDF), and Local Incident Degree First (LIDF). The algorithms were implemented using Apache Giraph and evaluated on several large real-world graphs using an 8-node Hadoop cluster. Experimental results found that LLDF and LSLDF generally used fewer colors and had shorter running times than the other algorithms. Future work could involve algorithms with performance guarantees and custom graph partitioning.
This document surveys and compares three large-scale graph processing platforms: Apache Giraph, Hadoop-MapReduce, and Neo4j. It analyzes their programming models and performance based on previous studies. Hadoop was found to have the worst performance for graph algorithms due to its lack of optimizations for graphs. Giraph was generally the fastest platform due to its in-memory computations and message passing model. Neo4j performed well for small graphs due to its caching but did not scale as well as distributed platforms for large graphs. The document concludes that distributed graph-specific platforms like Giraph outperform generic platforms for most graph problems.
Map reduce programming model to solve graph problemsNishant Gandhi
This document discusses using the MapReduce programming model to solve graph problems. It begins with an introduction to MapReduce, describing its history and programming model. It then provides examples of using MapReduce to solve specific graph algorithms, including breath first search, augmenting edges with degree counts, and enumerating triangles. The examples show how graph problems that don't initially seem to fit the MapReduce model can be solved through multiple MapReduce passes that iteratively process more of the graph.
1) The document provides instructions for setting up networks using Packet Tracer, including how to connect devices, configure IP addresses, and set up routing.
2) It gives step-by-step explanations for creating different network scenarios with one, two, and three networks connected by switches and routers.
3) For networks with multiple routers, it emphasizes that a routing protocol like RIP must be configured so the routers know how to direct traffic between networks.
The document provides an abstract for a paper on the Hadoop framework. It discusses how Hadoop is a software framework that supports data-intensive distributed applications under an open source license. It was inspired by Google's MapReduce and Google File System papers. The paper will represent the history, development, and current situation of Hadoop technology. It is now maintained by the Apache Software Foundation via Cloudera. The paper will include chapters on an introduction to Hadoop, its history, key technologies like MapReduce and HDFS, other related Apache projects, and instructions for setting up a single node Hadoop cluster.
3. Introduction:
What is ?
Hadoop is a framework for running applications on large clusters
built of commodity hardware.
----HADOOP WIKI
Hadoop is a free, Java-based programming framework that
supports the processing of large data sets in a distributed
computing environment.
4. Introduction (conti..)
#1 Open Source
#2 Part of Apache group
#3 Power of JAVA
#4 Supported By Big Web Giant Companies
#1 Google’s Powerful Computation MapReduce Technology
#2 Hadoop Distributed File System(HDFS) inspired by Google File
System(GFS)
#3 Used for Cluster & Distributed Computing
#4 Support from…
5. History:
Inventor Doug Cutting, creator of Apache Lucene
The Origin of the Name “Hadoop”:
The name my kid gave a stuffed yellow elephant. Short, relatively easy to
spell and pronounce, meaningless, and not used elsewhere: those are my
naming criteria. ---Daug Cutting.
Started with building Web Search Engine
•Nutch in 2002
•Aim was to index billions of pages
•Architecture can’t support billions of pages
Google’s GFS in 2003 solved storage problem
•Nutch Distributed Filesystem(NDFS) in 2004
Google’s MapReduce in 2004
•MapReduce implimented in Nutch 2005
Feb 2006 they moved out of Nutch to form an independent
subproject of Lucene called Hadoop.
6. History (conti..)
At around the same time, Doug Cutting joined Yahoo
February 2008 , Yahoo! announced that its production search index
was being generated by a 10,000-core Hadoop cluster
In January 2008, Hadoop was made its own top-level project at
apache, confirming its success and its diverse, active community.
By this time Hadoop was being used by many other companies
besides Yahoo! such as
• Last.fm
• Facebook
• The New York Times
• Twitter
• Microsoft
• IBM
7. Key Technologies:
•MapReduce
-Computational Parallel Programming Model
-Technology developed by google
•Hadoop Distributed File System
-Distributed File System for large data set
-Inspired by Google File System
9. Key Technologies: MapReduce
• Programming model developed at Google
• Sort/merge based distributed computing
• Initially, it was intended for their internal search/indexing
application, but now used extensively by more organizations
(e.g., Yahoo, Amazon.com, IBM, etc.)
• It is functional style programming (e.g., LISP) that is naturally
parallelizable across a large cluster of workstations or PCS.
• The underlying system takes care of the partitioning of the
input data, scheduling the program’s execution across several
machines, handling machine failures, and managing required
inter-machine communication. (This is the key for Hadoop’s
success)
10. Key Technologies: HDFS
At Google MapReduce operation are run on a special file system
called Google File System (GFS) that is highly optimized for this
purpose.
GFS is not open source.
Doug Cutting and others at Yahoo! reverse engineered the GFS
and called it Hadoop Distributed File System (HDFS).
12. Key Technologies: HDFS
• Very Large Distributed File System
– 10K nodes, 100 million files, 10 PB
• Assumes Commodity Hardware
– Files are replicated to handle hardware failure
– Detect failures and recovers from them
• Optimized for Batch Processing
– Data locations exposed so that computations can move to
where data resides
– Provides very high aggregate bandwidth
• User Space, runs on heterogeneous OS
13. Other Projects on Hadoop:
ZooKeeper: co-ordination services
Pig: A high-level data-flow language and execution
framework for parallel computation.
Hive:A data warehouse infrastructure that provides
data summarization and ad hoc querying.
Chukwa: A data collection system for managing
large distributed systems.
14. Other Projects on Hadoop:
Avro: Apache Avro is a data serialization system.
Avro provides:
•Rich data structures.
•A compact, fast, binary data format.
•A container file, to store persistent data.
•Simple integration with dynamic languages.
Just as Google's Bigtable leverages the
distributed data storage provided by the
Google File System, HBase provides
Bigtable-like capabilities on top of
Hadoop Core.
16. Conclusion:
Hadoop has been very effective solution for companies dealing
with the data in perabytes.
It has solved many problems in industry related to huge data
management and distributed system.
As it is open source, so it is adopted by companies widely.