Hadoop training by keylabs

Jan 26, 2015Download as PPT, PDF2 likes1,030 views

HADOOP online training by Keylabstraining is excellent and teached by real time faculty. Our Hadoop Big Data course content designed as per the current IT industry requirement. Apache Hadoop is having very good demand in the market, huge number of job openings are there in the IT world. Based on this demand, Keylabstrainings has started providing online classes on Hadoop training through the various online training methods like Gotomeeting. For more information Contact us : [email protected]

BigDataBigData
An Introduction byAn Introduction by
KeylabsKeylabs

Need For A New Processing Platform
(BigData)
 What is BigData ?
- Twitter (over 7~ TB/day)
- Facebook (over 10~ TB/day)
- Google (over 20~ PB/day)
 Where does it come from ?
 Existing systems (vertical scalibility)
 Why Hadoop (horizontal scalibility)?

Companies Using Hadoop
 Yahoo
 Google
 Facebook
 LinkedIn
 IBM
 Amazon
 HortonWorks
 Cloudera
 NY Times
 … the list goes on.

What is Hadoop?
 Flexible infrastructure for large scale computation & data
processing on a network of commodity hardware.
 Completely written in java.
 Open source & distributed under Apache license
 Hadoop Core Components: HDFS & MapReduce.
 The Apache Hadoop software library is a
framework that allows for the distributed
processing of large data sets across clusters of
computers using simple programming models.

What Hadoop is Not?
 A File system
 A database
 An online transaction processing (OLTP) system
 Replacement of all programming logic

Hadoop Architecture
Admin Node
Job Tracker
Name Node
Task Tracker
Data Node
Task Tracker
Data Node
Task Tracker
Data Node
MapReduce
Engine
HDFS
Cluster

Distributed File System
Hadoop Distributed File System
Read 1TB Data
1 Machine
•4 I/O Channels
•Each Channel – 100MB/s
10 Machines
•4 I/O Channels
•Each Channel – 100MB/s

What’s so Special About Open Source
Hadoop?

HDFS - Hadoop Distributed File
System
 Design of HDFS
 Where HDFS is not a good fit
 Why Is a Block in HDFS So Large?
 Advantage of HDFS?

HDFS is not for.
 Low Latency Data Access
 Large number of small files.
 Multiple writers, arbitrary file modifications.

NameNode
Deeper Things about Name Node
Request to note down these points

Data Replication
 What is Data Replication

File Write Operation
 File Write Operation

File Read Operation
 File Read Operation

When should we go for Hadoop?
 Data is too huge
 Processes are independent
 Online analytical processing (OLAP)
 Better scalability
 Parallelism
 Unstructured data

THANK YOUTHANK YOU
FOR YOURFOR YOUR
ATTENTION!ATTENTION!

This document discusses Wordcount examples in MapReduce, Cascading, and Scalding. Scalding is a Scala wrapper for Cascading that allows working with data like in-memory collections. It includes parsers for CSV and date formats, as well as helper algorithms. The document also provides instructions for building Scalding jobs, deploying them on EMR, and some tips for development including increasing memory limits and reading data directly from HDFS. Resources with more documentation and examples are also listed.

Hadoop File system (HDFS)Prashant Gupta

Hadoop distributed file systemAmeya Vijay Gokhale

Hadoop Distributed File System (HDFS) is an open-source software framework that provides distributed storage and processing of large datasets across clusters of commodity hardware. HDFS has two main components - HDFS for storage and MapReduce for distributed processing. HDFS uses a master-slave architecture with a NameNode master and DataNodes slaves. The NameNode manages the file system namespace and metadata, while DataNodes store data blocks and report to the NameNode.

Hadoop technologySohini~~ Music

A document describes a multi-node Hadoop cluster used for processing large amounts of data. The cluster includes multiple nodes with RAM and HDD storage. It uses a primary and secondary name node to pull transaction logs, merge changes, and store data to HDD. Hadoop provides a cost effective and reliable solution for processing large datasets across standard servers in a parallel and scalable way.

HADOOP TECHNOLOGY pptsravya raju

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The Hadoop platform uses the Hadoop Distributed File System (HDFS) to reliably store large files across thousands of nodes. It requires a minimum of computing power, memory, storage, and network bandwidth. A recommended cluster size depends on linear relationships between resources and efficiency. Dashboards can be created using data extracted from HDFS to SQL for analytics. The Hadoop architecture is designed to scale easily by adding more servers as data and workloads increase.

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

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The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.

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The document provides an overview of big data, analytics, Hadoop, and related concepts. It discusses what big data is and the challenges it poses. It then describes Hadoop as an open-source platform for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop introduced include HDFS for storage, MapReduce for parallel processing, and various other tools. A word count example demonstrates how MapReduce works. Common use cases and companies using Hadoop are also listed.

Hadoop architecture-tutorialvinayiqbusiness

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This document discusses big data and the Hadoop platform. It provides an overview of Hadoop versions 1 and 2. Hadoop version 1 included the Hadoop Distributed File System (HDFS) for large-scale storage and the MapReduce programming model. Hadoop version 2 introduced YARN to address MapReduce limitations like scalability and availability. The document also briefly describes the Hadoop ecosystem of related projects like Hive for data warehousing and Pig for data flows. It provides examples of Twitter and Facebook's use of Hadoop for big data analytics.

Introduction to Big Data and hadoopSandeep Patil

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This document provides information about J.Ayeesha Parveen, her class details, and incharge staff. It then summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets. Key aspects of Hadoop include its distributed file system (HDFS), MapReduce processing model, and various components like NameNode, DataNodes, JobTracker, and TaskTracker. Common uses of Hadoop include analytics of audio, video, and log files.

Hadoop Distributed File SystemVaibhav Jain

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.

Hadoop: Distributed Data ProcessingCloudera, Inc.

Hadoop is a scalable distributed system for storing and processing large datasets across commodity hardware. It consists of HDFS for storage and MapReduce for distributed processing. A large ecosystem of additional tools like Hive, Pig, and HBase has also developed. Hadoop provides significantly lower costs for data storage and analysis compared to traditional systems and is well-suited to unstructured or structured big data. It has seen wide adoption at companies like Yahoo, Facebook, and eBay for applications like log analysis, personalization, and fraud detection.

HDFSSteve Loughran

HDFS is a distributed file system used for large data sets in Hadoop. It scales well and can support thousands of nodes storing petabytes of data. Several large companies use HDFS in production including Yahoo, Facebook, and Last.fm. HDFS works well for batch jobs but may have issues for real-time logging or serving many small files to a website due to performance and high availability concerns. Improvements are being made to address issues with appends, availability, and reducing disk usage. Alternative solutions exist for low latency use cases.

HDFS ArchitectureJeff Hammerbacher

The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.

2.introduction to hdfsdatabloginfo

This document provides an introduction to HDFS (Hadoop Distributed File System). It discusses what HDFS is, its core components, architecture, and key elements like the NameNode, metadata, and blocks. HDFS is designed for storing very large files across commodity hardware in a fault-tolerant manner and allows for streaming access. While HDFS can handle small datasets, its real power is with large and distributed data.

Hadoop hdfsSudipta Ghosh

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.

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Design of Hadoop Distributed File SystemDr. C.V. Suresh Babu

An Introduction to the World of HadoopUniversity College Cork

The document is an introduction to Hadoop and MapReduce for scientific data mining. It aims to introduce MapReduce thinking and how it enables parallel computing, introduce Hadoop as an open source implementation of MapReduce, and present an example of using Hadoop's streaming API for a scientific data mining task. It also discusses higher-level concepts for performing ad hoc analysis and building systems with Hadoop.

BIG DATA Online Training | Hadoop Online Training with Placement Assistance Computer Trainings Online

Big data and hadoop online training is designed by computer online training to deliver a prompt and distinguished training with sophisticated instructors and live face to face sessions as per your schedule on your own laptop/desktop. We included exclusive lab sessions and theoretical sessions on big data to become a professional hadoop developer. We also give you extensive range of study materials for each module to prepare well for the interviews and get settled in your dream job as a hadoop developer. If you are software professional, analytical professional, ETL developer or project manager then you are welcome to join this course to build strong career as a hadoop developer. We include unique range of topics like MapReduce framework, data loading techniques, Pig and Hive concepts and debugging from hadoop best practices. Our big data course features: • Training on core java and analytical skills • Real time based data analytics project to practice the sessions • Cloud lab environment to practice data projects • More focus on advanced topics of big data • Recorded video sessions and life time free access • Training on code writing with MapReduce framework • Best practices from IT world and case studies Contact us: http://www.computertrainingsonline.com/big-data-online-training Email: [email protected] Call: 770-777-1269

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Dive into the fundamentals of P–N junctions, the heart of every diode and semiconductor device. In this concise presentation, Dr. G.S. Virdi (Former Chief Scientist, CSIR-CEERI Pilani) covers: What Is a P–N Junction? Learn how P-type and N-type materials join to create a diode. Depletion Region & Biasing: See how forward and reverse bias shape the voltage–current behavior. V–I Characteristics: Understand the curve that defines diode operation. Real-World Uses: Discover common applications in rectifiers, signal clipping, and more. Ideal for electronics students, hobbyists, and engineers seeking a clear, practical introduction to P–N junction semiconductors.

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Hadoop training by keylabs

1. BigDataBigData An Introduction byAn Introduction by KeylabsKeylabs

2. Need For A New Processing Platform (BigData)  What is BigData ? - Twitter (over 7~ TB/day) - Facebook (over 10~ TB/day) - Google (over 20~ PB/day)  Where does it come from ?  Existing systems (vertical scalibility)  Why Hadoop (horizontal scalibility)?

3. Origin of Hadoop

4. Companies Using Hadoop  Yahoo  Google  Facebook  LinkedIn  IBM  Amazon  HortonWorks  Cloudera  NY Times  … the list goes on.

5. What is Hadoop?  Flexible infrastructure for large scale computation & data processing on a network of commodity hardware.  Completely written in java.  Open source & distributed under Apache license  Hadoop Core Components: HDFS & MapReduce.  The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.

6. What Hadoop is Not?  A File system  A database  An online transaction processing (OLTP) system  Replacement of all programming logic

7. Three Vs of Hadoop and counting…

8. Hadoop Introduction and Architecture

9. Hadoop High-Level Architecture

10. Hadoop Architecture Admin Node Job Tracker Name Node Task Tracker Data Node Task Tracker Data Node Task Tracker Data Node MapReduce Engine HDFS Cluster

11. Hadoop Cluster

12. Distributed File System Hadoop Distributed File System Read 1TB Data 1 Machine •4 I/O Channels •Each Channel – 100MB/s 10 Machines •4 I/O Channels •Each Channel – 100MB/s

13. What’s so Special About Open Source Hadoop?

14. HDFS - Hadoop Distributed File System  Design of HDFS  Where HDFS is not a good fit  Why Is a Block in HDFS So Large?  Advantage of HDFS?

15. HDFS is not for.  Low Latency Data Access  Large number of small files.  Multiple writers, arbitrary file modifications.

16. HDFS Architecture

17. Let us Zoom into HDFS

18. NameNode Deeper Things about Name Node Request to note down these points

19. DataNode  What is DataNode?

20. NameNode and DataNodes

21. Data Replication  What is Data Replication

22. Data Replication & Rack Awareness

23. File Write Operation  File Write Operation

24. A client writing the data to HDFS

25. File Write Operation in Depth - 1

26. File Write Operation in Depth - 2

27. File Write Operation in Depth - 3

28. File Write Operation in Depth - 4

29. File Write Operation in Depth - 4

30. File Write Operation – Unhappy Path

31. File Read Operation  File Read Operation

32. A client reading data from HDFS

33. File Read Operation in Depth - 1

34. File Read Operation in Depth - 2

35. File Read Operation in Depth - 3

36. File Read Operation - Unhappy Path

37. Secondary NameNode

38. Hadoop Cluster – A Typical Scenario

39. Hadoop Ecosystem

40. Data Loading Techniques and Analysis

41. When should we go for Hadoop?  Data is too huge  Processes are independent  Online analytical processing (OLAP)  Better scalability  Parallelism  Unstructured data

42. THANK YOUTHANK YOU FOR YOURFOR YOUR ATTENTION!ATTENTION!

Hadoop training by keylabs

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