Intro to Hadoop ecosystem and Apache KylinChase Zhang
The document provides an introduction to the Hadoop ecosystem and Apache Kylin. It discusses how technologies like MapReduce, HDFS, Hive, and HBase were developed based on Google papers to address the need for distributed data processing. It introduces Apache Kylin as an OLAP system that performs automatic ETL to enable fast multi-dimensional analysis on large datasets. Key concepts of Kylin like models, cubes, jobs and segments are explained. Comparisons are made between Kylin and alternatives like Hive/SparkSQL and Druid for suitability for multi-tenant analytics use cases requiring sub-second queries.
The Exabyte Journey and DataBrew with CICDShu-Jeng Hsieh
The document discusses LinkedIn's use of Hadoop and HDFS to store and process over 1 exabyte of data across multiple clusters. Some key points:
1. LinkedIn now stores over 1 exabyte of total data across all of its Hadoop clusters, with its largest cluster being 10,000 nodes storing 500 petabytes of data.
2. The Hadoop clusters use a single NameNode for metadata management with an average latency under 10 milliseconds. High availability features help prevent single points of failure.
3. LinkedIn has optimized performance through techniques like Java tuning and satellite clusters to address issues like small files and logging directories.
- Big data refers to large sets of data that businesses and organizations collect, while Hadoop is a tool designed to handle big data. Hadoop uses MapReduce, which maps large datasets and then reduces the results for specific queries.
- Hadoop jobs run under five main daemons: the NameNode, DataNode, Secondary NameNode, JobTracker, and TaskTracker.
- HDFS is Hadoop's distributed file system that stores very large amounts of data across clusters. It replicates data blocks for reliability and provides clients high-throughput access to files.
This document provides an introduction and overview of HDFS and MapReduce in Hadoop. It describes HDFS as a distributed file system that stores large datasets across commodity servers. It also explains that MapReduce is a framework for processing large datasets in parallel by distributing work across clusters. The document gives examples of how HDFS stores data in blocks across data nodes and how MapReduce utilizes mappers and reducers to analyze datasets.
Hadoop Adminstration with Latest Release (2.0)Edureka!
The Hadoop Cluster Administration course at Edureka starts with the fundamental concepts of Apache Hadoop and Hadoop Cluster. It covers topics to deploy, manage, monitor, and secure a Hadoop Cluster. You will learn to configure backup options, diagnose and recover node failures in a Hadoop Cluster. The course will also cover HBase Administration. There will be many challenging, practical and focused hands-on exercises for the learners. Software professionals new to Hadoop can quickly learn the cluster administration through technical sessions and hands-on labs. By the end of this six week Hadoop Cluster Administration training, you will be prepared to understand and solve real world problems that you may come across while working on Hadoop Cluster.
Hadoop Interview Questions and Answers | Big Data Interview Questions | Hadoo...Edureka!
This Hadoop Tutorial on Hadoop Interview Questions and Answers ( Hadoop Interview Blog series: https://goo.gl/ndqlss ) will help you to prepare yourself for Big Data and Hadoop interviews. Learn about the most important Hadoop interview questions and answers and know what will set you apart in the interview process. Below are the topics covered in this Hadoop Interview Questions and Answers Tutorial:
Hadoop Interview Questions on:
1) Big Data & Hadoop
2) HDFS
3) MapReduce
4) Apache Hive
5) Apache Pig
6) Apache HBase and Sqoop
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
#HadoopInterviewQuestions #BigDataInterviewQuestions #HadoopInterview
The document discusses analyzing temperature data using Hadoop MapReduce. It describes importing a weather dataset from the National Climatic Data Center into Eclipse to create a MapReduce program. The program will classify days in the Austin, Texas data from 2015 as either hot or cold based on the recorded temperature. The steps outlined are: importing the project, exporting it as a JAR file, checking that the Hadoop cluster is running, uploading the input file to HDFS, and running the JAR file with the input and output paths specified. The goal is to analyze temperature variation and find the hottest/coldest days of the month/year from the large climate dataset.
Intro to Hadoop ecosystem and Apache KylinChase Zhang
The document provides an introduction to the Hadoop ecosystem and Apache Kylin. It discusses how technologies like MapReduce, HDFS, Hive, and HBase were developed based on Google papers to address the need for distributed data processing. It introduces Apache Kylin as an OLAP system that performs automatic ETL to enable fast multi-dimensional analysis on large datasets. Key concepts of Kylin like models, cubes, jobs and segments are explained. Comparisons are made between Kylin and alternatives like Hive/SparkSQL and Druid for suitability for multi-tenant analytics use cases requiring sub-second queries.
The Exabyte Journey and DataBrew with CICDShu-Jeng Hsieh
The document discusses LinkedIn's use of Hadoop and HDFS to store and process over 1 exabyte of data across multiple clusters. Some key points:
1. LinkedIn now stores over 1 exabyte of total data across all of its Hadoop clusters, with its largest cluster being 10,000 nodes storing 500 petabytes of data.
2. The Hadoop clusters use a single NameNode for metadata management with an average latency under 10 milliseconds. High availability features help prevent single points of failure.
3. LinkedIn has optimized performance through techniques like Java tuning and satellite clusters to address issues like small files and logging directories.
- Big data refers to large sets of data that businesses and organizations collect, while Hadoop is a tool designed to handle big data. Hadoop uses MapReduce, which maps large datasets and then reduces the results for specific queries.
- Hadoop jobs run under five main daemons: the NameNode, DataNode, Secondary NameNode, JobTracker, and TaskTracker.
- HDFS is Hadoop's distributed file system that stores very large amounts of data across clusters. It replicates data blocks for reliability and provides clients high-throughput access to files.
This document provides an introduction and overview of HDFS and MapReduce in Hadoop. It describes HDFS as a distributed file system that stores large datasets across commodity servers. It also explains that MapReduce is a framework for processing large datasets in parallel by distributing work across clusters. The document gives examples of how HDFS stores data in blocks across data nodes and how MapReduce utilizes mappers and reducers to analyze datasets.
Hadoop Adminstration with Latest Release (2.0)Edureka!
The Hadoop Cluster Administration course at Edureka starts with the fundamental concepts of Apache Hadoop and Hadoop Cluster. It covers topics to deploy, manage, monitor, and secure a Hadoop Cluster. You will learn to configure backup options, diagnose and recover node failures in a Hadoop Cluster. The course will also cover HBase Administration. There will be many challenging, practical and focused hands-on exercises for the learners. Software professionals new to Hadoop can quickly learn the cluster administration through technical sessions and hands-on labs. By the end of this six week Hadoop Cluster Administration training, you will be prepared to understand and solve real world problems that you may come across while working on Hadoop Cluster.
Hadoop Interview Questions and Answers | Big Data Interview Questions | Hadoo...Edureka!
This Hadoop Tutorial on Hadoop Interview Questions and Answers ( Hadoop Interview Blog series: https://goo.gl/ndqlss ) will help you to prepare yourself for Big Data and Hadoop interviews. Learn about the most important Hadoop interview questions and answers and know what will set you apart in the interview process. Below are the topics covered in this Hadoop Interview Questions and Answers Tutorial:
Hadoop Interview Questions on:
1) Big Data & Hadoop
2) HDFS
3) MapReduce
4) Apache Hive
5) Apache Pig
6) Apache HBase and Sqoop
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
#HadoopInterviewQuestions #BigDataInterviewQuestions #HadoopInterview
The document discusses analyzing temperature data using Hadoop MapReduce. It describes importing a weather dataset from the National Climatic Data Center into Eclipse to create a MapReduce program. The program will classify days in the Austin, Texas data from 2015 as either hot or cold based on the recorded temperature. The steps outlined are: importing the project, exporting it as a JAR file, checking that the Hadoop cluster is running, uploading the input file to HDFS, and running the JAR file with the input and output paths specified. The goal is to analyze temperature variation and find the hottest/coldest days of the month/year from the large climate dataset.
The document discusses the importance of final year undergraduate projects and provides ideas and suggestions. It recommends using projects as an opportunity to gain hands-on experience with software engineering processes and emerging technologies like machine learning, Big Data, and mobile development. The document provides examples of project ideas involving knowledge management systems, algorithms as a service, clustering algorithms, and building databases. It also discusses strategies for successful project planning and completion, and notes that projects can provide chances to win prizes.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Hadoop Administration Training | Hadoop Administration Tutorial | Hadoop Admi...Edureka!
This Edureka Hadoop Administration Training tutorial will help you understand the functions of all the Hadoop daemons and what are the configuration parameters involved with them. It will also take you through a step by step Multi-Node Hadoop Installation and will discuss all the configuration files in detail. Below are the topics covered in this tutorial:
1) What is Big Data?
2) Hadoop Ecosystem
3) Hadoop Core Components: HDFS & YARN
4) Hadoop Core Configuration Files
5) Multi Node Hadoop Installation
6) Tuning Hadoop using Configuration Files
7) Commissioning and Decommissioning the DataNode
8) Hadoop Web UI Components
9) Hadoop Job Responsibilities
Sam fineberg big_data_hadoop_storage_options_3v9-1Pramod Gosavi
The document provides an overview of big data storage options for Hadoop. It discusses the key aspects of Hadoop storage including the built-in Hadoop file system (HDFS) and other options like using direct attached storage, networked storage, alternative distributed file systems, cloud object storage, and emerging options. The document also provides details on what Hadoop and MapReduce are, how Hadoop uses storage, and distributed file system concepts.
IOD 2013 - Crunch Big Data in the Cloud with IBM BigInsights and HadoopLeons Petražickis
This document provides an overview of Hadoop MapReduce. It discusses map operations, reduce operations, submitting MapReduce jobs, the distributed mergesort engine, the two fundamental data types of MapReduce (key-value pairs and lists), fault tolerance, scheduling, and task execution. Map operations perform transformations on individual data elements, while reduce operations combine the outputs of map tasks into final results. Hadoop MapReduce allows large datasets to be processed in parallel across clusters of computers.
Hadoop Summit San Jose 2015: What it Takes to Run Hadoop at Scale Yahoo Persp...Sumeet Singh
Since 2006, Hadoop and its ecosystem components have evolved into a platform that Yahoo has begun to trust for running its businesses globally. In this talk, we will take a broad look at some of the top software, hardware, and services considerations that have gone in to make the platform indispensable for nearly 1,000 active developers, including the challenges that come from scale, security and multi-tenancy. We will cover the current technology stack that we have built or assembled, infrastructure elements such as configurations, deployment models, and network, and and what it takes to offer hosted Hadoop services to a large customer base.
This document provides a playbook for how Hadoop can support and extend an enterprise data warehouse (EDW) ecosystem. It outlines six common "plays" including using Hadoop to stage structured data, process structured and unstructured data, archive all data, and access data via both the EDW and Hadoop. The plays demonstrate how Hadoop can handle growing volumes of data more cost effectively than solely relying on the EDW. Specifically, Hadoop can be used to load, transform, and analyze structured, unstructured, and archived data, as well as offload processing tasks from the EDW.
Non-geek's big data playbook - Hadoop & EDW - SAS Best PracticesJyrki Määttä
This document provides an overview of how Hadoop can be used to support and extend existing enterprise data warehouse (EDW) systems. It describes six common "plays" or ways that Hadoop interacts with the EDW. The first play is to use Hadoop as a data staging platform to load and transform structured data from applications into the EDW more quickly and at lower cost than using the EDW alone. This allows the EDW resources to focus on analysis while Hadoop handles the processing and storage of large amounts of source data.
This document provides an overview of the Actian DataFlow software. It discusses how Hadoop holds promise for large-scale data analytics but has limitations around performance speed, skill requirements, and incorporating other data sources. Actian DataFlow addresses these challenges by automatically optimizing workloads for high performance on Hadoop through a scale up/out architecture and pipeline/data parallelism. It also enables joining data from multiple sources and shortens analytics project timelines through its visual interface and optimization of the data preparation and analysis process.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduceMahantesh Angadi
The document summarizes a technical seminar presentation on scheduling methods in the Hadoop MapReduce framework. The presentation covers the motivation for Hadoop and MapReduce, provides an introduction to big data and Hadoop, and describes HDFS and the MapReduce programming model. It then discusses challenges in MapReduce scheduling and surveys the literature on existing scheduling methods. The presentation surveys five papers on proposed MapReduce scheduling methods, summarizing the key points of each. It concludes that improving data locality can enhance performance and that future work could consider scheduling algorithms for heterogeneous clusters.
This document discusses Qubole, a cloud data platform for Hadoop and Hive. It describes challenges in running big data technologies in the cloud like dynamic provisioning and separation of compute and storage. Qubole addresses these through techniques such as auto-scaling Hadoop clusters, caching file systems, faster split generation and pipelined file opens to optimize performance for cloud storage like S3. It also discusses using spot instances to lower costs through strategies to make Hadoop resilient to spot interruptions.
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 growing exponentially and is too large for traditional databases to handle. It discusses the 4 V's of big data - volume, velocity, variety and veracity. The document then describes Hadoop as an open-source framework for distributed storage and processing of big data across clusters of commodity hardware. It outlines the key components of Hadoop including HDFS, MapReduce, YARN and related modules. The document also discusses challenges of big data, use cases for Hadoop and provides a demo of configuring an HDInsight Hadoop cluster on Azure.
Hadoop- A Highly Available and Secure Enterprise DataWarehousing solutionEdureka!
This document discusses how Hadoop can provide a highly available and secure enterprise data warehousing solution for big data. It describes how Hadoop addresses the challenges of storing and processing large datasets across clusters using Apache modules like HDFS, YARN, and MapReduce. It also discusses how Hadoop implements high availability for the NameNode through techniques like secondary NameNode and quorum-based journaling. Finally, it presents how Hadoop can function as an effective data warehouse for querying and analyzing large and diverse datasets through systems like Hive, Impala, and BI tools.
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a simple programming model called MapReduce that automatically parallelizes and distributes work across nodes. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and MapReduce execution engine for processing. HDFS stores data as blocks replicated across nodes for fault tolerance. MapReduce jobs are split into map and reduce tasks that process key-value pairs in parallel. Hadoop is well-suited for large-scale data analytics as it scales to petabytes of data and thousands of machines with commodity hardware.
This was presented at NHN on Jan. 27, 2009.
It introduces Big Data, its storages, and its analyses.
Especially, it covers MapReduce debates and hybrid systems of RDBMS and MapReduce.
In addition, in terms of Schema-Free, various non-relational data storages are explained.
This document provides an introduction to Hadoop, including:
- Hadoop challenges such as deployment, change management, and complexity in tuning its many parameters
- The main node types in Hadoop including NameNode, DataNode, and EdgeNode
- Common uses of Hadoop including distributed computing, storage, and presenting data in a SQL-like format for analysis
Hadoop provides a framework for companies to analyze and manage growing volumes of data at a lower cost than traditional solutions. It allows data to be stored for longer periods, enabling new analyses over time. Hadoop deployments typically start with a small test by one department and then expand as other departments see its value for analytics and managing large datasets. It commonly evolves from virtual deployments for testing to dedicated physical hardware as data volumes and performance needs increase. Understanding how Hadoop typically evolves can help companies better manage its adoption and growth within their organization.
The document provides an introduction to big data and Hadoop. It discusses key concepts like the characteristics of big data, use cases across different industries, the Hadoop architecture and ecosystem, and learning paths for different roles working with big data. It also includes examples of big data deployments at companies like Facebook and Sears, and how Hadoop addresses limitations of traditional data warehousing approaches.
This document provides an overview of Hadoop versions 1.x and 2.x. Hadoop 1.x included HDFS for storage and MapReduce for processing. It had limitations around scalability, availability, and resources. Hadoop 2.x introduced YARN to replace MapReduce and address its limitations. YARN provides a framework for multiple data processing models and improved cluster utilization. It allows multiple applications like streaming, interactive query, and graph processing to run on the same Hadoop cluster.
PRACE Autumn school 2021 - Big Data with Hadoop and Keras
27-30 September 2021
Fakulteta za strojništvo
Europe/Ljubljana
Data and scripts are available at: https://www.events.prace-ri.eu/event/1226/timetable/
The document provides an introduction to Hadoop and its distributed file system (HDFS) design and issues. It describes what Hadoop and big data are, and examples of large amounts of data generated every minute on the internet. It then discusses the types of big data and problems with traditional storage. The document outlines how Hadoop provides a solution through its HDFS and MapReduce components. It details the architecture and components of HDFS including the name node, data nodes, block replication, and rack awareness. Some advantages of Hadoop like scalability, flexibility and fault tolerance are also summarized along with some issues like small file handling and security problems.
The document discusses the importance of final year undergraduate projects and provides ideas and suggestions. It recommends using projects as an opportunity to gain hands-on experience with software engineering processes and emerging technologies like machine learning, Big Data, and mobile development. The document provides examples of project ideas involving knowledge management systems, algorithms as a service, clustering algorithms, and building databases. It also discusses strategies for successful project planning and completion, and notes that projects can provide chances to win prizes.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Hadoop Administration Training | Hadoop Administration Tutorial | Hadoop Admi...Edureka!
This Edureka Hadoop Administration Training tutorial will help you understand the functions of all the Hadoop daemons and what are the configuration parameters involved with them. It will also take you through a step by step Multi-Node Hadoop Installation and will discuss all the configuration files in detail. Below are the topics covered in this tutorial:
1) What is Big Data?
2) Hadoop Ecosystem
3) Hadoop Core Components: HDFS & YARN
4) Hadoop Core Configuration Files
5) Multi Node Hadoop Installation
6) Tuning Hadoop using Configuration Files
7) Commissioning and Decommissioning the DataNode
8) Hadoop Web UI Components
9) Hadoop Job Responsibilities
Sam fineberg big_data_hadoop_storage_options_3v9-1Pramod Gosavi
The document provides an overview of big data storage options for Hadoop. It discusses the key aspects of Hadoop storage including the built-in Hadoop file system (HDFS) and other options like using direct attached storage, networked storage, alternative distributed file systems, cloud object storage, and emerging options. The document also provides details on what Hadoop and MapReduce are, how Hadoop uses storage, and distributed file system concepts.
IOD 2013 - Crunch Big Data in the Cloud with IBM BigInsights and HadoopLeons Petražickis
This document provides an overview of Hadoop MapReduce. It discusses map operations, reduce operations, submitting MapReduce jobs, the distributed mergesort engine, the two fundamental data types of MapReduce (key-value pairs and lists), fault tolerance, scheduling, and task execution. Map operations perform transformations on individual data elements, while reduce operations combine the outputs of map tasks into final results. Hadoop MapReduce allows large datasets to be processed in parallel across clusters of computers.
Hadoop Summit San Jose 2015: What it Takes to Run Hadoop at Scale Yahoo Persp...Sumeet Singh
Since 2006, Hadoop and its ecosystem components have evolved into a platform that Yahoo has begun to trust for running its businesses globally. In this talk, we will take a broad look at some of the top software, hardware, and services considerations that have gone in to make the platform indispensable for nearly 1,000 active developers, including the challenges that come from scale, security and multi-tenancy. We will cover the current technology stack that we have built or assembled, infrastructure elements such as configurations, deployment models, and network, and and what it takes to offer hosted Hadoop services to a large customer base.
This document provides a playbook for how Hadoop can support and extend an enterprise data warehouse (EDW) ecosystem. It outlines six common "plays" including using Hadoop to stage structured data, process structured and unstructured data, archive all data, and access data via both the EDW and Hadoop. The plays demonstrate how Hadoop can handle growing volumes of data more cost effectively than solely relying on the EDW. Specifically, Hadoop can be used to load, transform, and analyze structured, unstructured, and archived data, as well as offload processing tasks from the EDW.
Non-geek's big data playbook - Hadoop & EDW - SAS Best PracticesJyrki Määttä
This document provides an overview of how Hadoop can be used to support and extend existing enterprise data warehouse (EDW) systems. It describes six common "plays" or ways that Hadoop interacts with the EDW. The first play is to use Hadoop as a data staging platform to load and transform structured data from applications into the EDW more quickly and at lower cost than using the EDW alone. This allows the EDW resources to focus on analysis while Hadoop handles the processing and storage of large amounts of source data.
This document provides an overview of the Actian DataFlow software. It discusses how Hadoop holds promise for large-scale data analytics but has limitations around performance speed, skill requirements, and incorporating other data sources. Actian DataFlow addresses these challenges by automatically optimizing workloads for high performance on Hadoop through a scale up/out architecture and pipeline/data parallelism. It also enables joining data from multiple sources and shortens analytics project timelines through its visual interface and optimization of the data preparation and analysis process.
BIGDATA- Survey on Scheduling Methods in Hadoop MapReduceMahantesh Angadi
The document summarizes a technical seminar presentation on scheduling methods in the Hadoop MapReduce framework. The presentation covers the motivation for Hadoop and MapReduce, provides an introduction to big data and Hadoop, and describes HDFS and the MapReduce programming model. It then discusses challenges in MapReduce scheduling and surveys the literature on existing scheduling methods. The presentation surveys five papers on proposed MapReduce scheduling methods, summarizing the key points of each. It concludes that improving data locality can enhance performance and that future work could consider scheduling algorithms for heterogeneous clusters.
This document discusses Qubole, a cloud data platform for Hadoop and Hive. It describes challenges in running big data technologies in the cloud like dynamic provisioning and separation of compute and storage. Qubole addresses these through techniques such as auto-scaling Hadoop clusters, caching file systems, faster split generation and pipelined file opens to optimize performance for cloud storage like S3. It also discusses using spot instances to lower costs through strategies to make Hadoop resilient to spot interruptions.
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 growing exponentially and is too large for traditional databases to handle. It discusses the 4 V's of big data - volume, velocity, variety and veracity. The document then describes Hadoop as an open-source framework for distributed storage and processing of big data across clusters of commodity hardware. It outlines the key components of Hadoop including HDFS, MapReduce, YARN and related modules. The document also discusses challenges of big data, use cases for Hadoop and provides a demo of configuring an HDInsight Hadoop cluster on Azure.
Hadoop- A Highly Available and Secure Enterprise DataWarehousing solutionEdureka!
This document discusses how Hadoop can provide a highly available and secure enterprise data warehousing solution for big data. It describes how Hadoop addresses the challenges of storing and processing large datasets across clusters using Apache modules like HDFS, YARN, and MapReduce. It also discusses how Hadoop implements high availability for the NameNode through techniques like secondary NameNode and quorum-based journaling. Finally, it presents how Hadoop can function as an effective data warehouse for querying and analyzing large and diverse datasets through systems like Hive, Impala, and BI tools.
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a simple programming model called MapReduce that automatically parallelizes and distributes work across nodes. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and MapReduce execution engine for processing. HDFS stores data as blocks replicated across nodes for fault tolerance. MapReduce jobs are split into map and reduce tasks that process key-value pairs in parallel. Hadoop is well-suited for large-scale data analytics as it scales to petabytes of data and thousands of machines with commodity hardware.
This was presented at NHN on Jan. 27, 2009.
It introduces Big Data, its storages, and its analyses.
Especially, it covers MapReduce debates and hybrid systems of RDBMS and MapReduce.
In addition, in terms of Schema-Free, various non-relational data storages are explained.
This document provides an introduction to Hadoop, including:
- Hadoop challenges such as deployment, change management, and complexity in tuning its many parameters
- The main node types in Hadoop including NameNode, DataNode, and EdgeNode
- Common uses of Hadoop including distributed computing, storage, and presenting data in a SQL-like format for analysis
Hadoop provides a framework for companies to analyze and manage growing volumes of data at a lower cost than traditional solutions. It allows data to be stored for longer periods, enabling new analyses over time. Hadoop deployments typically start with a small test by one department and then expand as other departments see its value for analytics and managing large datasets. It commonly evolves from virtual deployments for testing to dedicated physical hardware as data volumes and performance needs increase. Understanding how Hadoop typically evolves can help companies better manage its adoption and growth within their organization.
The document provides an introduction to big data and Hadoop. It discusses key concepts like the characteristics of big data, use cases across different industries, the Hadoop architecture and ecosystem, and learning paths for different roles working with big data. It also includes examples of big data deployments at companies like Facebook and Sears, and how Hadoop addresses limitations of traditional data warehousing approaches.
This document provides an overview of Hadoop versions 1.x and 2.x. Hadoop 1.x included HDFS for storage and MapReduce for processing. It had limitations around scalability, availability, and resources. Hadoop 2.x introduced YARN to replace MapReduce and address its limitations. YARN provides a framework for multiple data processing models and improved cluster utilization. It allows multiple applications like streaming, interactive query, and graph processing to run on the same Hadoop cluster.
PRACE Autumn school 2021 - Big Data with Hadoop and Keras
27-30 September 2021
Fakulteta za strojništvo
Europe/Ljubljana
Data and scripts are available at: https://www.events.prace-ri.eu/event/1226/timetable/
The document provides an introduction to Hadoop and its distributed file system (HDFS) design and issues. It describes what Hadoop and big data are, and examples of large amounts of data generated every minute on the internet. It then discusses the types of big data and problems with traditional storage. The document outlines how Hadoop provides a solution through its HDFS and MapReduce components. It details the architecture and components of HDFS including the name node, data nodes, block replication, and rack awareness. Some advantages of Hadoop like scalability, flexibility and fault tolerance are also summarized along with some issues like small file handling and security problems.
This document provides an introduction and overview of core Hadoop technologies including HDFS, MapReduce, YARN, and Spark. It describes what each technology is used for at a high level, provides links to tutorials, and in some cases provides short code examples. The focus is on giving the reader a basic understanding of the purpose and functionality of these central Hadoop components.
Enroll Free Live demo of Hadoop online training and big data analytics courses online and become certified data analyst/ Hadoop developer. Get online Hadoop training & certification.
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.
Hadoop-BAM is a small Java library that allows Binary Alignment Map (BAM) files, a common format for storing aligned DNA sequencing reads, to be directly manipulated and processed on Hadoop. It handles challenges like BAM's binary format and compression by detecting record boundaries and providing access to the files through the Picard SAM API. The library was used to build tools for preprocessing large BAM files for interactive browsing of genome data on Hadoop, demonstrating good scaling on a test of over 50GB of sequencing data from 1000 Genomes Project. Future work involves developing more BAM analysis tools that can leverage Hadoop-BAM.
How Hadoop Revolutionized Data Warehousing at Yahoo and FacebookAmr Awadallah
Hadoop was developed to solve problems with data warehousing systems at Yahoo and Facebook that were limited in processing large amounts of raw data in real-time. Hadoop uses HDFS for scalable storage and MapReduce for distributed processing. It allows for agile access to raw data at scale for ad-hoc queries, data mining and analytics without being constrained by traditional database schemas. Hadoop has been widely adopted for large-scale data processing and analytics across many companies.
Hadoop Training is cover Hadoop Administration training and Hadoop developer by Keylabs. we provide best Hadoop classroom & online-training in Hyderabad&Bangalore.
http://www.keylabstraining.com/hadoop-online-training-hyderabad-bangalore
Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment.
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Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses problems posed by large and complex datasets that cannot be processed by traditional systems. Hadoop uses HDFS for storage and MapReduce for distributed processing of data in parallel. Hadoop clusters can scale to thousands of nodes and petabytes of data, providing low-cost and fault-tolerant solutions for big data problems faced by internet companies and other large organizations.
50 must read hadoop interview questions & answers - whizlabsWhizlabs
At present, the Big Data Hadoop jobs are on the rise. So, here we present top 50 Hadoop Interview Questions and Answers to help you crack job interview..!!
Best Practices for Deploying Hadoop (BigInsights) in the CloudLeons Petražickis
This document provides best practices for optimizing the performance of InfoSphere BigInsights and InfoSphere Streams when deployed in the cloud. It discusses optimizing disk performance by choosing cloud providers and instances with good disk I/O, partitioning and formatting disks correctly, and configuring HDFS to use multiple data directories. It also discusses optimizing Java performance by correctly configuring JVM memory and optimizing MapReduce performance by setting appropriate values for map and reduce tasks based on machine resources.
E2Matrix Jalandhar provides Best Big Data training based on current industry standards that helps attendees to secure placements in their dream jobs at MNCs. E2Matrix Provides Best Big Data Training in Jalandhar Amritsar Ludhiana Phagwara Mohali Chandigarh. E2Matrix is one of the best Big Data training institute offering hands on practical knowledge. At E2Matrix Big Data training is conducted by subject specialist corporate professionals best experience in managing real-time Big Data projects. E2Matrix implements a blend of academic learning and practical sessions to give the student optimum exposure. At E2Matrix’s well-equipped Big Data training Institute aspirants learn the skills for Big Data Overview, Use Cases, Data Analytics Process, Data Preparation, Tools for Data Preparation, Hands on Exercise : Using SQL and NoSql DB's, Hands on Exercise : Usage of Tools, Data Analysis Introduction, Classification, Data Visualization using R, Automation Testing Training on real time projects.
E2Matrix Jalandhar provides Best Big Data training based on current industry standards that helps attendees to secure placements in their dream jobs at MNCs. E2Matrix Provides Best Big Data Training in Jalandhar Amritsar Ludhiana Phagwara Mohali Chandigarh. E2Matrix is one of the best Big Data training institute offering hands on practical knowledge. At E2Matrix Big Data training is conducted by subject specialist corporate professionals best experience in managing real-time Big Data projects. E2Matrix implements a blend of academic learning and practical sessions to give the student optimum exposure. At E2Matrix’s well-equipped Big Data training Institute aspirants learn the skills for Big Data Overview, Use Cases, Data Analytics Process, Data Preparation, Tools for Data Preparation, Hands on Exercise : Using SQL and NoSql DB's, Hands on Exercise : Usage of Tools, Data Analysis Introduction, Classification, Data Visualization using R, Automation Testing Training on real time projects.
E2Matrix Jalandhar provides Best Big Data training based on current industry standards that helps attendees to secure placements in their dream jobs at MNCs. E2Matrix Provides Best Big Data Training in Jalandhar Amritsar Ludhiana Phagwara Mohali Chandigarh. E2Matrix is one of the best Big Data training institute offering hands on practical knowledge. At E2Matrix Big Data training is conducted by subject specialist corporate professionals best experience in managing real-time Big Data projects. E2Matrix implements a blend of academic learning and practical sessions to give the student optimum exposure. At E2Matrix’s well-equipped Big Data training Institute aspirants learn the skills for Big Data Overview, Use Cases, Data Analytics Process, Data Preparation, Tools for Data Preparation, Hands on Exercise : Using SQL and NoSql DB's, Hands on Exercise : Usage of Tools, Data Analysis Introduction, Classification, Data Visualization using R, Automation Testing Training on real time projects.
This document provides an overview of big data and Hadoop. It discusses the concepts of data science, data-driven decision making, and data analytics. It then describes the types of databases and introduces Hadoop as an open source framework for distributed processing of large datasets across clusters of computers. Key aspects of Hadoop covered include the Hadoop approach using MapReduce, the HDFS architecture with NameNode and DataNodes, and how Hadoop compares to relational database management systems (RDBMS). The agenda concludes with an introduction to the trainer, Akash Pramanik.
This document provides an overview of Apache Hadoop, including its architecture, components, and ecosystem. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It consists of HDFS for storage, MapReduce for processing, and YARN for resource management. Related projects in the Hadoop ecosystem include HBase, Hive, Pig, Flume, Sqoop, Oozie, Zookeeper, and Mahout.
This document provides an overview of Hadoop, including its core components HDFS, MapReduce, and YARN. It describes how HDFS stores and replicates data across nodes for reliability. MapReduce is used for distributed processing of large datasets by mapping data to key-value pairs, shuffling, and reducing results. YARN was introduced to improve scalability by separating job scheduling and resource management from MapReduce. The document also gives examples of using MapReduce on a movie ratings dataset to demonstrate Hadoop functionality and running simple MapReduce jobs via the command line.
This document provides an overview of Hadoop and Big Data. It begins with introducing key concepts like structured, semi-structured, and unstructured data. It then discusses the growth of data and need for Big Data solutions. The core components of Hadoop like HDFS and MapReduce are explained at a high level. The document also covers Hadoop architecture, installation, and developing a basic MapReduce program.
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4. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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BigData concept Introduction
BigData:
• More complex data sets than traditional ones, especially
from new data sources. These data sets are so
voluminous that traditional data processing software just
can’t manage them.
• These massive volumes of data can be used to address
business problems you wouldn’t have been able to tackle
before.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 4 / 27
5. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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BigData concept Challanges
BigData challanges:
• Dealing with data growth
• Generating insights in a timely manner
• Integrating disparate data sources
• Validating data
• Securing BigData
• Recruiting and retaining big data talent
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 5 / 27
6. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Basics
• Open source Apache project
• Hadoop Core includes:
• Distributed File System - distributes data
• Map/Reduce - distributes application
• Runs on Java → cross-platform
So, Hadoop:
• Is an open-source framework for writting & running
distributed aplications that process large amounts of
data (≡ BigData volumes).
• Runs on large clusters of commodity machines or on
cloud computing services
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 6 / 27
8. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Distributed file system
Hadoop Distributed File System (HDFS)
• Based on the Google File System (GFS) and provides a
distributed file system that is designed to run on
commodity hardware.
• Highly fault-tolerant and is designed to be deployed on
low-cost hardware.
• Provides high throughput access to application data and
is suitable for applications having large datasets.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 8 / 27
9. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Distributed file system
HDFS:
• HDFS holds very large amount of data and provides
easier access, the files are stored across multiple
machines.
• These files are stored in redundant fashion to rescue the
system from possible data losses in case of failure.
• HDFS also makes applications available to parallel
processing.
HDFS - goals:
• Fault detection and recovery
• Huge datasets
• Hardware at data: a requested task can be done
efficiently, when the computation takes place near the
data. Especially where huge datasets are involved, it
reduces the network traffic and increases the throughput.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 9 / 27
11. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Distributed file system
NameNode:
• Is the master of HDFS that directs the slave DataNode
daemons to perform the low-level I/O task.
• Is the bookkeeper of HDFS; it keeps track of how your
file are broken down into file blocks; which nodes stores
those blocks, and the overall health of the distributed
filesystem
• Executes file system namespace operations like opening,
closing, and renaming files and directories.
• It also determines the mapping of blocks to DataNodes.
Drawback:
The NameNode is the single point of failure of a Hadoop
cluster. For any of the other daemons, if their host
nodes fail for software or hardware reasons, the Hadoop
cluster will likely continue to function smoothly or you
can quickly restart it → not so for the NameNode.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 11 / 27
12. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Distributed file system
DataNode:
• In addition to the NameNode, there are a number of
DataNodes, usually one per node in the cluster, which
manage storage attached to the nodes that they run on.
• The DataNodes are responsible for serving read and
write requests from the file system’s clients.
• The DataNodes also perform block creation, deletion,
and replication upon instruction from the NameNode.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 12 / 27
13. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Distributed file system
(Data)Block:
• HDFS splits huge files into small chunks knows as data
blocks.
• A data block is the smallest unit of data in a HDFS. We
(client ± admin) do not have any control over the data
block like block location. NameNode is the one that
decides all such things.
• The default size of the HDFS block is 128MB which you
can configure. All blocks of the file are the same size
except the last block, which can be either the same size
or smaller.
• The files are split into 128 MB blocks and then stored
into the Hadoop file system. Hadoop is responsible for
distributing the data blocks across multiple nodes.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 13 / 27
14. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Map/Reduce
Hadoop processes data like a pipeline (eg. functional
programming style).
eg: Linux pipe
• Pipelines can help the reuse of processing primitives,
simple chainnigng of existing modules creates new ones.
• Message queues can help the synchronization of
processing primitives.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 14 / 27
15. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Map/Reduce
• Similarly, MapReduce is a programming model for
efficient distributed computing.
• It can be easy scaled of data processing over multiple
computing nodes.
• Under the MapReduce model, the data processing
primitives are called mappers & reducers.
Decomposing a data processing application into mappers
& reducers is sometimes nontrivial.
• Efficiency from
• Streaming through data, reducing seeks.
• Pipelining
• A good fit for a lot of applications, eg:
• Log processing
• Web index building
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 15 / 27
17. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Hadoop Map/Reduce
Phases for MapReduce algorithm:
• mapper: MapReduce takes the input data and feeds
from each data element to the mapper.
• reducer: the reducer processes all the outputs from the
mapper and arrives at a final result.
In simple terms, the mapper is meant to filter and
transform the input into something that the reducer can
aggregate over.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 17 / 27
18. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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How Hadoop works?
How Hadoop works?
Hadoop runs code across a cluster of computers. This process
includes the following core tasks that Hadoop performs:
1 Data is initially divided into directories and files. Files
are divided into uniform sized blocks of 128MB and
64MB (preferably 128MB).
2 These files are then distributed across various cluster
nodes for further processing.
3 HDFS, being on top of the local file system, supervises
the processing.
4 Blocks are replicated for handling hardware failure.
5 Checking that the code was executed successfully.
6 Performing the sort that takes place between the map
and reduce stages.
7 Sending the sorted data to a certain computer.
8 Writing the debugging logs for each job.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 18 / 27
23. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Demo
(Offline) Demo
Our exercise is to count the number of times each word
occcurs in a set of documents. Let’s suppose that or
document has only one sentence:
Do as I say, not as I do
Word Count
as 2
do 2
i 2
not 1
say 1
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 23 / 27
24. Hadoop &
BigData
Gabriel
Răileanu
Agenda
BigData
concept
Introduction
Challanges
Hadoop
Basics
Distributed file
system
Map/Reduce
How Hadoop
works?
Q/A
Bibliography
Demo
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Demo
A simple pseudo-code for this particular word counting:
• This program works fine until the set of documents you
want to process becomes large.
• Looping through all the documents using a single
computer will be extremely time consuming → rewrite
the program so that it distributes the work over several
machines.
Gabriel Răileanu (AC TUIAȘI) Hadoop & BigData December 10, 2019 24 / 27