Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics.
This slide covers the basics of Hadoop and Big Data.
For training queries you can contact us:
Email: admin@designpathshala.com
Call us at: +91 98 188 23045
Visit us at: http://designpathshala.com
Join us at: http://www.designpathshala.com/contact-us
Course details: http://www.designpathshala.com/course/view/65536
Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792
Business Analytics Course details: http://www.designpathshala.com/course/view/196608
Hive - Apache hadoop Bigdata training by Desing PathshalaDesing Pathshala
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics.
This slide covers advance knowledge about Apache Hive.
For training queries you can contact us:
Email: admin@designpathshala.com
Call us at: +91 98 188 23045
Visit us at: http://designpathshala.com
Join us at: http://www.designpathshala.com/contact-us
Course details: http://www.designpathshala.com/course/view/65536
Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792
Business Analytics Course details: http://www.designpathshala.com/course/view/196608
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.
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...Mahantesh Angadi
This document provides an introduction to big data and the installation of a single-node Apache Hadoop cluster. It defines key terms like big data, Hadoop, and MapReduce. It discusses traditional approaches to handling big data like storage area networks and their limitations. It then introduces Hadoop as an open-source framework for storing and processing vast amounts of data in a distributed fashion using the Hadoop Distributed File System (HDFS) and MapReduce programming model. The document outlines Hadoop's architecture and components, provides an example of how MapReduce works, and discusses advantages and limitations of the Hadoop framework.
- Hadoop is a framework for managing and processing big data distributed across clusters of computers. It allows for parallel processing of large datasets.
- Big data comes from various sources like customer behavior, machine data from sensors, etc. It is used by companies to better understand customers and target ads.
- Hadoop uses a master-slave architecture with a NameNode master and DataNode slaves. Files are divided into blocks and replicated across DataNodes for reliability. The NameNode tracks where data blocks are stored.
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 is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses limitations in traditional RDBMS for big data by allowing scaling to large clusters of commodity servers, high fault tolerance, and distributed processing. The core components of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. Hadoop has an ecosystem of additional tools like Pig, Hive, HBase and more. Major companies use Hadoop to process and gain insights from massive amounts of structured and unstructured data.
Big Data Analytics with Hadoop, MongoDB and SQL ServerMark Kromer
This document discusses SQL Server and big data analytics projects in the real world. It covers the big data technology landscape, big data analytics, and three big data analytics scenarios using different technologies like Hadoop, MongoDB, and SQL Server. It also discusses SQL Server's role in the big data world and how to get data into Hadoop for analysis.
Hive - Apache hadoop Bigdata training by Desing PathshalaDesing Pathshala
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics.
This slide covers advance knowledge about Apache Hive.
For training queries you can contact us:
Email: admin@designpathshala.com
Call us at: +91 98 188 23045
Visit us at: http://designpathshala.com
Join us at: http://www.designpathshala.com/contact-us
Course details: http://www.designpathshala.com/course/view/65536
Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792
Business Analytics Course details: http://www.designpathshala.com/course/view/196608
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.
Introduction and Overview of BigData, Hadoop, Distributed Computing - BigData...Mahantesh Angadi
This document provides an introduction to big data and the installation of a single-node Apache Hadoop cluster. It defines key terms like big data, Hadoop, and MapReduce. It discusses traditional approaches to handling big data like storage area networks and their limitations. It then introduces Hadoop as an open-source framework for storing and processing vast amounts of data in a distributed fashion using the Hadoop Distributed File System (HDFS) and MapReduce programming model. The document outlines Hadoop's architecture and components, provides an example of how MapReduce works, and discusses advantages and limitations of the Hadoop framework.
- Hadoop is a framework for managing and processing big data distributed across clusters of computers. It allows for parallel processing of large datasets.
- Big data comes from various sources like customer behavior, machine data from sensors, etc. It is used by companies to better understand customers and target ads.
- Hadoop uses a master-slave architecture with a NameNode master and DataNode slaves. Files are divided into blocks and replicated across DataNodes for reliability. The NameNode tracks where data blocks are stored.
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 is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses limitations in traditional RDBMS for big data by allowing scaling to large clusters of commodity servers, high fault tolerance, and distributed processing. The core components of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. Hadoop has an ecosystem of additional tools like Pig, Hive, HBase and more. Major companies use Hadoop to process and gain insights from massive amounts of structured and unstructured data.
Big Data Analytics with Hadoop, MongoDB and SQL ServerMark Kromer
This document discusses SQL Server and big data analytics projects in the real world. It covers the big data technology landscape, big data analytics, and three big data analytics scenarios using different technologies like Hadoop, MongoDB, and SQL Server. It also discusses SQL Server's role in the big data world and how to get data into Hadoop for analysis.
The document discusses tools for working with big data without needing to know Java. It states that Hadoop can be learned without Java through tools like Pig and Hive that provide high-level languages. Pig uses Pig Latin to simplify complex MapReduce programs, allowing data operations like filters, joins and sorting with only 10 lines of code compared to 200 lines of Java. Hive also does not require Java knowledge, defining a SQL-like language called HiveQL to query and analyze stored data. The document promotes these tools as alternatives to writing custom MapReduce code in Java for non-programmers working with big data.
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/
1) Hadoop is well-suited for data science tasks like exploring large datasets directly, mining larger datasets to achieve better machine learning outcomes, and performing large-scale data preparation efficiently.
2) Traditional data architectures present barriers to speeding data-driven innovation due to the high cost of schema changes, whereas Hadoop's "schema on read" model has a lower barrier.
3) A Hortonworks Sandbox provides a free virtual environment to learn Hadoop and accelerate validating its use for an organization's unique data architecture and use cases.
Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan
The document discusses Hadoop and big data. It defines Hadoop as an open source, scalable, and fault tolerant platform for storing and processing large amounts of unstructured data distributed across machines. It describes Hadoop's core components like HDFS for data storage and MapReduce/YARN for data processing. It also discusses how Hadoop fits into big data scenarios and landscapes, applying Hadoop to save money, the concept of data lakes, Hadoop in the cloud, and big data analytics with Hadoop.
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
Big Data Processing Using Hadoop InfrastructureDmitry Buzdin
The document discusses using Hadoop infrastructure for big data processing. It describes Intrum Justitia SDC, which has data across 20 countries in various formats and a high number of data objects. Hadoop provides solutions like MapReduce and HDFS for distributed storage and processing at scale. The Hadoop ecosystem includes tools like Hive, Pig, HBase, Impala and Oozie that help process and analyze large datasets. Examples of using Hadoop with Java and integrating it into development environments are also included.
This document provides an overview of Hadoop and how it can be used for data consolidation, schema flexibility, and query flexibility compared to a relational database. It describes the key components of Hadoop including HDFS for storage and MapReduce for distributed processing. Examples of industry use cases are also presented, showing how Hadoop enables affordable long-term storage and scalable processing of large amounts of structured and unstructured data.
Facing trouble in distinguishing Big Data, Hadoop & NoSQL as well as finding connection among them? This slide of Savvycom team can definitely help you.
Enjoy reading!
The document provides an overview of big data analytics using Hadoop. It discusses how Hadoop allows for distributed processing of large datasets across computer clusters. The key components of Hadoop discussed are HDFS for storage, and MapReduce for parallel processing. HDFS provides a distributed, fault-tolerant file system where data is replicated across multiple nodes. MapReduce allows users to write parallel jobs that process large amounts of data in parallel on a Hadoop cluster. Examples of how companies use Hadoop for applications like customer analytics and log file analysis are also provided.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
The document discusses big data and Hadoop, providing an introduction to big data, use cases across industries, an overview of the Hadoop ecosystem and architecture, and learning paths for professionals. It also includes examples of how companies like Facebook use large Hadoop clusters to store and process massive amounts of user data at petabyte scale. The presentation aims to help attendees understand big data, Hadoop, and career opportunities working with these technologies.
Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.
This document provides an agenda for a Big Data summer training session presented by Amrit Chhetri. The agenda includes modules on Big Data analytics with Apache Hadoop, installing Apache Hadoop on Ubuntu, using HBase, advanced Python techniques, and performing ETL with tools like Sqoop and Talend. Amrit introduces himself and his background before delving into the topics to be covered in the training.
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
The document discusses big data and its applications. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It outlines the three V's of big data - volume, variety, and velocity. Various types of structured, semi-structured, and unstructured data are described. Examples are given of how big data is used in various industries like automotive, finance, manufacturing, policing, and utilities to improve products, detect fraud, perform simulations, track suspects, and monitor assets. Popular big data software like Hadoop and MongoDB are also mentioned.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
This document discusses Scala and big data technologies. It provides an overview of Scala libraries for working with Hadoop and MapReduce, including Scalding which provides a Scala DSL for Cascading. It also covers Spark, a cluster computing framework that operates on distributed datasets in memory for faster performance. Additional Scala projects for data analysis using functional programming approaches on Hadoop are also mentioned.
The document discusses tools for working with big data without needing to know Java. It states that Hadoop can be learned without Java through tools like Pig and Hive that provide high-level languages. Pig uses Pig Latin to simplify complex MapReduce programs, allowing data operations like filters, joins and sorting with only 10 lines of code compared to 200 lines of Java. Hive also does not require Java knowledge, defining a SQL-like language called HiveQL to query and analyze stored data. The document promotes these tools as alternatives to writing custom MapReduce code in Java for non-programmers working with big data.
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/
1) Hadoop is well-suited for data science tasks like exploring large datasets directly, mining larger datasets to achieve better machine learning outcomes, and performing large-scale data preparation efficiently.
2) Traditional data architectures present barriers to speeding data-driven innovation due to the high cost of schema changes, whereas Hadoop's "schema on read" model has a lower barrier.
3) A Hortonworks Sandbox provides a free virtual environment to learn Hadoop and accelerate validating its use for an organization's unique data architecture and use cases.
Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan
The document discusses Hadoop and big data. It defines Hadoop as an open source, scalable, and fault tolerant platform for storing and processing large amounts of unstructured data distributed across machines. It describes Hadoop's core components like HDFS for data storage and MapReduce/YARN for data processing. It also discusses how Hadoop fits into big data scenarios and landscapes, applying Hadoop to save money, the concept of data lakes, Hadoop in the cloud, and big data analytics with Hadoop.
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
Big Data Processing Using Hadoop InfrastructureDmitry Buzdin
The document discusses using Hadoop infrastructure for big data processing. It describes Intrum Justitia SDC, which has data across 20 countries in various formats and a high number of data objects. Hadoop provides solutions like MapReduce and HDFS for distributed storage and processing at scale. The Hadoop ecosystem includes tools like Hive, Pig, HBase, Impala and Oozie that help process and analyze large datasets. Examples of using Hadoop with Java and integrating it into development environments are also included.
This document provides an overview of Hadoop and how it can be used for data consolidation, schema flexibility, and query flexibility compared to a relational database. It describes the key components of Hadoop including HDFS for storage and MapReduce for distributed processing. Examples of industry use cases are also presented, showing how Hadoop enables affordable long-term storage and scalable processing of large amounts of structured and unstructured data.
Facing trouble in distinguishing Big Data, Hadoop & NoSQL as well as finding connection among them? This slide of Savvycom team can definitely help you.
Enjoy reading!
The document provides an overview of big data analytics using Hadoop. It discusses how Hadoop allows for distributed processing of large datasets across computer clusters. The key components of Hadoop discussed are HDFS for storage, and MapReduce for parallel processing. HDFS provides a distributed, fault-tolerant file system where data is replicated across multiple nodes. MapReduce allows users to write parallel jobs that process large amounts of data in parallel on a Hadoop cluster. Examples of how companies use Hadoop for applications like customer analytics and log file analysis are also provided.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
The document discusses big data and Hadoop, providing an introduction to big data, use cases across industries, an overview of the Hadoop ecosystem and architecture, and learning paths for professionals. It also includes examples of how companies like Facebook use large Hadoop clusters to store and process massive amounts of user data at petabyte scale. The presentation aims to help attendees understand big data, Hadoop, and career opportunities working with these technologies.
Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.
This document provides an agenda for a Big Data summer training session presented by Amrit Chhetri. The agenda includes modules on Big Data analytics with Apache Hadoop, installing Apache Hadoop on Ubuntu, using HBase, advanced Python techniques, and performing ETL with tools like Sqoop and Talend. Amrit introduces himself and his background before delving into the topics to be covered in the training.
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
The document discusses big data and its applications. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It outlines the three V's of big data - volume, variety, and velocity. Various types of structured, semi-structured, and unstructured data are described. Examples are given of how big data is used in various industries like automotive, finance, manufacturing, policing, and utilities to improve products, detect fraud, perform simulations, track suspects, and monitor assets. Popular big data software like Hadoop and MongoDB are also mentioned.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
This document discusses Scala and big data technologies. It provides an overview of Scala libraries for working with Hadoop and MapReduce, including Scalding which provides a Scala DSL for Cascading. It also covers Spark, a cluster computing framework that operates on distributed datasets in memory for faster performance. Additional Scala projects for data analysis using functional programming approaches on Hadoop are also mentioned.
Big data PPT prepared by Hritika Raj (Shivalik college of engg.)Hritika Raj
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, risks and benefits. Big data is characterized by volume, velocity and variety of structured and unstructured data that is growing exponentially. It is generated from sources like mobile devices, sensors, social media and more. Tools like Hadoop, MapReduce and data analytics are used to extract value from big data. Potential applications include healthcare, security, manufacturing and more. Risks include privacy and scale, while benefits include improved decision making and new business opportunities. The big data industry is rapidly growing and transforming IT and business.
The document discusses big data, including what it is, sources of big data like social media and stock exchange data, and the three Vs of big data - volume, velocity, and variety. It then discusses Hadoop, the open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed computation, and YARN which manages computing resources. The document also provides overviews of Pig and Jaql, programming languages used for analyzing data in Hadoop.
Capacity Management and BigData/Hadoop - Hitchhiker's guide for the Capacity ...Renato Bonomini
The document discusses capacity planning and performance tuning for Hadoop big data systems. It begins with an agenda that covers why capacity planners need to prepare for Hadoop, an overview of the Hadoop ecosystem, capacity planning and performance tuning of Hadoop, getting started, and the importance of measurement. The document then discusses various components of the Hadoop ecosystem and provides guidance on analyzing different types of workloads and components.
This document discusses big data, including its definition, characteristics of volume, velocity, and variety. It describes sources of big data like administrative data, transactions, public data, sensor data, and social media. It discusses processing big data using techniques like Hadoop MapReduce. It outlines benefits like real-time decision making but also drawbacks like security, privacy, and performance issues. It provides some facts about the size of data generated daily by companies and potential impacts and future growth of the big data industry and job market.
Somappa Srinivasan of sparrowanalytics.com presents their goal of creating a scalable recommendation engine using Hadoop and real-time analytics. Their system will acquire data from various sources into a data lake stored on Hadoop. A real-time engine will then select models, score recommendations, and return personalized suggestions to users as they browse. The components outlined include data acquisition, ingestion into a data hub of Hive and HBase tables, model selection, scoring, recommendation generation, and a UI dashboard.
This document provides an overview of bio big data and related technologies. It discusses what big data is and why bio big data is necessary given the large size of genomic data sets. It then outlines and describes Hadoop, Spark, machine learning, and streaming in the context of bio big data. For Hadoop, it explains HDFS, MapReduce, and the Hadoop ecosystem. For Spark, it covers RDDs, Spark SQL, MLlib, and Spark Streaming. The document is intended as an introduction to key concepts and tools for working with large biological data sets.
Somappa Srinivasan of sparrowanalytics.com presents their goal of creating a scalable recommendation engine using Hadoop and real-time analytics. Their system will acquire data from various sources into a data lake stored on Hadoop. A real-time engine will then process user requests, select predictive models, score items, and recommend contextual options to users browsing movies. The system components include data acquisition, ingestion into a data hub of Hive and HBase tables, a real-time engine for validation, modeling, scoring and recommendations, and a UI dashboard.
Digital has engendered a fundamental shift in the way we behave, think and perform business. One of the most essential transformations for today’s organisations is to adapt to how the customer has changed. This obviously has a massive impact on the salesforce and its methods. The Customer Journey has changed dramatically, becoming far more digitized and needs consistent use of many tools, technologies and methods to effectively reach the target audience.
Big Data - How Marketing Has Revolutionised - by Sean SingletonDigital Annexe
Through BIG DATA, Marketeers can now blend human psychology and understanding with behavioural insights to create communication messages and platforms that do not only resonate with the consumer but find them where and when the information is most needed. At Digital Annexe, we believe BIG DATA is going to completely revolutionize the marketing industry forever in 5 key ways.
Exploiter les potentialités du Big Data et du marketing automation en B2BSparklane
Au cours de cette matinée de conférences, cinq experts se sont succédés à la tribune. Compte rendu de l’intervention de Roland Koltchakian (Oracle Marketing Cloud), consacrée à l’exploitation des potentialités du Big Data et du marketing automation en B to B.
1. Le marketing B to B n’échappe pas à la complexité
2. Face à cette complexité croissante, les marketers se sentent sous pression
3. Mais ce contexte exigeant porte aussi de nouvelles opportunités…
4. Un exemple de partenariat au service des directions marketing : Oracle + Zebaz
En synthèse…
Internet of Things. Definition of a conceptJesús Fontecha
The document discusses the Internet of Things (IoT) and how it will further change people's lives beyond how the internet has already changed them. It defines the IoT as the evolution of the internet where everyday physical objects are connected through sensors and can exchange data. It provides examples of how the IoT could enhance various areas like smart parking, farming, lighting, payment systems, wearable devices, environmental monitoring and more. It also discusses issues around how much personal data would be collected through smart devices and shared, raising questions about privacy, ethics and whether the IoT will benefit all people.
This document provides teaching material on distributed systems replication from the book "Distributed Systems: Concepts and Design". It includes slides on replication concepts such as performance enhancement through replication, fault tolerance, and availability. The slides cover replication transparency, consistency requirements, system models, group communication, fault-tolerant and highly available services, and consistency criteria like linearizability.
Real Time Marketing Big Data Analytics Social Marketing Intelligence DisruptionChase McMichael
Social Analytics-driven Real-time Marketing with Domain-specific Use Cases The Take away from this event:
1. What is Real Time Marketing and how marketers are using it
2. Why social analysis "The Science" is here to stay and how it works
3. Beyond the buzz word of Big Data - real use cases on how SMBs and Big cos are harnessing insight, trends and content to engage with their customers.
Start your Monday off right and be the smartest person in the room. @chasemcmichael
Presented by Mark Miller, Software Developer, Cloudera
Apache Lucene/Solr committer Mark Miller talks about how Solr has been integrated into the Hadoop ecosystem to provide full text search at "Big Data" scale. This talk will give an overview of how Cloudera has tackled integrating Solr into the Hadoop ecosystem and highlights some of the design decisions and future plans. Learn how Solr is getting 'cozy' with Hadoop, which contributions are going to what project, and how you can take advantage of these integrations to use Solr efficiently at "Big Data" scale. Learn how you can run Solr directly on HDFS, build indexes with Map/Reduce, load Solr via Flume in 'Near Realtime' and much more.
eMarketer Webinar: Data Management Platforms—Using Big Data to Power Marketin...eMarketer
Join eMarketer for a discussion on how Data Management Platforms (DMPs) are enabling marketers to use their big data to make smarter and more efficient marketing decisions.
Hitachi Data Systems Hadoop Solution. Customers are seeing exponential growth of unstructured data from their social media websites to operational sources. Their enterprise data warehouses are not designed to handle such high volumes and varieties of data. Hadoop, the latest software platform that scales to process massive volumes of unstructured and semi-structured data by distributing the workload through clusters of servers, is giving customers new option to tackle data growth and deploy big data analysis to help better understand their business. Hitachi Data Systems is launching its latest Hadoop reference architecture, which is pre-tested with Cloudera Hadoop distribution to provide a faster time to market for customers deploying Hadoop applications. HDS, Cloudera and Hitachi Consulting will present together and explain how to get you there. Attend this WebTech and learn how to: Solve big-data problems with Hadoop. Deploy Hadoop in your data warehouse environment to better manage your unstructured and structured data. Implement Hadoop using HDS Hadoop reference architecture. For more information on Hitachi Data Systems Hadoop Solution please read our blog: http://blogs.hds.com/hdsblog/2012/07/a-series-on-hadoop-architecture.html
The document provides an overview of a conference on big data from September 30th to October 4th. It discusses how big data is more than just data and outlines some of the key challenges organizations face in working with big data, including not utilizing enough of their data and legacy techniques being insufficient. It also notes drivers for big data like growing data volumes and expectations for real-time access. The document highlights challenges often overlooked, such as organizational resistance to change and ensuring solutions are sustainable. It provides examples of HP's big data solutions and successes customers have seen.
10 июня в центре Digital October состоялась лекция эксперта в области больших данных Бьярна Берга.
http://digitaloctober.ru/events/knowledge_stream_informatsiya_dlya_biznesa
Bn1028 demo hadoop administration and developmentconline training
The document is an introduction to Hadoop administration and development training. It discusses big data challenges and how Hadoop provides a framework to process and analyze large datasets across clusters of commodity hardware. Specifically, it covers what Hadoop is and its ecosystem including HDFS, MapReduce, Pig, Hive, HBase and other related technologies. It also discusses use cases, architecture, benefits and how companies are using Hadoop to solve big data problems.
Powering Real-Time Big Data Analytics with a Next-Gen GPU DatabaseKinetica
Freed from the constraints of storage, network and memory, many big data analytics systems now are routinely revealing themselves to be compute bound. To compensate, big data analytic systems often result in wide horizontal sprawl (300-node Spark or NoSQL clusters are not unusual!)— to bring in enough compute for the task at hand. High system complexity and crushing operational costs often result. As the world shifts from physical to virtual assets and methods of engagement, there is an increasing need for systems of intelligence to live alongside the more traditional systems of record and systems of analysis. New approaches to data processing are required to support the real-time processing of data required to drive these systems of intelligence.
Join 451 Research and Kinetica to learn:
•An overview of the business and technical trends driving widespread interest in real-time analytics
•Why systems of analysis need to be transformed and augmented with systems of intelligence bringing new approaches to data processing
•How a new class of solution—a GPU-accelerated, scale out, in-memory database–can bring you orders of magnitude more compute power, significantly smaller hardware footprint, and unrivaled analytic capabilities.
•Hear how other companies in a variety of industries, such as financial services, entertainment, pharmaceutical, and oil and gas, benefit from augmenting their legacy systems with a modern analytics database.
Hadoop and the Data Warehouse: Point/Counter PointInside Analysis
Robin Bloor and Teradata
Live Webcast on April 22, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=2e69345c0a6a4e5a8de6fc72652e3bc6
Can you replace the data warehouse with Hadoop? Is Hadoop an ideal ETL subsystem? And what is the real magic of Hadoop? Everyone is looking to capitalize on the insights that lie in the vast pools of big data. Generating the value of that data relies heavily on several factors, especially choosing the right solution for the right context. With so many options out there, how do organizations best integrate these new big data solutions with the existing data warehouse environment?
Register for this episode of The Briefing Room to hear veteran analyst Dr. Robin Bloor as he explains where Hadoop fits into the information ecosystem. He’ll be briefed by Dan Graham of Teradata, who will offer perspective on how Hadoop can play a critical role in the analytic architecture. Bloor and Graham will interactively discuss big data in the big picture of the data center and will also seek to dispel several common misconceptions about Hadoop.
Visit InsideAnlaysis.com for more information.
Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation.
Hadoop Master Class : A concise overviewAbhishek Roy
Abhishek Roy will teach a master class on Big Data and Hadoop. The class will cover what Big Data is, the history and background of Hadoop, how to set up and use Hadoop, and tools like HDFS, MapReduce, Pig, Hive, Mahout, Sqoop, Flume, Hue, Zookeeper and Impala. The class will also discuss real world use cases and the growing market for Big Data tools and skills.
Analysis of historical movie data by BHADRABhadra Gowdra
Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. In this paper, we have proposed a recommendation system for the large amount of data available on the web in the form of ratings, reviews, opinions, complaints, remarks, feedback, and comments about any item (product, event, individual and services) using Hadoop Framework.
OBJETIVOS DO EVENTO
Fortalecer os estudos na área de Business Intelligence;
Promover o desenvolvimento de técnicas, metodologias e interfaces junto a comunidade;
Gerar interação entre estudantes, profissionais e empresas aumentado a qualidade do Networking.
Big Data Integration Webinar: Getting Started With Hadoop Big DataPentaho
This document discusses getting started with big data analytics using Hadoop and Pentaho. It provides an overview of installing and configuring Hadoop and Pentaho on a single machine or cluster. Dell's Crowbar tool is presented as a way to quickly deploy Hadoop clusters on Dell hardware in about two hours. The document also covers best practices like leveraging different technologies, starting with small datasets, and not overloading networks. A demo is given and contact information provided.
Danny Bickson - Python based predictive analytics with GraphLab Create PyData
Dato is presenting on their machine learning platform GraphLab Create. Key points include:
- GraphLab Create allows users to build intelligent applications using machine learning across different data types like images, text, graphs and tables.
- It provides tools for data ingestion, transformation, model building, and deployment in a machine learning pipeline.
- Some benefits of GraphLab Create are its efficient storage, ability to handle large datasets that exceed RAM size, and support for multi-core processing. It also has additional algorithms and automatic feature expansion compared to sklearn.
- Example intelligent applications that can be built include recommenders, fraud detection, ad targeting, personalized medicine, and more.
The document discusses how Orbitz Worldwide integrated Hadoop into its enterprise data infrastructure to handle large volumes of web analytics and transactional data. Some key points:
- Orbitz used Hadoop to store and analyze large amounts of web log and behavioral data to improve services like hotel search. This allowed analyzing more data than their previous 2-week data archive.
- They faced initial resistance but built a Hadoop cluster with 200TB of storage to enable machine learning and analytics applications.
- The challenges now are providing analytics tools for non-technical users and further integrating Hadoop with their existing data warehouse.
Architecting for Big Data - Gartner Innovation Peer Forum Sept 2011Jonathan Seidman
The document discusses how Orbitz Worldwide integrated Hadoop into its enterprise data infrastructure to handle large volumes of web analytics and transactional data. Some key points:
- Orbitz used Hadoop to store and analyze large amounts of web log and behavioral data to improve services like hotel search. This allowed analyzing more data than their previous 2-week data archive.
- They faced initial resistance but built a Hadoop cluster with 200TB of storage to enable machine learning and analytics applications.
- The challenges now are providing analytics tools for non-technical users and further integrating Hadoop with their existing data warehouse.
How to design and implement a data ops architecture with sdc and gcpJoseph Arriola
Do you know how to use StreamSets Data Collector with Google Cloud Platform (GCP)? In this session we'll explain how YaloChat designed and implemented a streaming architecture that is sustainable, operable and scalable. Discover how we deployed Data Collector to integrate GCP components such as Pub / Sub and BigQuery to achieve DataOps in the cloud
Storing, accessing, and analyzing large amounts of data from diverse sources and making it easily accessible to deliver actionable insights for users can be challenging for data driven organizations. The solution for customers is to optimize scaling and create a unified interface to simplify analysis. Qubole helps customers simplify their big data analytics with speed and scalability, while providing data analysts and scientists self-service access on the AWS Cloud. Join Qubole and AWS to discuss how Auto Scaling and Amazon EC2 Spot pricing can enable customers to efficiently turn data into insights. We'll talk about best practices for migrating from an on-premises Big Data architecture to the AWS Cloud.
Join us to learn:
• Learn how to more easily create elastic Hadoop, Spark, and other Big Data clusters for dynamic, large-scale workloads
• Best practices for Auto Scaling and Amazon EC2 Spot instances for cost optimization of Big Data workloads
• Best practices for deploying or migrating to Big Data on the
AWS Cloud
Who should attend: IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
2. Course Details
The Motivation for Hadoop
Hadoop: Basic Concepts
Writing a MapReduce Program
Common MapReduce Algorithms
PIG Concepts
Hive Concepts
Working with Sqoop
Working with Flume
OOZIE Concepts
HUE Concepts
Reporting Tools
Project
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2
3. Apache Hadoop
The Motivation for Hadoop
Design Pathshala
April 22, 2014
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3
4. Apache Hadoop Bigdata
Training By Design Pathshala
Contact us on: admin@designpathshala.com
Or Call us at: +91 120 260 5512 or +91 98 188 23045
Visit us at: http://designpathshala.com
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4
5. Design Pathshala
Every one of our courses, written by experts in their respective fields.
We try our best to make you connect real life examples with real business
practices.
Learn and apply to work or your own business.
We provide online classes on different subjects, including Oracle HRMS,
Peoplesoft HRMS & JAVA.
We have both Weekday as well as Weekend classes.
5
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6. How data comes?
6
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9. Volume .. Amount of data
~3 ZB of
data exist in
the digital
universe
today.
>300 TB of
data in U.S.
Library of
Congress.
Facebook
has 30+ PB.
~2.5 PB of
data in
DWH.
+10PB DWH
size.
9
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10. Velocity .. How Rapidly data is growing
48 hours of
new video
every minute
571 new
websites every
minute
500+ TB to
Facebook.
175 million
tweets every
day
1+ million
customer
transactions
every hour
Data
production will
be 44 times
greater in 2020
than it was in
2009.
10
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11. Variety.. How Rapidly data is growing
Structured
• Traditional
Databases
• Numeric data
Semi -
structured
• Json
• XML
Unstructured
• Text documents
• Email
• Video
• Audio
• Machine
Generated
11
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12. Apache Hadoop Bigdata
Training By Design Pathshala
Contact us on: admin@designpathshala.com
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12
13. How Companies minting on Bigdata!
Predict exactly what customers want before they ask for it
Marketing Campaign
Improve customer service
Fraud Detection
Get customers excited about their own data
Identify customer pain points and solve them
Reduce health care costs and improve treatment
Social Graph Analysis & Sentiment Analysis
Research and development
13
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14. How data is used by some big Companies for
different business analysis.
14
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27. Who uses Hadoop?
27
42,000 nodes
as on July
2011
4100 nodes
1400
nodes
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28. What is Hadoop
Hadoop is a framework for distributed processing of large datasets across
large clusters of commodity computers using simple programing model.
Large datasets Terabytes or petabytes of data
Large clusters hundreds or thousands of nodes
Hadoop is open-source implementation for Google MapReduce
Hadoop is based on a simple programming model called MapReduce
Hadoop is based on a simple data model, any data will fit
28
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29. Apache Hadoop Bigdata
Training By Design Pathshala
Contact us on: admin@designpathshala.com
Or Call us at: +91 120 260 5512 or +91 98 188 23045
Visit us at: http://designpathshala.com
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29
30. What makes it especially useful
Scalable: It can reliably store and process petabytes.
Economical: It distributes the data and processing across clusters of commonly available
computers (in thousands).
Efficient: By distributing the data, it can process it in parallel on the nodes where the
data is located.
Reliable: It automatically maintains multiple copies of data and automatically redeploys
computing tasks based on failures.
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30
31. Hadoop: Assumptions
Hardware will fail.
Applications need a write-once-read-many access model.
Data transfer and I/o is bottleneck
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
Move logic rather than data
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31
32. Secondary
NameNode
Client
HDFS Architecture
NameNode
Data Nodes
Metadata
NameNode : Contains information about data
DataNode : Contains physical data
SecondaryNameNode: Keeps reading data from NN
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32
33. Distributed File System
Single Namespace for entire cluster
Data Coherency
– Write-once-read-many access model
– Client can only append to existing files
Files are broken up into blocks
– Typically 64 MB block size
– Each block replicated on multiple DataNodes
Intelligent Client
– Client can find location of blocks
– Client accesses data directly from DataNode
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33
39. Apache Hadoop and the Hadoop Ecosystem
MapReduce
A distributed data processing model and execution environment that runs on large
clusters of commodity machines.
HDFS
A distributed filesystem that runs on large clusters of commodity machines.
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39
40. Apache Hadoop Bigdata
Training By Design Pathshala
Contact us on: admin@designpathshala.com
Or Call us at: +91 120 260 5512 or +91 98 188 23045
Visit us at: http://designpathshala.com
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40
41. Apache Hadoop and the Hadoop Ecosystem
Pig
A data flow language and execution environment for exploring very large datasets.
Pig runs on HDFS and MapReduce clusters.
Hive
A distributed data warehouse. Hive manages data stored in HDFS and provides a
query language based on SQL (and which is translated by the runtime engine to
MapReduce jobs) for querying the data.
Sqoop
A tool for efficiently moving data between relational databases and HDFS.
Oozie
Oozie is a workflow scheduler system to manage Apache Hadoop jobs. Oozie
Workflow jobs are Directed Acyclical Graphs (DAGs) of actions.
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41
42. Apache Hadoop and the Hadoop Ecosystem
HBase
A distributed, column-oriented database. HBase uses HDFS for its underlying
storage, and supports both batch-style computations using MapReduce and point
queries (random reads).
ZooKeeper
A distributed, highly available coordination service. ZooKeeper provides primitives
such as distributed locks that can be used for building distributed applications.
Flume
Flume is a distributed, reliable, and available service for efficiently collecting,
aggregating, and moving large amounts of log data.
Strom
Apache Storm is a free and open source distributed realtime computation system.
Storm makes it easy to reliably process unbounded streams of data, doing for
realtime processing what Hadoop did for batch processing.
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42
43. Apache Hadoop and the Hadoop Ecosystem
Spark & Spark
Apache Spark™ is a fast and general engine for large-scale data processing.
Drill
Apache Drill provides direct queries on self-describing and semi-structured data in
files (such as JSON, Parquet) and HBase tables without needing to specify metadata
definitions in a centralized store such as Hive metastore.
Avro
A serialization system for efficient, cross-language RPC, and persistent data
storage.
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43
51. Which Hadoop Distribution?
Type Distribution Pros Cons
Pureplay
(Apache/Ope
nSource)
Hortonworks 100% Open source version
Integration/Services focused
Extensive partnership
network
Slower interactive
queries
Cloudera Widely used distribution
Faster interactive queries
Extensive tooling
Proprietary extensions
like Impala
Commercial version only
MapR Enterprise and Production
ready focused
Works with NFS & Native Unix
commands
Less focused on using
new Hadoop features
such as Yarn, etc
Proprietary PivotalHD Faster interactive query
support with Greenplum
Integrates with CloudFoundry
PaaS platform
Proprietary extensions
Not easy to decouple
IBM Offer open source without
branch version
Integrated with PaaS and IBM
tools
Limited releases
Expensive
May not be easy to
decouple 51
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52. Disk 1 Disk 5
2 Disk 6
2
Disk 7
Disk 2
Disk 3
1
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52
Disk 9
1 2 3
Racks
Disk 10
Disk 11
Disk 8 Disk 12
Disk 4
1
1
2
3
3
3
Data blocks
Rack 1 Rack 2 Rack 3
File F 1 2 3 4 5
Blocks (64 MB)
53. Block Placement
Current Strategy
-- One replica on local node
-- Second replica on a remote rack
-- Third replica on same remote rack
-- Additional replicas are randomly placed
Clients read from nearest replica
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53
54. Apache Hadoop Bigdata
Training By Design Pathshala
Contact us on: admin@designpathshala.com
Or Call us at: +91 120 260 5512 or +91 98 188 23045
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54
55. Main Properties of HDFS
Large: A HDFS instance may consist of thousands of server machines, each
storing part of the file system’s data
Replication: Each data block is replicated many times (default is 3)
Failure: Failure is the norm rather than exception
Fault Tolerance: Detection of faults and quick, automatic recovery from
them is a core architectural goal of HDFS
Datanodes send heartbeats to Name node
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55
57. NameNode Metadata
Meta-data in Memory
Types of Metadata
– List of files
– List of Blocks for each file
– List of DataNodes for each block
– File attributes, e.g creation time, replication factor
A Transaction Log
– Records file creations, file deletions. etc
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57
58. DataNode
A Block Server
– Stores data in the local file system
– Stores meta-data of a block
– Serves data to Clients
Block Report
– Periodically sends a report of all existing blocks to the
NameNode
Facilitates Pipelining of Data
– Forwards data to other specified DataNodes
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59. Apache Hadoop Bigdata
Training By Design Pathshala
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60. Hadoop Master/Slave Architecture
Hadoop is designed as a master-slave shared-nothing architecture
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Master node (single node)
Many slave nodes
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61. JobTracker
Master node runs JobTracker instance, which accepts Job requests from
clients
There is only one JobTracker daemon running per hadoop cluster
Determine the execution plan by determining which files to process
Assigns Nodes to different task
Monitor all tasks as they are running
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62. TaskTracker
Manages execution of individual tasks on each data node
One TaskTracker each data node
Each TaskTracker can spawn multiple JVM’s to handle many map or reduce
task in parallel
TaskTracker constantly communicate with job tracker
JobTracker fails to receive heartbeat from TaskTracker in specified amount of
time, it assumes the task tracker has crashed. In such a scenario, job tracker
will resubmit the task to some other TaskTracker.
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69. Replication Engine
NameNode detects DataNode failures
Chooses new DataNodes for new replicas
Balances disk usage
Balances communication traffic to DataNodes
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70. Data Pipeline & Write Anatomy
HDFS Client Add Block Name Node
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Data Node
Data Node
Data Node
Write
Ack
Complete
71. Apache Hadoop Bigdata
Training By Design Pathshala
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Visit us at: http://designpathshala.com
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71
72. Data Pipelining
Client retrieves a list of DataNodes on which to place
replicas of a block
Client writes block to the first DataNode
The first DataNode forwards the data to the next
DataNode in the Pipeline
When all replicas are written, the Client moves on to
write the next block in file
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73. Read Anatomy
HDFS Client Get Block Name Node
Data Node Data Node Data Node
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Read
Read
74. Data Correctness
Use Checksums to validate data
– Use CRC32
File Creation
– Client computes checksum per 512 byte
– DataNode stores the checksum
File access
– Client retrieves the data and checksum from DataNode
– If Validation fails, Client tries other replicas
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75. Apache Hadoop Bigdata
Training By Design Pathshala
Contact us on: admin@designpathshala.com
Or Call us at: +91 120 260 5512 or +91 98 188 23045
Visit us at: http://designpathshala.com
www.designpathshala.com | +91 120 260 5512 | +91 98 188 23045 | admin@designpathshala.com |
http://designpathshala.com
75