In this session you will learn:
1. History of hadoop
2. Hadoop Ecosystem
3. Hadoop Animal Planet
4. What is Hadoop?
5. Distinctions of hadoop
6. Hadoop Components
7. The Hadoop Distributed Filesystem
8. Design of HDFS
9. When Not to use Hadoop?
10. HDFS Concepts
11. Anatomy of a File Read
12. Anatomy of a File Write
13. Replication & Rack awareness
14. Mapreduce Components
15. Typical Mapreduce Job
In this session you will learn:
1. Meet MapReduce
2. Word Count Algorithm – Traditional approach
3. Traditional approach on a Distributed System
4. Traditional approach – Drawbacks
5. MapReduce Approach
6. Input & Output Forms of a MR program
7. Map, Shuffle & Sort, Reduce Phase
8. WordCount Code walkthrough
9. Workflow & Transformation of Data
10. Input Split & HDFS Block
11. Relation between Split & Block
12. Data locality Optimization
13. Speculative Execution
14. MR Flow with Single Reduce Task
15. MR flow with multiple Reducers
16. Input Format & Hierarchy
17. Output Format & Hierarchy
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
Hadoop is an open source framework for distributed storage and processing of large datasets across commodity hardware. It has two main components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. HDFS stores data across clusters in a redundant and fault-tolerant manner. MapReduce allows distributed processing of large datasets in parallel using map and reduce functions. The architecture aims to provide reliable, scalable computing using commodity hardware.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
Hadoop is an open source framework for running large-scale data processing jobs across clusters of computers. It has two main components: HDFS for reliable storage and Hadoop MapReduce for distributed processing. HDFS stores large files across nodes through replication and uses a master-slave architecture. MapReduce allows users to write map and reduce functions to process large datasets in parallel and generate results. Hadoop has seen widespread adoption for processing massive datasets due to its scalability, reliability and ease of use.
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.
The document provides an introduction to Hadoop, including an overview of its core components HDFS and MapReduce, and motivates their use by explaining the need to process large amounts of data in parallel across clusters of computers in a fault-tolerant and scalable manner. It also presents sample code walkthroughs and discusses the Hadoop ecosystem of related projects like Pig, HBase, Hive and Zookeeper.
In this session you will learn:
1. Meet MapReduce
2. Word Count Algorithm – Traditional approach
3. Traditional approach on a Distributed System
4. Traditional approach – Drawbacks
5. MapReduce Approach
6. Input & Output Forms of a MR program
7. Map, Shuffle & Sort, Reduce Phase
8. WordCount Code walkthrough
9. Workflow & Transformation of Data
10. Input Split & HDFS Block
11. Relation between Split & Block
12. Data locality Optimization
13. Speculative Execution
14. MR Flow with Single Reduce Task
15. MR flow with multiple Reducers
16. Input Format & Hierarchy
17. Output Format & Hierarchy
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
Hadoop is an open source framework for distributed storage and processing of large datasets across commodity hardware. It has two main components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. HDFS stores data across clusters in a redundant and fault-tolerant manner. MapReduce allows distributed processing of large datasets in parallel using map and reduce functions. The architecture aims to provide reliable, scalable computing using commodity hardware.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
Hadoop is an open source framework for running large-scale data processing jobs across clusters of computers. It has two main components: HDFS for reliable storage and Hadoop MapReduce for distributed processing. HDFS stores large files across nodes through replication and uses a master-slave architecture. MapReduce allows users to write map and reduce functions to process large datasets in parallel and generate results. Hadoop has seen widespread adoption for processing massive datasets due to its scalability, reliability and ease of use.
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.
The document provides an introduction to Hadoop, including an overview of its core components HDFS and MapReduce, and motivates their use by explaining the need to process large amounts of data in parallel across clusters of computers in a fault-tolerant and scalable manner. It also presents sample code walkthroughs and discusses the Hadoop ecosystem of related projects like Pig, HBase, Hive and Zookeeper.
This document provides an overview of Hadoop and MapReduce. It discusses how Hadoop uses HDFS for distributed storage and replication of data blocks across commodity servers. It also explains how MapReduce allows for massively parallel processing of large datasets by splitting jobs into mappers and reducers. Mappers process data blocks in parallel and generate intermediate key-value pairs, which are then sorted and grouped by the reducers to produce the final results.
WANdisco is a provider of non-stop software for global enterprises to meet the challenges of Big Data and distributed software development.
KEY HIGHLIGHTS, Session 1: Tuesday, Feb. 26, 5:15 p.m.-6 p.m.
Hadoop and HBase on the Cloud: A Case Study on Performance and Isolation
Cloud infrastructure is a flexible tool to orchestrate multiple Hadoop and HBase clusters, which provides strict isolation of data and compute resources for multiple customers. Most importantly, our benchmarks show that virtualized environment allows for higher average utilization of per-node resources. For more session information, visit http://na.apachecon.com/schedule/presentation/131/.
CO-PRESENTERS, Dr. Konstantin V. Shvachko, Chief Architect, Big Data, WANdisco and Jagane Sundar, CTO/VP Engineering, Big Data, WANdisco
A veteran Hadoop developer and respected author, Konstantin Shvachko is a technical expert specializing in efficient data structures and algorithms for large-scale distributed storage systems. Konstantin joined WANdisco through the AltoStor acquisition and before that he was founder and Chief Scientist at AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. Konstantin played a lead architectural role at eBay, building two generations of the organization's Hadoop platform. At Yahoo!, he worked on the Hadoop Distributed File System (HDFS). Konstantin has dozens of publications and presentations to his credit and is currently a member of the Apache Hadoop PMC. Konstantin has a Ph.D. in Computer Science and M.S. in Mathematics from Moscow State University, Russia.
Jagane Sundar has extensive big data, cloud, virtualization, and networking experience and joined WANdisco through its AltoStor acquisition. Before AltoStor, Jagane was founder and CEO of AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. His experience with Hadoop began as Director of Hadoop Performance and Operability at Yahoo! Jagane has such accomplishments to his credit as the creation of Livebackup, development of a user mode TCP Stack for Precision I/O, development of the NFS and PPP clients and parts of the TCP stack for JavaOS for Sun MicroSystems, and more. Jagane received his B.E. in Electronics and Communications Engineering from Anna University.
About WANdisco
WANdisco ( LSE : WAND ) is a provider of enterprise-ready, non-stop software solutions that enable globally distributed organizations to meet today's data challenges of secure storage, scalability and availability. WANdisco's products are differentiated by the company's patented, active-active data replication technology, serving crucial high availability (HA) requirements, including Hadoop Big Data and Application Lifecycle Management (ALM). Fortune Global 1000 companies including AT&T, Motorola, Intel and Halliburton rely on WANdisco for performance, reliability, security and availability. For additional information, please visit www.wandisco.com.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
Apache hadoop, hdfs and map reduce OverviewNisanth Simon
This document provides an overview of Apache Hadoop, HDFS, and MapReduce. It describes how Hadoop uses a distributed file system (HDFS) to store large amounts of data across commodity hardware. It also explains how MapReduce allows distributed processing of that data by allocating map and reduce tasks across nodes. Key components discussed include the HDFS architecture with NameNodes and DataNodes, data replication for fault tolerance, and how the MapReduce engine works with a JobTracker and TaskTrackers to parallelize jobs.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
This document provides an overview of Hadoop and how it addresses the challenges of big data. It discusses how Hadoop uses a distributed file system (HDFS) and MapReduce programming model to allow processing of large datasets across clusters of computers. Key aspects summarized include how HDFS works using namenodes and datanodes, how MapReduce leverages mappers and reducers to parallelize processing, and how Hadoop provides fault tolerance.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
The document discusses Apache HCatalog, which provides metadata services and a unified view of data across Hadoop tools like Hive, Pig, and MapReduce. It allows sharing of data and metadata between tools and external systems through a consistent schema. HCatalog simplifies data management by allowing tools to access metadata like the schema, location, and format of data from a shared metastore instead of encoding that information within each application.
Apache Drill is an open source SQL query engine for analysis of large scale datasets. It is modeled after Google's Dremel system and allows for interactive analysis of data stored in Hadoop files. Drill uses a columnar data format and execution engine to enable fast query performance on large datasets. It also supports pluggable components for different data formats, query languages, and data sources to provide a flexible and extensible platform.
Distributed Computing with Apache Hadoop is a technology overview that discusses:
1) Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of commodity hardware.
2) Hadoop addresses limitations of traditional distributed computing with an architecture that scales linearly by adding more nodes, moves computation to data instead of moving data, and provides reliability even when hardware failures occur.
3) Core Hadoop components include the Hadoop Distributed File System for storage, and MapReduce for distributed processing of large datasets in parallel on multiple machines.
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This document compares the Google File System (GFS) and the Hadoop Distributed File System (HDFS). It discusses their motivations, architectures, performance measurements, and role in larger systems. GFS was designed for Google's data processing needs, while HDFS was created as an open-source framework for Hadoop applications. Both divide files into blocks and replicate data across multiple servers for reliability. The document provides details on their file structures, data flow models, consistency approaches, and benchmark results. It also explores how systems like MapReduce/Hadoop utilize these underlying storage systems.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
The document discusses Hadoop, its components, and how they work together. It covers HDFS, which stores and manages large files across commodity servers; MapReduce, which processes large datasets in parallel; and other tools like Pig and Hive that provide interfaces for Hadoop. Key points are that Hadoop is designed for large datasets and hardware failures, HDFS replicates data for reliability, and MapReduce moves computation instead of data for efficiency.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
* The file size is 1664MB
* HDFS block size is usually 128MB by default in Hadoop 2.0
* To calculate number of blocks required: File size / Block size
* 1664MB / 128MB = 13 blocks
* 8 blocks have been uploaded successfully
* So remaining blocks = Total blocks - Uploaded blocks = 13 - 8 = 5
If another client tries to access/read the data while the upload is still in progress, it will only be able to access the data from the 8 blocks that have been uploaded so far. The remaining 5 blocks of data will not be available or visible to other clients until the full upload is completed. HDFS follows write-once semantics, so partial
The document provides an introduction to Hadoop and big data concepts. It discusses key topics like what big data is characterized by the three V's of volume, velocity and variety. It then defines Hadoop as a framework for distributed storage and processing of large datasets using commodity hardware. The rest of the document outlines the main components of the Hadoop ecosystem including HDFS, YARN, MapReduce, Hive, Pig, Zookeeper, Flume and Sqoop and provides brief descriptions of each.
This document provides an overview of Apache Hadoop, a framework for storing and processing large datasets in a distributed computing environment. It discusses what big data is and the challenges of working with large datasets. Hadoop addresses these challenges through its two main components: the HDFS distributed file system, which stores data across commodity servers, and MapReduce, a programming model for processing large datasets in parallel. The document outlines the architecture and benefits of Hadoop for scalable, fault-tolerant distributed computing on big data.
This document provides an overview of Hadoop and MapReduce. It discusses how Hadoop uses HDFS for distributed storage and replication of data blocks across commodity servers. It also explains how MapReduce allows for massively parallel processing of large datasets by splitting jobs into mappers and reducers. Mappers process data blocks in parallel and generate intermediate key-value pairs, which are then sorted and grouped by the reducers to produce the final results.
WANdisco is a provider of non-stop software for global enterprises to meet the challenges of Big Data and distributed software development.
KEY HIGHLIGHTS, Session 1: Tuesday, Feb. 26, 5:15 p.m.-6 p.m.
Hadoop and HBase on the Cloud: A Case Study on Performance and Isolation
Cloud infrastructure is a flexible tool to orchestrate multiple Hadoop and HBase clusters, which provides strict isolation of data and compute resources for multiple customers. Most importantly, our benchmarks show that virtualized environment allows for higher average utilization of per-node resources. For more session information, visit http://na.apachecon.com/schedule/presentation/131/.
CO-PRESENTERS, Dr. Konstantin V. Shvachko, Chief Architect, Big Data, WANdisco and Jagane Sundar, CTO/VP Engineering, Big Data, WANdisco
A veteran Hadoop developer and respected author, Konstantin Shvachko is a technical expert specializing in efficient data structures and algorithms for large-scale distributed storage systems. Konstantin joined WANdisco through the AltoStor acquisition and before that he was founder and Chief Scientist at AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. Konstantin played a lead architectural role at eBay, building two generations of the organization's Hadoop platform. At Yahoo!, he worked on the Hadoop Distributed File System (HDFS). Konstantin has dozens of publications and presentations to his credit and is currently a member of the Apache Hadoop PMC. Konstantin has a Ph.D. in Computer Science and M.S. in Mathematics from Moscow State University, Russia.
Jagane Sundar has extensive big data, cloud, virtualization, and networking experience and joined WANdisco through its AltoStor acquisition. Before AltoStor, Jagane was founder and CEO of AltoScale, a Hadoop and HBase-as-a-Platform company acquired by VertiCloud. His experience with Hadoop began as Director of Hadoop Performance and Operability at Yahoo! Jagane has such accomplishments to his credit as the creation of Livebackup, development of a user mode TCP Stack for Precision I/O, development of the NFS and PPP clients and parts of the TCP stack for JavaOS for Sun MicroSystems, and more. Jagane received his B.E. in Electronics and Communications Engineering from Anna University.
About WANdisco
WANdisco ( LSE : WAND ) is a provider of enterprise-ready, non-stop software solutions that enable globally distributed organizations to meet today's data challenges of secure storage, scalability and availability. WANdisco's products are differentiated by the company's patented, active-active data replication technology, serving crucial high availability (HA) requirements, including Hadoop Big Data and Application Lifecycle Management (ALM). Fortune Global 1000 companies including AT&T, Motorola, Intel and Halliburton rely on WANdisco for performance, reliability, security and availability. For additional information, please visit www.wandisco.com.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
Apache hadoop, hdfs and map reduce OverviewNisanth Simon
This document provides an overview of Apache Hadoop, HDFS, and MapReduce. It describes how Hadoop uses a distributed file system (HDFS) to store large amounts of data across commodity hardware. It also explains how MapReduce allows distributed processing of that data by allocating map and reduce tasks across nodes. Key components discussed include the HDFS architecture with NameNodes and DataNodes, data replication for fault tolerance, and how the MapReduce engine works with a JobTracker and TaskTrackers to parallelize jobs.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
This document provides an overview of Hadoop and how it addresses the challenges of big data. It discusses how Hadoop uses a distributed file system (HDFS) and MapReduce programming model to allow processing of large datasets across clusters of computers. Key aspects summarized include how HDFS works using namenodes and datanodes, how MapReduce leverages mappers and reducers to parallelize processing, and how Hadoop provides fault tolerance.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
The document discusses Apache HCatalog, which provides metadata services and a unified view of data across Hadoop tools like Hive, Pig, and MapReduce. It allows sharing of data and metadata between tools and external systems through a consistent schema. HCatalog simplifies data management by allowing tools to access metadata like the schema, location, and format of data from a shared metastore instead of encoding that information within each application.
Apache Drill is an open source SQL query engine for analysis of large scale datasets. It is modeled after Google's Dremel system and allows for interactive analysis of data stored in Hadoop files. Drill uses a columnar data format and execution engine to enable fast query performance on large datasets. It also supports pluggable components for different data formats, query languages, and data sources to provide a flexible and extensible platform.
Distributed Computing with Apache Hadoop is a technology overview that discusses:
1) Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of commodity hardware.
2) Hadoop addresses limitations of traditional distributed computing with an architecture that scales linearly by adding more nodes, moves computation to data instead of moving data, and provides reliability even when hardware failures occur.
3) Core Hadoop components include the Hadoop Distributed File System for storage, and MapReduce for distributed processing of large datasets in parallel on multiple machines.
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This document compares the Google File System (GFS) and the Hadoop Distributed File System (HDFS). It discusses their motivations, architectures, performance measurements, and role in larger systems. GFS was designed for Google's data processing needs, while HDFS was created as an open-source framework for Hadoop applications. Both divide files into blocks and replicate data across multiple servers for reliability. The document provides details on their file structures, data flow models, consistency approaches, and benchmark results. It also explores how systems like MapReduce/Hadoop utilize these underlying storage systems.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
The document discusses Hadoop, its components, and how they work together. It covers HDFS, which stores and manages large files across commodity servers; MapReduce, which processes large datasets in parallel; and other tools like Pig and Hive that provide interfaces for Hadoop. Key points are that Hadoop is designed for large datasets and hardware failures, HDFS replicates data for reliability, and MapReduce moves computation instead of data for efficiency.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
* The file size is 1664MB
* HDFS block size is usually 128MB by default in Hadoop 2.0
* To calculate number of blocks required: File size / Block size
* 1664MB / 128MB = 13 blocks
* 8 blocks have been uploaded successfully
* So remaining blocks = Total blocks - Uploaded blocks = 13 - 8 = 5
If another client tries to access/read the data while the upload is still in progress, it will only be able to access the data from the 8 blocks that have been uploaded so far. The remaining 5 blocks of data will not be available or visible to other clients until the full upload is completed. HDFS follows write-once semantics, so partial
The document provides an introduction to Hadoop and big data concepts. It discusses key topics like what big data is characterized by the three V's of volume, velocity and variety. It then defines Hadoop as a framework for distributed storage and processing of large datasets using commodity hardware. The rest of the document outlines the main components of the Hadoop ecosystem including HDFS, YARN, MapReduce, Hive, Pig, Zookeeper, Flume and Sqoop and provides brief descriptions of each.
This document provides an overview of Apache Hadoop, a framework for storing and processing large datasets in a distributed computing environment. It discusses what big data is and the challenges of working with large datasets. Hadoop addresses these challenges through its two main components: the HDFS distributed file system, which stores data across commodity servers, and MapReduce, a programming model for processing large datasets in parallel. The document outlines the architecture and benefits of Hadoop for scalable, fault-tolerant distributed computing on big data.
This document provides information about Hadoop and its components. It discusses the history of Hadoop and how it has evolved over time. It describes key Hadoop components including HDFS, MapReduce, YARN, and HBase. HDFS is the distributed file system of Hadoop that stores and manages large datasets across clusters. MapReduce is a programming model used for processing large datasets in parallel. YARN is the cluster resource manager that allocates resources to applications. HBase is the Hadoop database that provides real-time random data access.
The document provides an overview of Apache Hadoop and how it addresses challenges related to big data. It discusses how Hadoop uses HDFS to distribute and store large datasets across clusters of commodity servers and uses MapReduce as a programming model to process and analyze the data in parallel. The core components of Hadoop - HDFS for storage and MapReduce for processing - allow it to efficiently handle large volumes and varieties of data across distributed systems in a fault-tolerant manner. Major companies have adopted Hadoop to derive insights from their big data.
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This document provides an overview of Hadoop, including:
1. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware.
2. The two main components of Hadoop are HDFS, the distributed file system that stores data reliably across nodes, and MapReduce, which splits tasks across nodes to process data stored in HDFS in parallel.
3. HDFS scales out storage and has a master-slave architecture with a NameNode that manages file system metadata and DataNodes that store data blocks. MapReduce similarly scales out processing via a master JobTracker and slave TaskTrackers.
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for scalable, fault-tolerant storage and MapReduce for parallel processing. The core components of Hadoop - HDFS and MapReduce - allow for distributed processing of large datasets across commodity hardware, providing capabilities for scalability, cost-effectiveness, and efficient distributed computing.
This document discusses big data and Hadoop. It defines big data as large amounts of unstructured data that would be too costly to store and analyze in a traditional database. It then describes how Hadoop provides a solution to this challenge through distributed and parallel processing across clusters of commodity hardware. Key aspects of Hadoop covered include HDFS for reliable storage, MapReduce for distributed computing, and how together they allow scalable analysis of very large datasets. Popular users of Hadoop like Amazon, Yahoo and Facebook are also mentioned.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created in 2005 and is designed to reliably handle large volumes of data and complex computations in a distributed fashion. The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing data in parallel across large clusters of computers. It is widely adopted by companies handling big data like Yahoo, Facebook, Amazon and Netflix.
Apache Hadoop is an open-source software framework that supports distributed applications and processing of large data sets across clusters of commodity hardware. It is highly scalable, fault-tolerant and allows processing of data in parallel. Hadoop consists of Hadoop Common, HDFS for storage, YARN for resource management and MapReduce for distributed processing. HDFS stores large files across clusters and provides high throughput access to application data. MapReduce allows distributed processing of large datasets across clusters using a simple programming model.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It has four main modules - Hadoop Common, HDFS, YARN and MapReduce. HDFS provides a distributed file system that stores data reliably across commodity hardware. MapReduce is a programming model used to process large amounts of data in parallel. Hadoop architecture uses a master-slave model, with a NameNode master and DataNode slaves. It provides fault tolerance, high throughput access to application data and scales to thousands of machines.
The document provides an overview of big data and Hadoop fundamentals. It discusses what big data is, the characteristics of big data, and how it differs from traditional data processing approaches. It then describes the key components of Hadoop including HDFS for distributed storage, MapReduce for distributed processing, and YARN for resource management. HDFS architecture and features are explained in more detail. MapReduce tasks, stages, and an example word count job are also covered. The document concludes with a discussion of Hive, including its use as a data warehouse infrastructure on Hadoop and its query language HiveQL.
This document provides an overview and introduction to Hadoop, HDFS, and MapReduce. It covers the basic concepts of HDFS, including how files are stored in blocks across data nodes, and the role of the name node and data nodes. It also explains the MapReduce programming model, including the mapper, reducer, and how jobs are split into parallel tasks. The document discusses using Hadoop from the command line and writing MapReduce jobs in Java. It also mentions some other projects in the Hadoop ecosystem like Pig, Hive, HBase and Zookeeper.
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.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Dr. Sean Tan, Head of Data Science, Changi Airport Group
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GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
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Agenda
• History of hadoop
• Hadoop Ecosystem
• Hadoop Animal Planet
• What is Hadoop?
• Distinctions of hadoop
• Hadoop Components
• The Hadoop Distributed Filesystem
• Design of HDFS
• When Not to use Hadoop?
• HDFS Concepts
• Anatomy of a File Read
• Anatomy of a File Write
• Replication & Rack awareness
• Mapreduce Components
• Typical Mapreduce Job
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Hadoop Ecosystem
• Hadoop Distributed File System (HDFS) is a Java-based file system that
provides scalable and reliable data storage that is designed to span large
clusters of commodity servers. HDFS, MapReduce and YARN form the core of
Apache™ Hadoop
• MapReduce is a programming model and it provides implementation for
processing and generating large data sets with a parallel, distributed
algorithm on a cluster.
• Apache Hadoop YARN (short, in self-deprecating fashion, for Yet Another
Resource Negotiator) is a cluster management technology. It is one of the key
features in second-generation Hadoop.
• Apache Hive is a data warehouse infrastructure built on top of Hadoop for
providing data summarization, query, and analysis. While initially developed
by Facebook, Apache Hive is now used and developed by other companies
such as Netflix.
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Hadoop Ecosystem
• Apache Pig is a platform for analyzing large data sets that consists of a high-
level language for expressing data analysis programs, coupled with
infrastructure for evaluating these programs.
• Apache HBase is an open-source, distributed, versioned, non-relational
database modeled after Google's Bigtable: A Distributed Storage System for
Structured Data by Chang et al.
• ZooKeeper is a centralized service for maintaining configuration information,
naming, providing distributed synchronization, and providing group services.
All of these kinds of services are used in some form or another by distributed
applications.
• Apache Sqoop is a tool designed for efficiently transferring bulk data
between Apache Hadoop and structured datastores such as relational
databases. Sqoop imports data from external structured datastores into
HDFS or related systems like Hive and HBase.
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Hadoop Ecosystem
• Flume is a distributed, reliable, and available service for efficiently collecting,
aggregating, and moving large amounts of log data. It has a simple and
flexible architecture based on streaming data flows. It is robust and fault
tolerant with tunable reliability mechanisms and many failover and recovery
mechanisms.
• Machine learning is a type of artificial intelligence (AI) that provides
computers with the ability to learn without being explicitly programmed.
Machine learning focuses on the development of computer programs that
can teach themselves to grow and change when exposed to new data.
• R is a programming language and software environment for statistical
computing and graphics. The R language is widely used among statisticians
and data miners for developing statistical software and data analysis
• Cloudera Impala is Cloudera's open source massively parallel processing
(MPP) SQL query engine for data stored in a computer cluster
running Apache Hadoop.
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Hadoop Ecosystem
• MongoDB is a document database that provides high performance, high
availability, and easy scalability. Document Database. Documents (objects)
map nicely to programming language data types. Embedded documents and
arrays reduce need for joins. Dynamic schema makes polymorphism easier.
• Apache CouchDB, commonly referred to as CouchDB, is an open
source database that focuses on ease of use and on being "a databasethat
completely embraces the web". It is a document-oriented
NoSQLdatabase that uses JSON to store data, JavaScript as its query
language using MapReduce, and HTTP for an API.
• Cascading is a software abstraction layer for Apache Hadoop. Cascading is
used to create and execute complex data processing workflows on a Hadoop
cluster using any JVM-based language (Java, JRuby, Clojure, etc.), hiding the
underlying complexity of MapReduce jobs.
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What is Hadoop?
• The Apache Hadoop software library is a framework that allows for the
distributed processing of large data sets across clusters of computers using
simple programming models
• It is designed to scale up from single servers to thousands of machines, each
offering local computation and storage.
• Rather than rely on hardware to deliver high-availability, the
library itself is designed to detect and handle failures at the application layer,
so delivering a highly-available service on top of a cluster of computers, each
of which may be prone to failures.
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Hadoop Components
MapReduc
e
HDFS
Cluster
Job
Tracker
Namenod
e
Task
Tracke
r
Task
Tracke
r
Task
Tracke
r
Data
Node
Data
Node
Data
Node
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Hadoop Components:
• HDFS – Hadoop Distributed File System(storage):
• Data is split and distributed across nodes
• Each split is replicated
• Namenode is the master & Datanodes are the slaves
• Mapreduce(processing):
• Splits a task across processors
• Execution is Near the data & the results are merged
• Self-healing
• JobTracker is the master & Task trackers are slaves
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The Hadoop Distributed Filesystem:
• When a dataset is larger than the storage capacity of a single physical
machine, it is necessary to split it and save it in a number of separate
machines. Filesystems that manage the storage across a network of
machines are called distributed filesystems. One of the biggest challenges
of distributed filesystems is handling node (machine) failure without
suffering data loss.
• Hadoop comes with a distributed filesystem called HDFS, which
stands for Hadoop Distributed Filesystem. HDFS overcomes this
challenge.
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Design Of HDFS:
• HDFS is designed for storing very large files with streaming data access
patterns, running on clusters of commodity hardware.
• Very Large Files: Files in the sizes of hundreds of gigabytes (GB) or
terabytes (TB)
• Streaming Data Access: HDFS is built around the idea that the most
efficient data processing pattern is a write-once, read-many-times
pattern. A dataset is typically generated or copied from source, and then
various analyses are performed on that dataset over time.
• Commodity Hardware: Commonly available hardware which is cheap
(not enterprise level)
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When Not to Use Hadoop?
• HDFS does not fit
• When you need low-latency(faster) access to data: Applications that
require faster data access will not work well with HDFS. HDFS is
optimized for high throughput of data.
• When you have lots of small files: Namenode holds the filesystem
metadata in memory. Every block, file, directory occupies around
150bytes. Hence having large number of small files will cause burden on
the Namenode
• When random writes in the file is needed: Files in the HDFS may be
written to by a single writer. Writes are always made at the end-of file
in append-only fashion. There is no support for multiple writers or for
modifications at random offsets(positions)
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HDFS Concepts
• HDFS Block: A HDFS block is the smallest unit of data that we can read or
write in HDFS. HDFS stores files in blocks that are typically at least 64 MB or
(more commonly now) 128 MB in size, much larger than the 4-32 KB seen in
most filesystems.
• In HDFS, 1 MB file stored with a block size of 128 MB uses 1 MB of disk
space, not 128 MB
Why is a block in HDFS so large?
• The Disk seek time and the data transfer rate are not at the same level
(data transfer is much faster). As of now, the disk seek time is 10 ms and
the transfer rate is 100MB/s. To keep them in sync with each other, the
data block was chosen to be large (which results in longer transfer time)
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HDFS Concepts:
• NameNode
• It is the master node & responsible for the entire cluster
• Manages the filesystem namespace
• Enterprise level software is used
• DataNode
• Slaves which run on commodity/cheap hardware
• Store and retrieve data when they are told to (by client or Namenode)
• Sends heart-beat signals to NN with the blocks that they store
• Secondary NameNode
• It is a backup for the Namenode(not a hot stand-by)
• It periodically merges the fsimage & edit log files
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Anatomy of a File Read
1. The client opens the file it wishes to read by calling open() on
the FileSystem object, which for HDFS is an instance of DistributedFileSystem
2. DistributedFileSystem calls the namenode, using RPC, to determine the
locations of the blocks for the first few blocks in the file
3. For each block, the namenode returns the addresses of the datanodes that
have a copy of that block. Furthermore, the datanodes are sorted according
to their proximity to the client. The DistributedFileSystem returns
an FSDataInputStream (an input stream that supports file seeks) to the client
for it to read data from. FSDataInputStream in turn wraps a DFSInputStream,
which manages the datanode and namenode I/O. The client then
calls read() on the stream.
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Anatomy of a File Read
4. DFSInputStream, which has stored the datanode addresses for the first
few blocks in the file, then connects to the first (closest) datanode for the
first block in the file. Data is streamed from the datanode back to the
client, which calls read() repeatedly on the stream
5. When the end of the block is reached, DFSInputStream will close the
connection to the datanode, then find the best datanode for the next
block
6. Blocks are read in order, with the DFSInputStream opening new
connections to datanodes as the client reads through the stream. It will
also call the namenode to retrieve the datanode locations for the next
batch of blocks as needed. When the client has finished reading, it
calls close() on the FSDataInputStream
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Anatomy of a File Write
1. The client creates the file by calling create() on DistributedFileSystem
2. DistributedFileSystem makes an RPC call to the namenode to create a new
file in the filesystem’s namespace, with no blocks associated with it. NN
checks for client’s access permissions and checks if the file already exists (file
exists - IO Exception); The DistributedFileSystem returns an
FSDataOutputStream for the client to start writing data to.
FSDataOutputStream wraps a DFSOutputStream, which handles
communication with the datanodes and namenode.
3. As the client writes data (step 3), DFSOutputStream splits it into packets,
which it writes to an internal queue, called the data queue. The data queue is
consumed by the DataStreamer, which is responsible for asking the
namenode to allocate new blocks by picking a list of suitable datanodes to
store the replicas. The list of datanodes forms a pipeline
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Anatomy of a File Write
4. The DataStreamer streams the packets to the first datanode in the
pipeline, which stores the packet and forwards it to the second
datanode in the pipeline. Similarly, the second datanode stores the
packet and forwards it to the third (and last) datanode in the pipeline
5. DFSOutputStream also maintains an internal queue of packets that
are waiting to be acknowledged by datanodes, called the ack queue.
A packet is removed from the ack queue only when it has been
acknowledged by all the datanodes in the pipeline (step 5).
6. When the client has finished writing data, it calls close() on the
stream waits for acknowledgments before contacting the namenode
to signal that the file is complete
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MapReduce Components:
• Job Tracker:
• Coordinates all the jobs run on the system by scheduling tasks
• Keeps a record of overall progress of each job
• If a job fails, reschedules the job on a different tasktracker
• Task Tracker:
• Slave daemon which accepts tasks to be run a block of data
• Sends progress reports as heart beat signals to the Job tracker at regular
intervals