Hadoop introduction
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Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through its Hadoop Distributed File System (HDFS) and allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop was created by Doug Cutting and Mike Cafarella to address the growing need to handle large datasets in a distributed computing environment.
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Big Data and Hadoop Ecosystem
Big data and Hadoop are introduced as ways to handle the increasing volume, variety, and velocity of data. Hadoop evolved as a solution to process large amounts of unstructured and semi-structured data across distributed systems in a cost-effective way using commodity hardware. It provides scalable and parallel processing via MapReduce and HDFS distributed file system that stores data across clusters and provides redundancy and failover. Key Hadoop projects include HDFS, MapReduce, HBase, Hive, Pig and Zookeeper.
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This document discusses big data and the Hadoop platform. It provides an overview of Hadoop versions 1 and 2. Hadoop version 1 included the Hadoop Distributed File System (HDFS) for large-scale storage and the MapReduce programming model. Hadoop version 2 introduced YARN to address MapReduce limitations like scalability and availability. The document also briefly describes the Hadoop ecosystem of related projects like Hive for data warehousing and Pig for data flows. It provides examples of Twitter and Facebook's use of Hadoop for big data analytics.
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The document discusses various Hadoop technologies including HDFS, MapReduce, Pig/Hive, HBase, Flume, Oozie, Zookeeper, and HBase. HDFS provides reliable storage across multiple machines by replicating data on different nodes. MapReduce is a framework for processing large datasets in parallel. Pig and Hive provide high-level languages for analyzing data stored in Hadoop. Flume collects log data as it is generated. Oozie manages Hadoop jobs. Zookeeper allows distributed coordination. HBase provides a fault-tolerant way to store large amounts of sparse data.
Introduction to Big Data & Hadoop Architecture - Module 1
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
Hadoop
This document provides an overview of Hadoop, an open source framework for distributed storage and processing of large datasets. It discusses what Hadoop is, its applications and architecture, advantages like scalability and fault tolerance, and disadvantages such as security concerns. The document also outlines when Hadoop should be used, such as for large datasets that don't fit on a single machine or for extracting, transforming and loading large amounts of data. Key components of Hadoop include MapReduce, HDFS, YARN and its wider ecosystem of related projects.
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The most well known technology used for Big Data is Hadoop.
It is actually a large scale batch data processing system
Hadoop Architecture
Hadoop 2.0 architecture uses a scale-out storage and distributed processing framework. It stores large datasets across commodity hardware clusters and allows for processing using a simple programming model. The architecture utilizes HDFS for storage which splits files into 128MB blocks, stores replicas across racks for fault tolerance, and is managed by a resource manager and node managers that track hardware resources and heartbeats.
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This document discusses Facebook's use of Hadoop and Hive for storing and analyzing large amounts of user-generated data. Key points include:
- Facebook stores petabytes of user data including statuses, photos, videos in its Hadoop/Hive warehouse and other Hadoop clusters.
- The data is used for business intelligence to inform strategies and decisions, and power artificial intelligence like recommendations and ads optimization.
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Big Data and Hadoop Ecosystem
Big data and Hadoop are introduced as ways to handle the increasing volume, variety, and velocity of data. Hadoop evolved as a solution to process large amounts of unstructured and semi-structured data across distributed systems in a cost-effective way using commodity hardware. It provides scalable and parallel processing via MapReduce and HDFS distributed file system that stores data across clusters and provides redundancy and failover. Key Hadoop projects include HDFS, MapReduce, HBase, Hive, Pig and Zookeeper.
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Hadoop Technologies
The document discusses various Hadoop technologies including HDFS, MapReduce, Pig/Hive, HBase, Flume, Oozie, Zookeeper, and HBase. HDFS provides reliable storage across multiple machines by replicating data on different nodes. MapReduce is a framework for processing large datasets in parallel. Pig and Hive provide high-level languages for analyzing data stored in Hadoop. Flume collects log data as it is generated. Oozie manages Hadoop jobs. Zookeeper allows distributed coordination. HBase provides a fault-tolerant way to store large amounts of sparse data.
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Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
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This document provides an overview of Hadoop, an open source framework for distributed storage and processing of large datasets. It discusses what Hadoop is, its applications and architecture, advantages like scalability and fault tolerance, and disadvantages such as security concerns. The document also outlines when Hadoop should be used, such as for large datasets that don't fit on a single machine or for extracting, transforming and loading large amounts of data. Key components of Hadoop include MapReduce, HDFS, YARN and its wider ecosystem of related projects.
HADOOP TECHNOLOGY ppt
The most well known technology used for Big Data is Hadoop.
It is actually a large scale batch data processing system
Hadoop Architecture
Hadoop 2.0 architecture uses a scale-out storage and distributed processing framework. It stores large datasets across commodity hardware clusters and allows for processing using a simple programming model. The architecture utilizes HDFS for storage which splits files into 128MB blocks, stores replicas across racks for fault tolerance, and is managed by a resource manager and node managers that track hardware resources and heartbeats.
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This document discusses Facebook's use of Hadoop and Hive for storing and analyzing large amounts of user-generated data. Key points include:
- Facebook stores petabytes of user data including statuses, photos, videos in its Hadoop/Hive warehouse and other Hadoop clusters.
- The data is used for business intelligence to inform strategies and decisions, and power artificial intelligence like recommendations and ads optimization.
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A document describes a multi-node Hadoop cluster used for processing large amounts of data. The cluster includes multiple nodes with RAM and HDD storage. It uses a primary and secondary name node to pull transaction logs, merge changes, and store data to HDD. Hadoop provides a cost effective and reliable solution for processing large datasets across standard servers in a parallel and scalable way.
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The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and additional tools like Hive, Pig, HBase, Zookeeper, Flume, Sqoop and Oozie that make up its ecosystem. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
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Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses problems with traditional systems like data growth, network/server failures, and high costs by allowing data to be stored in a distributed manner and processed in parallel. Hadoop has two main components - the Hadoop Distributed File System (HDFS) which provides high-throughput access to application data across servers, and the MapReduce programming model which processes large amounts of data in parallel by splitting work into map and reduce tasks.
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This document provides information about J.Ayeesha Parveen, her class details, and incharge staff. It then summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets. Key aspects of Hadoop include its distributed file system (HDFS), MapReduce processing model, and various components like NameNode, DataNodes, JobTracker, and TaskTracker. Common uses of Hadoop include analytics of audio, video, and log files.
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Economical
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This document provides an overview of Hadoop and big data concepts. It discusses key characteristics of big data including volume, velocity, variety, and value. It then describes Hadoop distributed file system (HDFS) and MapReduce framework which are core components of Hadoop that allow distributed storage and processing of large datasets across clusters of commodity hardware. Finally, it outlines several common use cases where Hadoop can be applied such as customer churn analysis, recommendation engines, and network data analysis.
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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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Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers. It was created in 2005 by Doug Cutting and Mike Carafella at Yahoo!, with Cutting naming it after his son's toy elephant. Hadoop features include reliable data storage with the Hadoop Distributed File System (HDFS), and its MapReduce programming model for large-scale data processing using a distributed algorithm on a computing cluster.
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Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in big data by providing reliability, scalability, and fault tolerance. Hadoop allows distributed processing of large datasets across clusters using MapReduce and can scale from single servers to thousands of machines, each offering local computation and storage. It is widely used for applications such as log analysis, data warehousing, and web indexing.
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
YouTube Link: https://youtu.be/ll_O9JsjwT4
** Big Data Hadoop Certification Training - https://www.edureka.co/big-data-hadoop-training-certification **
This Edureka PPT on "Hadoop components" will provide you with detailed knowledge about the top Hadoop Components and it will help you understand the different categories of Hadoop Components. This PPT covers the following topics:
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Core Components of Hadoop
Hadoop Architecture
Hadoop EcoSystem
Hadoop Components in Data Storage
General Purpose Execution Engines
Hadoop Components in Database Management
Hadoop Components in Data Abstraction
Hadoop Components in Real-time Data Streaming
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Hadoop Components in Machine Learning
Hadoop Cluster Management tools
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It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
Hadoop Maharajathi,II-M.sc.,Computer Science,Bonsecours college for women
This document provides an overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across commodity hardware. It discusses Hadoop's history and goals, describes its core architectural components including HDFS, MapReduce and their roles, and gives examples of how Hadoop is used at large companies to handle big data.
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This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
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It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Introduction to Apache Hive
Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. It presents a SQL-like interface for querying data stored in various databases and file systems that integrate with Hadoop. The document provides links to Hive documentation, tutorials, presentations and other resources for learning about and using Hive. It also includes a table describing common Hive CLI commands and their usage.
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A document describes a multi-node Hadoop cluster used for processing large amounts of data. The cluster includes multiple nodes with RAM and HDD storage. It uses a primary and secondary name node to pull transaction logs, merge changes, and store data to HDD. Hadoop provides a cost effective and reliable solution for processing large datasets across standard servers in a parallel and scalable way.
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Hadoop is one of the booming and innovative data analytics technology which can effectively handle Big Data problems and achieve the data security. It is an open source and trending technology which involves in data collection, data processing and data analytics using HDFS (Hadoop Distributed File System) and MapReduce algorithms.
Hadoop And Their Ecosystem
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and additional tools like Hive, Pig, HBase, Zookeeper, Flume, Sqoop and Oozie that make up its ecosystem. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
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Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses problems with traditional systems like data growth, network/server failures, and high costs by allowing data to be stored in a distributed manner and processed in parallel. Hadoop has two main components - the Hadoop Distributed File System (HDFS) which provides high-throughput access to application data across servers, and the MapReduce programming model which processes large amounts of data in parallel by splitting work into map and reduce tasks.
Hadoop ecosystem J.AYEESHA PARVEEN II-M.SC.,COMPUTER SCIENCE, BON SECOURS CO...
This document provides information about J.Ayeesha Parveen, her class details, and incharge staff. It then summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets. Key aspects of Hadoop include its distributed file system (HDFS), MapReduce processing model, and various components like NameNode, DataNodes, JobTracker, and TaskTracker. Common uses of Hadoop include analytics of audio, video, and log files.
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Hadoop ecosystem; J.Ayeesha parveen 2 nd M.sc., computer science Bon Secours...
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Distributed processing
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Scalable
Reliable
Fault-tolerant
Economical
Flexible
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This document provides an overview of Hadoop and big data concepts. It discusses key characteristics of big data including volume, velocity, variety, and value. It then describes Hadoop distributed file system (HDFS) and MapReduce framework which are core components of Hadoop that allow distributed storage and processing of large datasets across clusters of commodity hardware. Finally, it outlines several common use cases where Hadoop can be applied such as customer churn analysis, recommendation engines, and network data analysis.
Big data and Hadoop
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.
Anju
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.
Hadoop
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers. It was created in 2005 by Doug Cutting and Mike Carafella at Yahoo!, with Cutting naming it after his son's toy elephant. Hadoop features include reliable data storage with the Hadoop Distributed File System (HDFS), and its MapReduce programming model for large-scale data processing using a distributed algorithm on a computing cluster.
Hadoop and Distributed Computing
This document discusses distributed computing and Hadoop. It begins by explaining distributed computing and how it divides programs across several computers. It then introduces Hadoop, an open-source Java framework for distributed processing of large data sets across clusters of computers. Key aspects of Hadoop include its scalable distributed file system (HDFS), MapReduce programming model, and ability to reliably process petabytes of data on thousands of nodes. Common use cases and challenges of using Hadoop are also outlined.
Hadoop overview
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in big data by providing reliability, scalability, and fault tolerance. Hadoop allows distributed processing of large datasets across clusters using MapReduce and can scale from single servers to thousands of machines, each offering local computation and storage. It is widely used for applications such as log analysis, data warehousing, and web indexing.
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
YouTube Link: https://youtu.be/ll_O9JsjwT4
** Big Data Hadoop Certification Training - https://www.edureka.co/big-data-hadoop-training-certification **
This Edureka PPT on "Hadoop components" will provide you with detailed knowledge about the top Hadoop Components and it will help you understand the different categories of Hadoop Components. This PPT covers the following topics:
What is Hadoop?
Core Components of Hadoop
Hadoop Architecture
Hadoop EcoSystem
Hadoop Components in Data Storage
General Purpose Execution Engines
Hadoop Components in Database Management
Hadoop Components in Data Abstraction
Hadoop Components in Real-time Data Streaming
Hadoop Components in Graph Processing
Hadoop Components in Machine Learning
Hadoop Cluster Management tools
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Big data and tools
It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
Hadoop Maharajathi,II-M.sc.,Computer Science,Bonsecours college for women
This document provides an overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets across commodity hardware. It discusses Hadoop's history and goals, describes its core architectural components including HDFS, MapReduce and their roles, and gives examples of how Hadoop is used at large companies to handle big data.
Introduction to Hadoop Technology
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
Apache Hadoop at 10
This document summarizes the history and evolution of Apache Hadoop over the past 10 years. It discusses how Hadoop originated from Doug Cutting's work on Nutch in 2002. It grew to include HDFS for storage and MapReduce for processing. Yahoo was an early large-scale user. The community has expanded Hadoop to include over 25 components like Hive, HBase, Spark and more. The open source model and ability to adapt have helped Hadoop succeed and it will continue to evolve to handle new data sources and cloud deployments in the next 10 years.
Bn1028 demo hadoop administration and development
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.
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Hadoop ecosystem J.AYEESHA PARVEEN II-M.SC.,COMPUTER SCIENCE, BON SECOURS CO...
Hadoop ecosystem J.AYEESHA PARVEEN II-M.SC.,COMPUTER SCIENCE, BON SECOURS CO...
Hadoop ecosystem; J.Ayeesha parveen 2 nd M.sc., computer science Bon Secours...
Hadoop ecosystem; J.Ayeesha parveen 2 nd M.sc., computer science Bon Secours...
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
Hadoop Maharajathi,II-M.sc.,Computer Science,Bonsecours college for women
Hadoop Maharajathi,II-M.sc.,Computer Science,Bonsecours college for women
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At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
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What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
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What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
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Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
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Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
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Hive on spark is blazing fast or is it final
This presentation was given at the Strata + Hadoop World, 2015 in San Jose.
Apache Hive is the most popular and most widely used SQL solution for Hadoop. To keep pace with Hadoop’s increasingly vital role in the Enterprise, Hive has transformed from a batch-only, high-latency system into a modern SQL engine capable of both batch and interactive queries over large datasets. Hive’s momentum is accelerating: With Spark integration and a shift to in-memory processing on the horizon, Hive continues to expand the boundaries of Big Data.
In this talk the speakers examined Hive performance, past, present and future. In particular they looked at Hive’s origins as a petabyte scale SQL engine.
Through some numbers and graphs, they showed how Hive became 100x faster by moving beyond MapReduce, by vectorizing execution and by introducing a cost-based optimizer.
They detailed and discussed the challenges of scalable SQL on Hadoop.
The looked into Hive’s sub-second future, powered by LLAP and Hive on Spark.
And showed just how fast Hive on Spark really is.
Integration of HIve and HBase
This document discusses integrating Apache Hive and HBase. It provides an overview of Hive and HBase, describes use cases for querying HBase data using Hive SQL, and outlines features and improvements for Hive and HBase integration. Key points include mapping Hive schemas and data types to HBase tables and columns, pushing filters and other operations down to HBase, and using a storage handler to interface between Hive and HBase. The integration allows analysts to query both structured Hive and unstructured HBase data using a single SQL interface.
Hadoop Hive Tutorial | Hive Fundamentals | Hive Architecture
This Hadoop Hive Tutorial will unravel the complete Introduction to Hive, Hive Architecture, Hive Commands, Hive Fundamentals & HiveQL. In addition to this, even fundamental concepts of BIG Data & Hadoop are extensively covered.
At the end, you'll have a strong knowledge regarding Hadoop Hive Basics.
PPT Agenda
✓ Introduction to BIG Data & Hadoop
✓ What is Hive?
✓ Hive Data Flows
✓ Hive Programming
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What is Apache Hive?
Apache Hive is a data warehousing infrastructure built over Hadoop which is targeted towards SQL programmers. Hive permits SQL programmers to directly enter the Hadoop ecosystem without any pre-requisites in Java or other programming languages. HiveQL is similar to SQL, it is utilized to process Hadoop & MapReduce operations by managing & querying data.
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Hive has the following 5 Components:
1. Driver
2. Compiler
3. Shell
4. Metastore
5. Execution Engine
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Applications of Hive
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
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Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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- 2. Background ● Big data challenge - (link)
- 3. User Case - Retails ● 知己知彼，百戰不殆 – Retail and The Big Data Evolution:
- 4. User Case - Retails ● Data is providing gains in three main ways: opening new channels, tailoring my service, and driving revenue:
- 5. Pain Point ● Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
- 6. So what is Hadoop? ● Hadoop was created by Doug Cutting and Mike Cafarella. ● Hadoop provides the reliable shared storage and analysis system. (HDFS) ● It is designed to scale up from a single server to thousand of machines, with a high degree of fault tolerance. (HDFS) ● It provides a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. (MapReduce)
- 7. History
- 8. Hadoop Distributed FileSystem (HDFS) ● A given file is broken down into blocks (default=64MB), then blocks are replicated across cluster (default=3). ● The number and size of file have no limition. ● HDFS allows you to put/get/delete files. (No update!) ● Follows the philosophy – "Write Once, Read Multiple Times!"
- 10. Hadoop Ecosystem ● The Hadoop Ecosystem Table
- 11. Flume ● Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. ● Each Flume agent has a source, a sink and a channel
- 12. Sqoop ● Apache Sqoop(TM) is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.
- 13. Hive ● Apache Hive is a high-level abstraction on top of MapReduce ● Uses an SQL-like language called HiveQL ● Generates MapReduce jobs that run on the Hadoop cluster ● Originally developed by Facebook for data warehousing - Now an open-source Apache project.
- 14. HBase ● Apache HBase™ is the Hadoop database, a distributed, scalable, big data store. ● Linear scalability, capabile of storing hundreds of terabytes of data ● Automatic and configurable sharding of tables ● Automatic failover support ● Block cache and Bloom Filters for real-time queries ● Provides realtime random read/write access to data stored in HDFS.
- 15. Pig ● 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. ● The data flow language (Pig Latin) ● The interactive shell where you can type Pig Latin statements (Grunt) ● The Pig interpreter and execution engine
- 16. Oozie ● Oozie is a 'workflow engine' which runs on a server and typically outside the cluster. It can runs workflows of Hadoop jobs including Pig, Hive, Sqoop jobs and submit those jobs to the cluster based on a workflow definition.
- 17. Why?
- 18. Why?
- 19. Appendix ● What is Hadoop? ● https://www.youtube.com/watch?v=4DgTLaFNQq0 ● https://www.youtube.com/watch?v=9s-vSeWej1U ● Intro to MapReduce ● https://www.youtube.com/watch?v=HFplUBeBhcM ● What is Big Data? Big Data Explained (Hadoop & MapReduce) ● Big Data University - Hadoop Fundamentals I - v2 ● Big Data Challenges
- 20. Appendix ● Hadoop Tutorial 1 - What is Hadoop? ● Hadoop Tutorial 2 - Challenges Created by Big Data ● Hadoop Tutorial 3 - History Behind Creation of Hadoop (Google, Yahoo, and Apac ● Hadoop Tutorial 4 - Overview of Hadoop Projects ● Hadoop Tutorial 5 - Steps to Install Hadoop on a Personal Computer (Windows/OS ● Hadoop Tutorial 6 - Downloading and Installing Oracle VirtualBox ● Hadoop Tutorial 7 - Downloading Hadoop Appliance for Oracle VirtualBox ●