This document provides an introduction to Hadoop, including its motivation and key components. It discusses the scale of cloud computing that Hadoop addresses, and describes the core Hadoop technologies - the Hadoop Distributed File System (HDFS) and MapReduce framework. It also briefly introduces the Hadoop ecosystem, including other related projects like Pig, HBase, Hive and ZooKeeper. Sample code is walked through to illustrate MapReduce programming. Key aspects of HDFS like fault tolerance, scalability and data reliability are summarized.
Hadoop is a distributed processing framework for large datasets. It utilizes HDFS for storage and MapReduce as its programming model. The Hadoop ecosystem has expanded to include many other tools. YARN was developed to address limitations in the original Hadoop architecture. It provides a common platform for various data processing engines like MapReduce, Spark, and Storm. YARN improves scalability, utilization, and supports multiple workloads by decoupling cluster resource management from application logic. It allows different applications to leverage shared Hadoop cluster resources.
This presentation discusses the follow topics What is Hadoop? Need for Hadoop History of Hadoop Hadoop Overview Advantages and Disadvantages of Hadoop Hadoop Distributed File System Comparing: RDBMS vs. Hadoop Advantages and Disadvantages of HDFS Hadoop frameworks Modules of Hadoop frameworks Features of 'Hadoop‘ Hadoop Analytics Tools
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
Here is how you can solve this problem using MapReduce and Unix commands: Map step: grep -o 'Blue\|Green' input.txt | wc -l > output This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches). Reduce step: cat output This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green. So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map
Hadoop DFS consists of HDFS for storage and MapReduce for processing. HDFS provides massive storage, fault tolerance through data replication, and high throughput access to data. It uses a master-slave architecture with a NameNode managing the file system namespace and DataNodes storing file data blocks. The NameNode ensures data reliability through policies that replicate blocks across racks and nodes. HDFS provides scalability, flexibility and low-cost storage of large datasets.
The document discusses the Hadoop ecosystem, which includes core Apache Hadoop components like HDFS, MapReduce, YARN, as well as related projects like Pig, Hive, HBase, Mahout, Sqoop, ZooKeeper, Chukwa, and HCatalog. It provides overviews and diagrams explaining the architecture and purpose of each component, positioning them as core functionality that speeds up Hadoop processing and makes Hadoop more usable and accessible.
Hadoop, flexible and available architecture for large scale computation and data processing on a network of commodity hardware.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units. Below topics are explained in this Hadoop presentation: 1. What is Hadoop 2. Why Hadoop 3. Big Data generation 4. Hadoop HDFS 5. Hadoop MapReduce 6. Hadoop YARN 7. Use of Hadoop 8. Demo on HDFS, MapReduce and YARN What is this Big Data Hadoop training course about? The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. What are the course objectives? This course will enable you to: 1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
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.
This presentation provides an overview of Hadoop, including: - A brief history of data and the rise of big data from various sources. - An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers. - Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture. - An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes. - Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
The document summarizes the history and evolution of non-relational databases, known as NoSQL databases. It discusses early database systems like MUMPS and IMS, the development of the relational model in the 1970s, and more recent NoSQL databases developed by companies like Google, Amazon, Facebook to handle large, dynamic datasets across many servers. Pioneering systems like Google's Bigtable and Amazon's Dynamo used techniques like distributed indexing, versioning, and eventual consistency that influenced many open-source NoSQL databases today.
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.
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail Below topics are explained in this Hive presetntation: 1. History of Hive 2. What is Hive? 3. Architecture of Hive 4. Data flow in Hive 5. Hive data modeling 6. Hive data types 7. Different modes of Hive 8. Difference between Hive and RDBMS 9. Features of Hive 10. Demo on HiveQL What is this Big Data Hadoop training course about? The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. What are the course objectives? This course will enable you to: 1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
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
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
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In this webcast, Reynold Xin from Databricks will be speaking about Apache Spark's new 2.0 major release. The major themes for Spark 2.0 are: - Unified APIs: Emphasis on building up higher level APIs including the merging of DataFrame and Dataset APIs - Structured Streaming: Simplify streaming by building continuous applications on top of DataFrames allow us to unify streaming, interactive, and batch queries. - Tungsten Phase 2: Speed up Apache Spark by 10X
Apache Spark is a fast and general-purpose cluster computing system for large-scale data processing. It improves on MapReduce by allowing data to be kept in memory across jobs, enabling faster iterative jobs. Spark consists of a core engine along with libraries for SQL, streaming, machine learning, and graph processing. The document discusses new APIs in Spark including DataFrames, which provide a tabular interface like in R/Python, and data sources, which allow plugging external data systems into Spark. These changes aim to make Spark easier for data scientists to use at scale.
Spark is a fast and general engine for large-scale data processing. It provides APIs in Java, Scala, and Python and an interactive shell. Spark applications operate on resilient distributed datasets (RDDs) that can be cached in memory for faster performance. RDDs are immutable and fault-tolerant via lineage graphs. Transformations create new RDDs from existing ones while actions return values to the driver program. Spark's execution model involves a driver program that coordinates tasks on executor machines. RDD caching and lineage graphs allow Spark to efficiently run jobs across clusters.
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker. At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics. PPT Agenda: ✓ Introduction to BIG Data & Hadoop ✓ What is MapReduce? ✓ MapReduce Data Flows ✓ MapReduce Programming ---------- 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. ---------- 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. ---------- 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
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Apache Spark is a fast, general engine for large-scale data processing. It provides unified analytics engine for batch, interactive, and stream processing using an in-memory abstraction called resilient distributed datasets (RDDs). Spark's speed comes from its ability to run computations directly on data stored in cluster memory and optimize performance through caching. It also integrates well with other big data technologies like HDFS, Hive, and HBase. Many large companies are using Spark for its speed, ease of use, and support for multiple workloads and languages.
This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
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This document discusses MapReduce and its ability to process large datasets in a distributed manner. MapReduce addresses challenges of distributed computation by allowing programmers to specify map and reduce functions. It then parallelizes the execution of these functions across large clusters and handles failures transparently. The map function processes input key-value pairs to generate intermediate pairs, which are then grouped by key and passed to reduce functions to generate the final output.
The document describes the Hadoop ecosystem and its core components. It discusses HDFS, which stores large files across clusters and is made up of a NameNode and DataNodes. It also discusses MapReduce, which allows distributed processing of large datasets using a map and reduce function. Other components discussed include Hive, Pig, Impala, and Sqoop.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a simple programming model called MapReduce that automatically parallelizes and distributes work across nodes. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and MapReduce execution engine for processing. HDFS stores data as blocks replicated across nodes for fault tolerance. MapReduce jobs are split into map and reduce tasks that process key-value pairs in parallel. Hadoop is well-suited for large-scale data analytics as it scales to petabytes of data and thousands of machines with commodity hardware.
An increasing number of popular applications become data-intensive in nature. In the past decade, the World Wide Web has been adopted as an ideal platform for developing data-intensive applications, since the communication paradigm of the Web is sufficiently open and powerful. Data-intensive applications like data mining and web indexing need to access ever-expanding data sets ranging from a few gigabytes to several terabytes or even petabytes. Google leverages the MapReduce model to process approximately twenty petabytes of data per day in a parallel fashion. In this talk, we introduce the Google’s MapReduce framework for processing huge datasets on large clusters. We first outline the motivations of the MapReduce framework. Then, we describe the dataflow of MapReduce. Next, we show a couple of example applications of MapReduce. Finally, we present our research project on the Hadoop Distributed File System. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into account for launching speculative map tasks, because it is assumed that most maps are data-local. Unfortunately, both the homogeneity and data locality assumptions are not satisfied in virtualized data centers. We show that ignoring the datalocality issue in heterogeneous environments can noticeably reduce the MapReduce performance. In this paper, we address the problem of how to place data across nodes in a way that each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster, our data placement scheme adaptively balances the amount of data stored in each node to achieve improved data-processing performance. Experimental results on two real data-intensive applications show that our data placement strategy can always improve the MapReduce performance by rebalancing data across nodes before performing a data-intensive application in a heterogeneous Hadoop cluster.
The document provides an overview of data science with Python and integrating Python with Hadoop and Apache Spark frameworks. It discusses: - Why Python should be integrated with Hadoop and the ecosystem including HDFS, MapReduce, and Spark. - Key concepts of Hadoop including HDFS for storage, MapReduce for processing, and how Python can be integrated via APIs. - Benefits of Apache Spark like speed, simplicity, and efficiency through its RDD abstraction and how PySpark enables Python access. - Examples of using Hadoop Streaming and PySpark to analyze data and determine word counts from documents.
This document provides an introduction to MapReduce programming model. It describes how MapReduce inspired by Lisp functions works by dividing tasks into mapping and reducing parts that are distributed and processed in parallel. It then gives examples of using MapReduce for word counting and calculating total sales. It also provides details on MapReduce daemons in Hadoop and includes demo code for summing array elements in Java and doing word counting on a text file using the Hadoop framework in Python.
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics. This slide covers the Advance Map reduce concepts of Hadoop and Big Data. For training queries you can contact us: Email: admin@designpathshala.com Call us at: +91 98 188 23045 Visit us at: http://designpathshala.com Join us at: http://www.designpathshala.com/contact-us Course details: http://www.designpathshala.com/course/view/65536 Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792 Business Analytics Course details: http://www.designpathshala.com/course/view/196608
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through HDFS and distributed processing via MapReduce. HDFS handles storage and MapReduce provides a programming model for parallel processing of large datasets across a cluster. The MapReduce framework consists of a mapper that processes input key-value pairs in parallel, and a reducer that aggregates the output of the mappers by key.
The document discusses network performance profiling of Hadoop jobs. It presents results from running two common Hadoop benchmarks - Terasort and Ranked Inverted Index - on different Amazon EC2 instance configurations. The results show that the shuffle phase accounts for a significant portion (25-29%) of total job runtime. They aim to reproduce existing findings that network performance is a key bottleneck for shuffle-intensive Hadoop jobs. Some questions are also raised about inconsistencies in reported network bandwidth capabilities for EC2.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
This document provides an overview of MapReduce and Hadoop. It describes the Map and Reduce functions, explaining that Map applies a function to each element of a list and Reduce reduces a list to a single value. It gives examples of Map and Reduce using employee salary data. It then discusses Hadoop and its core components HDFS for distributed storage and MapReduce for distributed processing. Key aspects covered include the NameNode, DataNodes, input/output formats, and the job launch process. It also addresses some common questions around small files, large files, and accessing SQL data from Hadoop.
Hadoop is an open source framework for distributed storage and processing of vast amounts of data across clusters of computers. It uses a master-slave architecture with a single JobTracker master and multiple TaskTracker slaves. The JobTracker schedules tasks like map and reduce jobs on TaskTrackers, which each run task instances in separate JVMs. It monitors task progress and reschedules failed tasks. Hadoop uses MapReduce programming model where the input is split and mapped in parallel, then outputs are shuffled, sorted, and reduced to form the final results.
This document provides an overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop was developed based on Google's MapReduce algorithm and how it uses HDFS for scalable storage and MapReduce as an execution engine. Key components of Hadoop architecture include HDFS for fault-tolerant storage across data nodes and the MapReduce programming model for parallel processing of data blocks. The document also gives examples of how MapReduce works and industries that use Hadoop for big data applications.