Big data hadoop
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Developed by Doug Cutting, Mika Cafarella and Team Open Source Project that works on MapReduce algorithm Apache Hadoop is a registered trademark of Apache Software Foundation
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Big data and hadoop
This document discusses big data and the Apache Hadoop framework. It defines big data as large, complex datasets that are difficult to process using traditional tools. Hadoop is an open-source framework for distributed storage and processing of big data 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 of machines with redundancy, while MapReduce splits tasks across processors and handles shuffling and sorting of data. Hadoop allows cost-effective processing of large, diverse datasets and has become a standard for big data.
Srikanth hadoop hyderabad_3.4yeras - copy
Srikanth K - Hadoop Developer, ADP india Pvt Ltd from Alwasi Software Pvt Ltd, 3.4 yrs, Hyderabad / Secunderabad
Big data
This document provides an overview of big data and related technologies. It defines big data as large amounts of data from various sources, including sensors, social media, purchases, and mobile devices. Hadoop and MapReduce are introduced as frameworks for distributed storage and processing of large datasets across clusters of servers. Key-value database Hypertable is also summarized as an open source alternative to Bigtable. The advantages of big data systems include flexibility, scalability, efficiency, and failure resistance through data replication.
Big Data Technologies - Hadoop
The document provides an overview of Hadoop including what it is, how it works, its architecture and components. Key points include:
- Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers using simple programming models.
- It consists of HDFS for storage and MapReduce for processing via parallel computation using a map and reduce technique.
- HDFS stores data reliably across commodity hardware and MapReduce processes large amounts of data in parallel across nodes in a cluster.
Big data course
The Big Data Hadoop Certification Training Course aims to provide complete knowledge of Big Data and Hadoop technologies including HDFS, YARN, and MapReduce. It offers comprehensive knowledge of tools in the Hadoop ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase. Students will learn to ingest and analyze large datasets stored in HDFS using real-world industry projects covering domains such as banking, telecommunications, social media, insurance, and e-commerce. Graduates can expect average salaries of Rs. 7,12,453 per year for Hadoop engineers according to payscale.com.
Srikanth hadoop 3.6yrs_hyd
Srikanth K - Hadoop Developer - Jawaharlal Nehru Technological University (JNTU), 2012, Hadoop Developer, ADP india Pvt Ltd from Alwasi Software Pvt Ltd, 3.6 yrs, Hyderabad
Hadoop Eco system
This document provides an overview of the Hadoop ecosystem. It begins by defining big data and explaining how Hadoop uses MapReduce and HDFS to allow for distributed processing and storage of large datasets across commodity hardware. It then describes various components of the Hadoop ecosystem for acquiring, arranging, analyzing, and visualizing data, including Flume, Sqoop, Kafka, HDFS, HBase, Spark, Pig, Hive, Impala, Mahout, and HUE. Real-world use cases of Hadoop at companies like Facebook, Twitter, and NASA are also discussed. Overall, the document outlines the key elements that make up the Hadoop ecosystem for working with big data.
The Fundamentals Guide to HDP and HDInsight
This session will give you the architectural overview and introduction in to inner workings of HDP 2.0 (http://hortonworks.com/products/hdp-windows/) and HDInsight. The world has embraced the Hadoop toolkit to solve their data problems from ETL, data warehouses to event processing pipelines. As Hadoop consists of many components, services and interfaces, understanding its architecture is crucial, before you can successfully integrate it in to your own environment.
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Big data and hadoop
This document discusses big data and the Apache Hadoop framework. It defines big data as large, complex datasets that are difficult to process using traditional tools. Hadoop is an open-source framework for distributed storage and processing of big data 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 of machines with redundancy, while MapReduce splits tasks across processors and handles shuffling and sorting of data. Hadoop allows cost-effective processing of large, diverse datasets and has become a standard for big data.
Srikanth hadoop hyderabad_3.4yeras - copy
Srikanth K - Hadoop Developer, ADP india Pvt Ltd from Alwasi Software Pvt Ltd, 3.4 yrs, Hyderabad / Secunderabad
Big data
This document provides an overview of big data and related technologies. It defines big data as large amounts of data from various sources, including sensors, social media, purchases, and mobile devices. Hadoop and MapReduce are introduced as frameworks for distributed storage and processing of large datasets across clusters of servers. Key-value database Hypertable is also summarized as an open source alternative to Bigtable. The advantages of big data systems include flexibility, scalability, efficiency, and failure resistance through data replication.
Big Data Technologies - Hadoop
The document provides an overview of Hadoop including what it is, how it works, its architecture and components. Key points include:
- Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers using simple programming models.
- It consists of HDFS for storage and MapReduce for processing via parallel computation using a map and reduce technique.
- HDFS stores data reliably across commodity hardware and MapReduce processes large amounts of data in parallel across nodes in a cluster.
Big data course
The Big Data Hadoop Certification Training Course aims to provide complete knowledge of Big Data and Hadoop technologies including HDFS, YARN, and MapReduce. It offers comprehensive knowledge of tools in the Hadoop ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase. Students will learn to ingest and analyze large datasets stored in HDFS using real-world industry projects covering domains such as banking, telecommunications, social media, insurance, and e-commerce. Graduates can expect average salaries of Rs. 7,12,453 per year for Hadoop engineers according to payscale.com.
Srikanth hadoop 3.6yrs_hyd
Srikanth K - Hadoop Developer - Jawaharlal Nehru Technological University (JNTU), 2012, Hadoop Developer, ADP india Pvt Ltd from Alwasi Software Pvt Ltd, 3.6 yrs, Hyderabad
Hadoop Eco system
This document provides an overview of the Hadoop ecosystem. It begins by defining big data and explaining how Hadoop uses MapReduce and HDFS to allow for distributed processing and storage of large datasets across commodity hardware. It then describes various components of the Hadoop ecosystem for acquiring, arranging, analyzing, and visualizing data, including Flume, Sqoop, Kafka, HDFS, HBase, Spark, Pig, Hive, Impala, Mahout, and HUE. Real-world use cases of Hadoop at companies like Facebook, Twitter, and NASA are also discussed. Overall, the document outlines the key elements that make up the Hadoop ecosystem for working with big data.
The Fundamentals Guide to HDP and HDInsight
This session will give you the architectural overview and introduction in to inner workings of HDP 2.0 (http://hortonworks.com/products/hdp-windows/) and HDInsight. The world has embraced the Hadoop toolkit to solve their data problems from ETL, data warehouses to event processing pipelines. As Hadoop consists of many components, services and interfaces, understanding its architecture is crucial, before you can successfully integrate it in to your own environment.
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- Apache Spark provides a richer, more expressive API that allows developing applications with 2-5x less code than MapReduce. It also provides fast in-memory execution up to an order of magnitude faster than MapReduce.
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You'll learn:
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The document provides an overview of Apache Hadoop and common use cases. It describes how Hadoop is well-suited for log processing due to its ability to handle large amounts of data in parallel across commodity hardware. Specifically, it allows processing of log files to be distributed per unit of data, avoiding bottlenecks that can occur when trying to process a single large file sequentially.
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What’s important about a technology is what you can use it to do. I’ve looked at what a number of groups are doing with Apache Hadoop and NoSQL in production, and I will relay what worked well for them and what did not. Drawing from real world use cases, I show how people who understand these new approaches can employ them well in conjunction with traditional approaches and existing applications. Thread Detection, Datawarehouse optimization, Marketing Efficiency, Biometric Database are some examples exposed during this presentation.
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Apache Eagle: Secure Hadoop in Real Time
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The document discusses Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides an overview of Hadoop core projects including HDFS, MapReduce, and related projects like Pig, Hive, HBase and Zookeeper. The document also references presentations and articles about Hadoop use cases at Yahoo and the evolution of the Hadoop ecosystem with higher-level tools and interfaces for programming, querying, and managing distributed Hadoop applications and data.
Keynote – From MapReduce to Spark: An Ecosystem Evolves by Doug Cutting, Chie...
The document summarizes the evolution from MapReduce to Apache Spark for data processing. Some key points:
- MapReduce provided breakthroughs like data locality, fault tolerance, and scalability but the programming model required developing generally scalable solutions.
- Apache Spark provides a richer, more expressive API that allows developing applications with 2-5x less code than MapReduce. It also provides fast in-memory execution up to an order of magnitude faster than MapReduce.
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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.
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This document discusses deploying and researching Hadoop in virtual machines. It provides definitions of Hadoop, MapReduce, and HDFS. It describes using CloudStack to deploy a Hadoop cluster across multiple virtual machines to enable distributed and parallel processing of large datasets. The proposed system is to deploy Hadoop applications on virtual machines from a CloudStack infrastructure for improved performance, reliability and reduced power consumption compared to a single virtual machine. It outlines the hardware, software, architecture, design, testing and outputs of the proposed system.
Hadoop.pptx
Hadoop is an open source software framework that allows for distributed processing of large data sets across clusters of computers. It uses MapReduce as a programming model and HDFS for storage. Hadoop supports various big data applications like HBase for distributed column storage, Hive for data warehousing and querying, Pig and Jaql for data flow languages, and Hadoop ecosystem projects for tasks like system monitoring and machine learning.
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 ppt1
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Introduction to BIg Data and Hadoop
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses limitations in traditional RDBMS for big data by allowing scaling to large clusters of commodity servers, high fault tolerance, and distributed processing. The core components of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. Hadoop has an ecosystem of additional tools like Pig, Hive, HBase and more. Major companies use Hadoop to process and gain insights from massive amounts of structured and unstructured data.
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Big data hadoop
- 1. Big Data- Hadoop Presented By: Vikram Dey
- 2. Big Data- Hadoop 90% of world’s data is generated in the last few years Big Data: Large dataset the cannot be processed using the traditional computing techniques. What comes under Big Data: • Social Media Data • Stock exchange Data • Search engine data
- 4. HADOOP • Developed by Doug Cutting, Mika Cafarella and Team • Open Source Project that works on MapReduce algorithm • Apache Hadoop is a registered trademark of Apache Software Foundation
- 5. HADOOP Framework • Hadoop Common: Java Libraries • Hadoop YARN: Job Scheduling and cluster management framework •Hadoop HDFS: Distributed File System that provides high- throughput access to application data MapReduce: Software framework for parallel processing of large data sets
- 6. How Does HADOOP Work? Stage 1 User submit a job to the Hadoop Job-Client for required process by specifying : • the input and output files location in DFS • Job configuration by setting different parameters specific to the jobStage 2 • The Hadoop job client then submits the job and configuration to JobTracker • JobTracker distributes the configuration to the slaves, scheduling tasks and monitoring them, providing status to job- client.
- 7. How Does HADOOP Work? Stage 3 TaskTracker executes the task as per MapReduce implementation and output is stored into output files on the file system.
- 8. Thank You