Hadoop training in bangalore
•Download as PPTX, PDF•
0 likes•17 views
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.
Report
Share
Report
Share
Recommended
Hadoop tutorial for Freshers,
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
This document discusses Hadoop technology. It was developed by Doug Cutting and Michael J. Cafarella to support large-scale data processing for the Nutch search engine project. Hadoop features include distributed storage via HDFS and distributed processing via MapReduce. It allows for scalable and fault-tolerant processing of large data sets across commodity hardware. While Hadoop provides low-cost processing of big data, its administration and integration with other systems can be challenging.
HADOOP DISTRIBUTED FILE SYSTEM AND MAPREDUCE
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It has three key components:
1. The Hadoop Distributed File System (HDFS) provides distributed storage and access to large datasets across clusters.
2. MapReduce is a programming model used to process and generate large datasets in a distributed computing environment.
3. YARN is a framework for job scheduling and cluster resource management. It allows multiple data processing engines like MapReduce to handle distributed applications.
The document discusses these components in detail, explaining HDFS data replication across nodes, the MapReduce programming model for distributed computation, and how Hadoop provides a cost-effective, flexible and resilient
MapReduce - Hadoop - Big Data
Problem analysis of MapReduce .
Mapreduce performs poorly in iterative why ?
Hadoop does not function well for random access to its datasets . But YARN promise to support that .
Why Hadoop do not support broadcasting ?
JAVA do not support sharing references during mapping task .
Apache hadoop & map reduce
This document discusses Apache Hadoop and how it provides solutions for big data problems through MapReduce and HDFS. It outlines key issues like hardware failure and combining data when implementing parallelism for big data storage and analysis. Hadoop overcomes these issues using HDFS for reliable shared storage and MapReduce for reliable analysis through processing data in parallel using keys and values. MapReduce is a batch query processor that is already used by companies to handle large datasets, providing an alternative to traditional RDBMS for big data applications.
Azure cognitive service
1. The document discusses artificial intelligence and machine learning models, from research and development to production deployment.
2. It notes that AI today is trying to mimic human senses through computer vision, natural language processing, and cognitive capabilities like perception and perspectives.
3. The document outlines the machine learning lifecycle from data collection and training to model deployment on various targets using products that leverage machine learning.
Hadoop
Hadoop is an open-source framework for storing and processing large datasets in a distributed computing environment. It allows for massive data storage, enormous processing power, and the ability to handle large numbers of concurrent tasks across clusters of commodity hardware. The framework includes Hadoop Distributed File System (HDFS) for reliable data storage and MapReduce for parallel processing of large datasets. An ecosystem of related projects like Pig, Hive, HBase, Sqoop and Flume extend the functionality of Hadoop.
Challenges in Large Scale Machine Learning
This document discusses challenges in large scale machine learning. It begins by discussing why distributed machine learning is necessary when data is too large for one computer to store or when models have too many parameters. It then discusses various challenges that arise in distributed machine learning including scalability issues, class imbalance, the curse of dimensionality, overfitting, and algorithm complexities related to data loading times. Specific examples are provided of distributing k-means clustering and spectral clustering algorithms. Distributed implementations of support vector machines are also discussed. Throughout, it emphasizes the importance of understanding when and where distributed approaches are suitable compared to single machine learning.
Recommended
Hadoop tutorial for Freshers,
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
This document discusses Hadoop technology. It was developed by Doug Cutting and Michael J. Cafarella to support large-scale data processing for the Nutch search engine project. Hadoop features include distributed storage via HDFS and distributed processing via MapReduce. It allows for scalable and fault-tolerant processing of large data sets across commodity hardware. While Hadoop provides low-cost processing of big data, its administration and integration with other systems can be challenging.
HADOOP DISTRIBUTED FILE SYSTEM AND MAPREDUCE
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It has three key components:
1. The Hadoop Distributed File System (HDFS) provides distributed storage and access to large datasets across clusters.
2. MapReduce is a programming model used to process and generate large datasets in a distributed computing environment.
3. YARN is a framework for job scheduling and cluster resource management. It allows multiple data processing engines like MapReduce to handle distributed applications.
The document discusses these components in detail, explaining HDFS data replication across nodes, the MapReduce programming model for distributed computation, and how Hadoop provides a cost-effective, flexible and resilient
MapReduce - Hadoop - Big Data
Problem analysis of MapReduce .
Mapreduce performs poorly in iterative why ?
Hadoop does not function well for random access to its datasets . But YARN promise to support that .
Why Hadoop do not support broadcasting ?
JAVA do not support sharing references during mapping task .
Apache hadoop & map reduce
This document discusses Apache Hadoop and how it provides solutions for big data problems through MapReduce and HDFS. It outlines key issues like hardware failure and combining data when implementing parallelism for big data storage and analysis. Hadoop overcomes these issues using HDFS for reliable shared storage and MapReduce for reliable analysis through processing data in parallel using keys and values. MapReduce is a batch query processor that is already used by companies to handle large datasets, providing an alternative to traditional RDBMS for big data applications.
Azure cognitive service
1. The document discusses artificial intelligence and machine learning models, from research and development to production deployment.
2. It notes that AI today is trying to mimic human senses through computer vision, natural language processing, and cognitive capabilities like perception and perspectives.
3. The document outlines the machine learning lifecycle from data collection and training to model deployment on various targets using products that leverage machine learning.
Hadoop
Hadoop is an open-source framework for storing and processing large datasets in a distributed computing environment. It allows for massive data storage, enormous processing power, and the ability to handle large numbers of concurrent tasks across clusters of commodity hardware. The framework includes Hadoop Distributed File System (HDFS) for reliable data storage and MapReduce for parallel processing of large datasets. An ecosystem of related projects like Pig, Hive, HBase, Sqoop and Flume extend the functionality of Hadoop.
Challenges in Large Scale Machine Learning
This document discusses challenges in large scale machine learning. It begins by discussing why distributed machine learning is necessary when data is too large for one computer to store or when models have too many parameters. It then discusses various challenges that arise in distributed machine learning including scalability issues, class imbalance, the curse of dimensionality, overfitting, and algorithm complexities related to data loading times. Specific examples are provided of distributing k-means clustering and spectral clustering algorithms. Distributed implementations of support vector machines are also discussed. Throughout, it emphasizes the importance of understanding when and where distributed approaches are suitable compared to single machine learning.
Machine Learning on Distributed Systems by Josh Poduska
Abstract:- Most real-world data science workflows require more than multiple cores on a single server to meet scale and speed demands, but there is a general lack of understanding when it comes to what machine learning on distributed systems looks like in practice. Gartner and Forrester do not consider distributed execution when they score advanced analytics software solutions. Many formal machine learning training occurs on single node machines with non-distributed algorithms. In this talk we discuss why an understanding of distributed architectures is important for anyone in the analytical sciences. We will cover the current distributed machine learning ecosystem. We will review common pitfalls when performing machine learning at scale. We will discuss architectural considerations for a machine learning program such as the role of storage and compute and under what circumstances they should be combined or separated.
Introduction To Hadoop Administration - SpringPeople
The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The popularity of Hadoop is juts increasing exponentially.
Pervasive DataRush
This document summarizes Pervasive DataRush, a software platform that can eliminate performance bottlenecks in data-intensive applications. It processes data in parallel to provide high throughput and scale performance on commodity hardware. DataRush integrates with Apache Hadoop and can increase Hadoop performance, processing data up to 13x faster than MapReduce. It is used across industries for tasks like genomic analysis, fraud detection, cybersecurity, and more.
SparkApplicationDevMadeEasy_Spark_Summit_2015
The document discusses Spark application development and common problems that can occur. It introduces Unravel Data, a startup that aims to help developers visualize Spark job data, optimize performance through automated analysis and diagnoses, and strategize to prevent problems and meet goals. Key points include discussing common issues like failures, wrong results, poor performance, and resource problems; the difficulty of debugging using logs alone; and demonstrating Unravel's platform to address these challenges.
Spark For Faster Batch Processing
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
Revolution Analytics
This document introduces Revolution Analytics' support for integrating R and Hadoop. It outlines Revolution Analytics' big data analytics strategy and products that allow R programmers to access Hadoop data stores like HDFS and HBase directly from R. Key packages discussed are RevoHDFS, RevoHBase, and RevoHStream, which provide connectivity and capabilities to work with Hadoop from R for tasks like reading/writing HDFS files, interacting with HBase tables, and developing/running MapReduce jobs. Examples of using these packages for logistic regression analysis on Hadoop are also provided.
Couchbase
This document summarizes Couchbase's NoSQL database solutions and its use by Zynga. It discusses how Couchbase is simple to deploy, develop, and manage. It is also fast with predictable low latency and sub-ms response times. Couchbase is elastic and can spread I/O and data across instances with consistent performance and linear cost scaling. It has proven scalability from small to large deployments, including serving over 230 million monthly users for Zynga. The document also describes Couchbase's connector to Cloudera for connecting online and offline data processing.
5 things one must know about spark!
Spark can process data faster than Hadoop by keeping data in-memory as much as possible to avoid disk I/O. It supports streaming data, machine learning algorithms, graph processing, and SQL queries on structured data using its DataFrame API. Spark can integrate with Hadoop by running on YARN and accessing data from HDFS. The key capabilities discussed include low latency processing, streaming, machine learning, graph processing, DataFrames, and Hadoop integration.
Introduction to Big Data Technologies: Hadoop/EMR/Map Reduce & Redshift
Boston Data Mining Meetup introduction slides from Big Data Infrastructure workshop - A hands-on introduction
Impala use case @ Zoosk
In this presentation, Zoosk will share its experience in transitioning the Zoosk Big Data Platform from Hive to a Hive/Impala configuration. We will share lessons learned, some guidelines about when to use one or another, and a high level before-and-after view of its architecture.
Power aware load balancing in cloud
This document summarizes a paper about power aware load balancing in cloud computing. It discusses how data centers consume large amounts of energy and outlines various techniques to reduce power consumption, including dynamic resource provisioning algorithms like AutoScale. The document reviews related works on load balancing algorithms and power savings. It then describes the AutoScale algorithm and compares it to other approaches like AlwaysOn and Reactive methods in terms of meeting service level agreements while minimizing servers and energy use.
Cluster computing
Cluster computing involves connecting multiple computers together to work as a single system. The key components of a cluster include applications, parallel programming environments, cluster middleware, computers connected via a high-speed network switch. There are three main types of clusters: load-balancing clusters which distribute work evenly, high availability clusters which ensure critical applications can failover to another node, and high performance clusters which tackle complex computations.
Atlanta MLConf
Qubole presentation by Jason Huang, Sr. Solutions Architect for Qubole at the MLConf in Atlanta September 18th, 2015
Building Data Quality pipelines with Apache Spark and Delta Lake
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
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.
- A survey found 82% of developers replaced MapReduce with Spark for its speed and ability to handle large datasets faster than MapReduce. Spark is now an important part of the Hadoop ecosystem.
BDM26: Spark Summit 2014 Debriefing
Highlights of the most interesting topics discussed at the Spark Summit 2014 in San Francisco, California
From Pipelines to Refineries: scaling big data applications with Tim Hunter
Big data tools are challenging to combine into a larger application: ironically, big data applications themselves do not tend to scale very well. These issues of integration and data management are only magnified by increasingly large volumes of data. Apache Spark provides strong building blocks for batch processes, streams and ad-hoc interactive analysis. However, users face challenges when putting together a single coherent pipeline that could involve hundreds of transformation steps, especially when confronted by the need of rapid iterations. This talk explores these issues through the lens of functional programming. It presents an experimental framework that provides full-pipeline guarantees by introducing more laziness to Apache Spark. This framework allows transformations to be seamlessly composed and alleviates common issues, thanks to whole program checks, auto-caching, and aggressive computation parallelization and reuse.
Data replication and synchronization tool
This presentation discusses a data sharing synchronization tool, implemented using Infinispan. The focus is medical images and meta data such as the cancer imaging archive (TCIA)
Hadoop Ecosystem at a Glance
Hadoop as we know is a Java based massive scalable distributed framework for processing large data (several peta bytes) across a cluster (1000s) of commodity computers.
The Hadoop ecosystem has grown over the last few years and there is a lot of jargon in terms of tools as well as frameworks.
Many organizations are investing & innovating heavily in Hadoop to make it better and easier. The mind map on the next slide should be useful to get a high level picture of the ecosystem.
Distributed machine learning
Machine learning can be distributed across multiple machines to allow for processing of large datasets and complex models. There are three main approaches to distributed machine learning: data parallel, where the data is partitioned across machines and models are replicated; model parallel, where different parts of large models are distributed; and graph parallel, where graphs and algorithms are partitioned. Distributed frameworks use these approaches to efficiently and scalably train machine learning models on big data in parallel.
PPT on Hadoop
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
Hadoop introduction , Why and What is Hadoop ?
Hadoop Introduction
you connect with us: http://www.linkedin.com/profile/view?id=232566291&trk=nav_responsive_tab_profile
More Related Content
What's hot
Machine Learning on Distributed Systems by Josh Poduska
Abstract:- Most real-world data science workflows require more than multiple cores on a single server to meet scale and speed demands, but there is a general lack of understanding when it comes to what machine learning on distributed systems looks like in practice. Gartner and Forrester do not consider distributed execution when they score advanced analytics software solutions. Many formal machine learning training occurs on single node machines with non-distributed algorithms. In this talk we discuss why an understanding of distributed architectures is important for anyone in the analytical sciences. We will cover the current distributed machine learning ecosystem. We will review common pitfalls when performing machine learning at scale. We will discuss architectural considerations for a machine learning program such as the role of storage and compute and under what circumstances they should be combined or separated.
Introduction To Hadoop Administration - SpringPeople
The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The popularity of Hadoop is juts increasing exponentially.
Pervasive DataRush
This document summarizes Pervasive DataRush, a software platform that can eliminate performance bottlenecks in data-intensive applications. It processes data in parallel to provide high throughput and scale performance on commodity hardware. DataRush integrates with Apache Hadoop and can increase Hadoop performance, processing data up to 13x faster than MapReduce. It is used across industries for tasks like genomic analysis, fraud detection, cybersecurity, and more.
SparkApplicationDevMadeEasy_Spark_Summit_2015
The document discusses Spark application development and common problems that can occur. It introduces Unravel Data, a startup that aims to help developers visualize Spark job data, optimize performance through automated analysis and diagnoses, and strategize to prevent problems and meet goals. Key points include discussing common issues like failures, wrong results, poor performance, and resource problems; the difficulty of debugging using logs alone; and demonstrating Unravel's platform to address these challenges.
Spark For Faster Batch Processing
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
Revolution Analytics
This document introduces Revolution Analytics' support for integrating R and Hadoop. It outlines Revolution Analytics' big data analytics strategy and products that allow R programmers to access Hadoop data stores like HDFS and HBase directly from R. Key packages discussed are RevoHDFS, RevoHBase, and RevoHStream, which provide connectivity and capabilities to work with Hadoop from R for tasks like reading/writing HDFS files, interacting with HBase tables, and developing/running MapReduce jobs. Examples of using these packages for logistic regression analysis on Hadoop are also provided.
Couchbase
This document summarizes Couchbase's NoSQL database solutions and its use by Zynga. It discusses how Couchbase is simple to deploy, develop, and manage. It is also fast with predictable low latency and sub-ms response times. Couchbase is elastic and can spread I/O and data across instances with consistent performance and linear cost scaling. It has proven scalability from small to large deployments, including serving over 230 million monthly users for Zynga. The document also describes Couchbase's connector to Cloudera for connecting online and offline data processing.
5 things one must know about spark!
Spark can process data faster than Hadoop by keeping data in-memory as much as possible to avoid disk I/O. It supports streaming data, machine learning algorithms, graph processing, and SQL queries on structured data using its DataFrame API. Spark can integrate with Hadoop by running on YARN and accessing data from HDFS. The key capabilities discussed include low latency processing, streaming, machine learning, graph processing, DataFrames, and Hadoop integration.
Introduction to Big Data Technologies: Hadoop/EMR/Map Reduce & Redshift
Boston Data Mining Meetup introduction slides from Big Data Infrastructure workshop - A hands-on introduction
Impala use case @ Zoosk
In this presentation, Zoosk will share its experience in transitioning the Zoosk Big Data Platform from Hive to a Hive/Impala configuration. We will share lessons learned, some guidelines about when to use one or another, and a high level before-and-after view of its architecture.
Power aware load balancing in cloud
This document summarizes a paper about power aware load balancing in cloud computing. It discusses how data centers consume large amounts of energy and outlines various techniques to reduce power consumption, including dynamic resource provisioning algorithms like AutoScale. The document reviews related works on load balancing algorithms and power savings. It then describes the AutoScale algorithm and compares it to other approaches like AlwaysOn and Reactive methods in terms of meeting service level agreements while minimizing servers and energy use.
Cluster computing
Cluster computing involves connecting multiple computers together to work as a single system. The key components of a cluster include applications, parallel programming environments, cluster middleware, computers connected via a high-speed network switch. There are three main types of clusters: load-balancing clusters which distribute work evenly, high availability clusters which ensure critical applications can failover to another node, and high performance clusters which tackle complex computations.
Atlanta MLConf
Qubole presentation by Jason Huang, Sr. Solutions Architect for Qubole at the MLConf in Atlanta September 18th, 2015
Building Data Quality pipelines with Apache Spark and Delta Lake
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
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.
- A survey found 82% of developers replaced MapReduce with Spark for its speed and ability to handle large datasets faster than MapReduce. Spark is now an important part of the Hadoop ecosystem.
BDM26: Spark Summit 2014 Debriefing
Highlights of the most interesting topics discussed at the Spark Summit 2014 in San Francisco, California
From Pipelines to Refineries: scaling big data applications with Tim Hunter
Big data tools are challenging to combine into a larger application: ironically, big data applications themselves do not tend to scale very well. These issues of integration and data management are only magnified by increasingly large volumes of data. Apache Spark provides strong building blocks for batch processes, streams and ad-hoc interactive analysis. However, users face challenges when putting together a single coherent pipeline that could involve hundreds of transformation steps, especially when confronted by the need of rapid iterations. This talk explores these issues through the lens of functional programming. It presents an experimental framework that provides full-pipeline guarantees by introducing more laziness to Apache Spark. This framework allows transformations to be seamlessly composed and alleviates common issues, thanks to whole program checks, auto-caching, and aggressive computation parallelization and reuse.
Data replication and synchronization tool
This presentation discusses a data sharing synchronization tool, implemented using Infinispan. The focus is medical images and meta data such as the cancer imaging archive (TCIA)
Hadoop Ecosystem at a Glance
Hadoop as we know is a Java based massive scalable distributed framework for processing large data (several peta bytes) across a cluster (1000s) of commodity computers.
The Hadoop ecosystem has grown over the last few years and there is a lot of jargon in terms of tools as well as frameworks.
Many organizations are investing & innovating heavily in Hadoop to make it better and easier. The mind map on the next slide should be useful to get a high level picture of the ecosystem.
Distributed machine learning
Machine learning can be distributed across multiple machines to allow for processing of large datasets and complex models. There are three main approaches to distributed machine learning: data parallel, where the data is partitioned across machines and models are replicated; model parallel, where different parts of large models are distributed; and graph parallel, where graphs and algorithms are partitioned. Distributed frameworks use these approaches to efficiently and scalably train machine learning models on big data in parallel.
What's hot (20)
Machine Learning on Distributed Systems by Josh Poduska
Machine Learning on Distributed Systems by Josh Poduska
Introduction To Hadoop Administration - SpringPeople
Introduction To Hadoop Administration - SpringPeople
Introduction to Big Data Technologies: Hadoop/EMR/Map Reduce & Redshift
Introduction to Big Data Technologies: Hadoop/EMR/Map Reduce & Redshift
Building Data Quality pipelines with Apache Spark and Delta Lake
Building Data Quality pipelines with Apache Spark and Delta Lake
Keynote – From MapReduce to Spark: An Ecosystem Evolves by Doug Cutting, Chie...
Keynote – From MapReduce to Spark: An Ecosystem Evolves by Doug Cutting, Chie...
From Pipelines to Refineries: scaling big data applications with Tim Hunter
From Pipelines to Refineries: scaling big data applications with Tim Hunter
Similar to Hadoop training in bangalore
PPT on Hadoop
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
Hadoop introduction , Why and What is Hadoop ?
Hadoop Introduction
you connect with us: http://www.linkedin.com/profile/view?id=232566291&trk=nav_responsive_tab_profile
Seminar ppt
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 by kamran khan
This document provides an overview of Apache Hadoop, an open source framework for distributed storage and processing of large datasets across clusters of computers. It discusses big data and the need for solutions like Hadoop, describes the key components of Hadoop including HDFS for storage and MapReduce for processing, and outlines some applications and pros and cons of the Hadoop framework.
Hadoop Seminar Report
1. The document discusses the evolution of computing from mainframes to smaller commodity servers and PCs. It then introduces cloud computing as an emerging technology that is changing the technology landscape, with examples like Google File System and Amazon S3.
2. It discusses the need for large data processing due to increasing amounts of data from sources like the stock exchange, Facebook, genealogy sites, and scientific experiments.
3. Hadoop is introduced as a framework for distributed computing and reliable shared storage and analysis of large datasets using its Hadoop Distributed File System (HDFS) for storage and MapReduce for analysis.
Big Data and Hadoop
This document provides an overview of big data and Hadoop. It defines big data using the 3Vs - volume, variety, and velocity. It describes Hadoop as an open-source software framework for distributed storage and processing of large datasets. The key components of Hadoop are HDFS for storage and MapReduce for processing. HDFS stores data across clusters of commodity hardware and provides redundancy. MapReduce allows parallel processing of large datasets. Careers in big data involve working with Hadoop and related technologies to extract insights from large and diverse datasets.
2.1-HADOOP.pdf
This document provides an overview of Hadoop, including:
- Prerequisites for getting the most out of Hadoop include programming skills in languages like Java and Python, SQL knowledge, and basic Linux skills.
- Hadoop is a software framework for distributed processing of large datasets across computer clusters using MapReduce and HDFS.
- Core Hadoop components include HDFS for storage, MapReduce for distributed processing, and YARN for resource management.
- The Hadoop ecosystem also includes components like HBase, Pig, Hive, Mahout, Sqoop and others that provide additional functionality.
Hadoop live online training
Jumpstart your career with the world’s most in-demand technology: Hadoop. Hadooptrainingacademy provides best Hadoop online training with quality videos, comprehensive
online live training and detailed study material. Join today!
For more info, visit: http://www.hadooptrainingacademy.com/
Contact Us:
8121660088
732-419-2619
http://www.hadooptrainingacademy.com/
Hadoop info
hadoop architecture,hadoop,advantages of hadoop,disadvantages of hadoop,uses of hadoop,why hadoop,doug cutting,big data,hdfs,
Understanding hadoop
This document provides an overview of Hadoop, including its core components HDFS, MapReduce, and YARN. It describes how HDFS stores and replicates data across nodes for reliability. MapReduce is used for distributed processing of large datasets by mapping data to key-value pairs, shuffling, and reducing results. YARN was introduced to improve scalability by separating job scheduling and resource management from MapReduce. The document also gives examples of using MapReduce on a movie ratings dataset to demonstrate Hadoop functionality and running simple MapReduce jobs via the command line.
Hadoop technology
Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
Seminar_Report_hadoop
This document provides an introduction and overview of Hadoop, an open-source framework for distributed storage and processing of large datasets across clusters of computers. It discusses how Hadoop uses MapReduce and HDFS to parallelize workloads and store data redundantly across nodes to solve issues around hardware failure and combining results. Key aspects covered include how HDFS distributes and replicates data, how MapReduce isolates processing into mapping and reducing functions to abstract communication, and how Hadoop moves computation to the data to improve performance.
Introduccion a Hadoop / Introduction to Hadoop
Hadoop is a framework for distributed storage and processing of large datasets across clusters of computers. It addresses problems like hardware failure and combining data after analysis. The core components are HDFS for distributed storage and MapReduce for distributed processing. HDFS stores data as blocks across nodes and handles replication for reliability. The Namenode manages the file system namespace and metadata, while Datanodes store and retrieve blocks. Hadoop supports reliable analysis of large datasets in a distributed manner through its scalable architecture.
Cppt Hadoop
This document discusses Hadoop and its core components HDFS and MapReduce. It provides an overview of how Hadoop addresses the challenges of big data by allowing distributed processing of large datasets across clusters of computers. Key points include: Hadoop uses HDFS for distributed storage and MapReduce for distributed processing; HDFS works on a master-slave model with a Namenode and Datanodes; MapReduce utilizes a map and reduce programming model to parallelize tasks. Fault tolerance is built into Hadoop to prevent single points of failure.
Cppt
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.
Cppt
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.
Hadoop Technology
This document provides an overview of Big Data and Hadoop. It defines Big Data as large volumes of structured, semi-structured, and unstructured data that is too large to process using traditional databases and software. It provides examples of the large amounts of data generated daily by organizations. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop including HDFS for distributed storage and fault tolerance, and MapReduce for distributed processing, are described at a high level. Common use cases for Hadoop by large companies are also mentioned.
Learn what is Hadoop-and-BigData
Hadoop Training, Enhance your Big data subject knowledge with Online Training without wasting your time. Register for Free LIVE DEMO Class.
For more info: http://www.hadooponlinetutor.com
Contact Us:
8121660044
732-419-2619
http://www.hadooponlinetutor.com
Big data
Big Data - Overiew , Architecture, Testing, Performance Testing , Performance Monitoring , Performance Tuning Hadoop Clusters , Automation Tools
Seminar Presentation Hadoop
The document discusses big data and distributed computing. It provides examples of the large amounts of data generated daily by organizations like the New York Stock Exchange and Facebook. It explains how distributed computing frameworks like Hadoop use multiple computers connected via a network to process large datasets in parallel. Hadoop's MapReduce programming model and HDFS distributed file system allow users to write distributed applications that process petabytes of data across commodity hardware clusters.
Similar to Hadoop training in bangalore (20)
More from TIB Academy
Msbi
MSBI (Microsoft Business Intelligence) is a powerful tool which can be used to perform Data Collection, Data Analyzation and Data Reporting in an effective way. In BI workflow, ETL involves in the data collection phase. It relies on the existing or old data which has been collected from multiple sources. Microsoft Visual Studio together with Business Intelligence suite can be used to develop all sort of data warehousing applications.
Ios operating system
That’s great.
iOS is the leader in delivering the safest and the fastest mobile applications. It is constantly running forward to find new solutions for tomorrow’s security threats.
It can be applicable in many fields including finance & banking, chemicals, healthcare, energy, pharmaceuticals, telecommunications, entertainment, government at local, state and federal levels. So the need of hiring knowledgeable mobile application developers is highly intense in this mobile world.
Salesforce
Salesforce is a cloud-based customer relationship management (CRM) platform that offers various products and services including Sales Cloud, Service Cloud, and Marketing Cloud. It was founded in 1999 by Marc Benioff and is headquartered in San Francisco. Salesforce provides a complete CRM solution for marketing, sales, services, and other functions. It uses a software-as-a-service model and is accessible via the cloud, with no on-premise software required.
CCNA Introducing
CCNA (Cisco Certified Network Associate) is an advanced level of Cisco’s networking certification. It requires an in-depth knowledge to acquire CCNA certification.
CCNA Introducing
The document provides information about IT training courses offered by NIICT in Bangalore, India, with a focus on Cisco certification courses. It outlines the various Cisco certification paths from the associate to expert levels, including CCNA, CCNP, and CCIE certifications in areas such as routing and switching, security, voice, and wireless. It also provides details about the CCNA, CCNP, and CCIE Routing and Switching certifications and their associated skills and prerequisites.
Hadoop training
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.
Selenium institute in bangalore
Learn best Selenium course at Tibacademy training institute in Bangalore , TIBAcademy provides hands-on training in Weekdays and weekends
Selenium Tutorial for Beginners - TIB Academy
This document provides an introduction and overview of Selenium, including:
- The different forms of Selenium like Core, RC, IDE, and Grid.
- How Selenium Core works directly in the browser using JavaScript.
- How Selenium RC controls the browser from another process using different language drivers.
- Features of Selenium like support for multiple browsers and platforms, programming languages, and locator strategies.
- Additional tools that can be used with Selenium like the Selenium IDE Firefox extension.
Django framework
This document provides an overview of the Python web framework Django. It describes Django's features such as its MVT architecture, ORM, template system, admin interface, and URL dispatcher. It also covers installing Django, creating projects and applications, Django settings, and some major companies that use Django like Instagram, Pinterest, and The Washington Post.
Python basics
This document provides an overview of Python basics including statements, expressions, loops, strings, functions, and more. It discusses what a program is and how it runs. It explains how to get input from the user, import modules like math, use assignment statements, and print output. It also covers syntax, side effects vs returns, whitespace, operators, loops, range function, strings, indexes, basic string operations, len function, and functions. The document is from a Python training institute in Bangalore and is intended to teach Python fundamentals.
Core java tutorials
Learn Best Core Java course at TIBAcademy training institute in Bangalore, TIBAcademy Provides Hans-on training
Spring tutorials
This document provides an introduction to the Spring Framework, including its core features and architecture. It discusses how Spring addresses issues with other Java frameworks through loose coupling and dependency injection. It also describes Spring's Model-View-Controller architecture and how Spring supports aspects like logging and integration with big data technologies and databases. The document concludes with an overview of building a web application demo using Spring.
78
Tib academy is one of the best Hibernate training in Entire Bangalore. It is a platform where you can gain your gain from our trainers
Oracle DBA Tutorial for Beginners -Oracle training institute in bangalore
Get Oracle DBA Training through free Oracle DBA Tutorial, In this Oracle DBA Tutorial specially made for Beginners. You can download Oracle DBA Tutrial
Python tutorial for beginners - Tib academy
Get python training through simple tutorial from TIB Academy, through this python tutorial you can lean more topics of python. you can download python tutorial free as PPT
Best Angularjs tutorial for beginners - TIB Academy
TIB Academy is the best Angularjs training institute in Bangalore. We provides placement assistance live real time projects training from industrial experts.
https://www.tibacademy.in/training-in-bangalore/web-designing-training-in-bangalore/angularjs-training-in-bangalore/
More from TIB Academy (16)
Oracle DBA Tutorial for Beginners -Oracle training institute in bangalore
Oracle DBA Tutorial for Beginners -Oracle training institute in bangalore
Best Angularjs tutorial for beginners - TIB Academy
Best Angularjs tutorial for beginners - TIB Academy
Recently uploaded
How to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Fix the Import Error in the Odoo 17
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityAkanksha trivedi rama nursing college kanpur.
Natural birth techniques are various type such as/ water birth , alexender method, hypnosis, bradley method, lamaze method etcDRUGS AND ITS classification slide share
Any substance (other than food) that is used to prevent, diagnose, treat, or relieve symptoms of a
disease or abnormal condition
Recently uploaded (20)
Digital Artefact 1 - Tiny Home Environmental Design
Digital Artefact 1 - Tiny Home Environmental Design
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
Azure Interview Questions and Answers PDF By ScholarHat
Azure Interview Questions and Answers PDF By ScholarHat
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
Hadoop training in bangalore
- 2. 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 made by apache software foundation in 2011. • Written in JAVA.
- 3. Hadoop is open source software. Framework Massive Storage Processing Power
- 4. Big Data •Big data is a term used to define very large amount of unstructured and semi structured data a company creates. •The term is used when talking about Petabytes and Exabyte of data. •That much data would take so much time and cost to load into relational database for analysis. •Facebook has almost 10billion photos taking up to 1Petabytes of storage.
- 5. So what is theproblem?? 1. Processing that large data is very difficult in relational database. 2. It would take too much time to process data and cost.
- 6. We can solve this problem by Distributed Computing. But the problems in distributed computing is – Hardware failure Chances of hardware failure is always there. Combine the data after analysis Data from all disks have to be combined from all the disks which is a mess.
- 7. ToSolve all the Problems HadoopCame. It has two main parts – 1. Hadoop Distributed File System (HDFS), 2. Data Processing Framework & MapReduce
- 8. 1. Hadoop Distributed File System It ties so many small and reasonable priced machines together into a single cost effective computer cluster. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. it automatically stores multiple copies of all data. It provides simplified programming model which allows user to quickly read and write the distributed system.
- 9. 2. MapReduce MapReduce is a programming model for processing and generating large data sets with a parallel, distributed algorithm on a cluster. It is an associative implementation for processing and generating large data sets. MAP function that process a key pair to generates a set of intermediate key pairs. REDUCE function that merges all intermediate values associated with the same intermediate key
- 12. Pros of Hadoop 1. Computing power 2. Flexibility 3. Fault Tolerance 4. Low Cost 5. Scalability
- 13. Cons of Hadoop 1. Integration with existing systems Hadoop is not optimised for ease for use. Installing and integrating with existing databases might prove to be difficult, especially since there is no software support provided. 2. Administration and ease of use Hadoop requires knowledge of MapReduce, while most data practitioners use SQL. This means significant training may be required to administer Hadoop clusters. 3. Security Hadoop lacks the level of security functionality needed for safe enterprise deployment, especially if it concerns sensitive data.