It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
ارائه در زمینه کلان داده،
کارگاه آموزشی "عصر کلان داده، چرا و چگونه؟" در بیست و دومین کنفرانس انجمن کامپیوتر ایران csicc2017.ir
وحید امیری
vahidamiry.ir
datastack.ir
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
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
A review of slicing techniques in software engineeringSalam Shah
Program slice is the part of program that may take the program off the path of the
desired output at some point of its execution. Such point is known as the slicing criterion.
This point is generally identified at a location in a given program coupled with the subset
of variables of program. This process in which program slices are computed is called
program slicing. Weiser was the person who gave the original definition of program slice
in 1979. Since its first definition, many ideas related to the program slice have been
formulated along with the numerous numbers of techniques to compute program slice.
Meanwhile, distinction between the static slice and dynamic slice was also made.
Program slicing is now among the most useful techniques that can fetch the particular
elements of a program which are related to a particular computation. Quite a large
numbers of variants for the program slicing have been analyzed along with the
algorithms to compute the slice. Model based slicing split the large architectures of
software into smaller sub models during early stages of SDLC. Software testing is
regarded as an activity to evaluate the functionality and features of a system. It verifies
whether the system is meeting the requirement or not. A common practice now is to
extract the sub models out of the giant models based upon the slicing criteria. Process of
model based slicing is utilized to extract the desired lump out of slice diagram. This
specific survey focuses on slicing techniques in the fields of numerous programing
paradigms like web applications, object oriented, and components based. Owing to the
efforts of various researchers, this technique has been extended to numerous other
platforms that include debugging of program, program integration and analysis, testing
and maintenance of software, reengineering, and reverse engineering. This survey
portrays on the role of model based slicing and various techniques that are being taken
on to compute the slices.
ارائه در زمینه کلان داده،
کارگاه آموزشی "عصر کلان داده، چرا و چگونه؟" در بیست و دومین کنفرانس انجمن کامپیوتر ایران csicc2017.ir
وحید امیری
vahidamiry.ir
datastack.ir
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
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
A review of slicing techniques in software engineeringSalam Shah
Program slice is the part of program that may take the program off the path of the
desired output at some point of its execution. Such point is known as the slicing criterion.
This point is generally identified at a location in a given program coupled with the subset
of variables of program. This process in which program slices are computed is called
program slicing. Weiser was the person who gave the original definition of program slice
in 1979. Since its first definition, many ideas related to the program slice have been
formulated along with the numerous numbers of techniques to compute program slice.
Meanwhile, distinction between the static slice and dynamic slice was also made.
Program slicing is now among the most useful techniques that can fetch the particular
elements of a program which are related to a particular computation. Quite a large
numbers of variants for the program slicing have been analyzed along with the
algorithms to compute the slice. Model based slicing split the large architectures of
software into smaller sub models during early stages of SDLC. Software testing is
regarded as an activity to evaluate the functionality and features of a system. It verifies
whether the system is meeting the requirement or not. A common practice now is to
extract the sub models out of the giant models based upon the slicing criteria. Process of
model based slicing is utilized to extract the desired lump out of slice diagram. This
specific survey focuses on slicing techniques in the fields of numerous programing
paradigms like web applications, object oriented, and components based. Owing to the
efforts of various researchers, this technique has been extended to numerous other
platforms that include debugging of program, program integration and analysis, testing
and maintenance of software, reengineering, and reverse engineering. This survey
portrays on the role of model based slicing and various techniques that are being taken
on to compute the slices.
Закон об онлайн-кассах и сроки его реализацииMoySklad
Презентация Аскара Рахимбердиева, генерального директора сервиса МойСклад, с семинара, посвященного введению онлайн-касс, который прошел в Екатеринбурге 8.12.2016.
Закон 54-ФЗ вводит новый тип поставщиков услуг для бизнеса — операторы фискальных данных (ОФД). Они будут получать данные с онлайн-касс и передавать их в ФНС. Подключение к ОФД — обязательное требование закона. На семинаре представитель ОФД Такском рассказал о функциях и подключении к этой услуге.
Предпринимателям будет необходимо либо модернизировать существующий кассовый аппарат, либо купить новый. О том, какие кассы подойдут для торговли в новых условиях и как модернизировать старые, рассказал представитель компании АТОЛ, ведущего производителя торгового оборудования.
Изменится и обязательное содержание пробиваемого чека. Теперь просто суммы покупки без название товара будет недостаточно. Поэтому понадобится кассовая программа, которая будет вести справочник товаров, выводить на чек другие обязательные данные и отправлять его по требованию покупателя на мейл или телефон. Подробнее про использование кассового ПО, соответствующего требованиям 54-ФЗ, рассказали организаторы семинара, сервис МойСклад.
Презентация Тригубчук Светланы, компания АТОЛ, с семинара, посвященного введению онлайн-касс, который прошел в Санкт-Петербурге 24.11.2016.
Закон 54-ФЗ вводит новый тип поставщиков услуг для бизнеса — операторы фискальных данных (ОФД). Они будут получать данные с онлайн-касс и передавать их в ФНС. Подключение к ОФД — обязательное требование закона. На семинаре представитель ОФД Такском рассказал о функциях и подключении к этой услуге.
Предпринимателям будет необходимо либо модернизировать существующий кассовый аппарат, либо купить новый. О том, какие кассы подойдут для торговли в новых условиях и как модернизировать старые, рассказал представитель компании АТОЛ, ведущего производителя торгового оборудования.
Изменится и обязательное содержание пробиваемого чека. Теперь просто суммы покупки без название товара будет недостаточно. Поэтому понадобится кассовая программа, которая будет вести справочник товаров, выводить на чек другие обязательные данные и отправлять его по требованию покупателя на мейл или телефон. Подробнее про использование кассового ПО, соответствующего требованиям 54-ФЗ, рассказали организаторы семинара, сервис МойСклад.
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The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
This presentation provides a comprehensive introduction to the Hadoop Distributed System, a powerful and widely used framework for distributed storage and processing of large-scale data. Hadoop has revolutionized the way organizations manage and analyze data, making it a crucial tool in the field of big data and data analytics.
In this presentation, we explore the key components and features of Hadoop, shedding light on the fundamental building blocks that enable its exceptional data processing capabilities. We cover essential topics, including the Hadoop Distributed File System (HDFS), MapReduce, YARN (Yet Another Resource Negotiator), and Hadoop Ecosystem components like Hive, Pig, and Spark.
HADOOP online training by Keylabstraining is excellent and teached by real time faculty. Our Hadoop Big Data course content designed as per the current IT industry requirement. Apache Hadoop is having very good demand in the market, huge number of job openings are there in the IT world. Based on this demand, Keylabstrainings has started providing online classes on Hadoop training through the various online training methods like Gotomeeting.
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Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA, Sybase and SAP BusinessObjects enabling a broad range of new analytic applications.
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
Big-Data Hadoop Tutorials - MindScripts Technologies, Pune amrutupre
MindScripts Technologies, is the leading Big-Data Hadoop Training institutes in Pune, providing a complete Big-Data Hadoop Course with Cloud-Era certification.
2. Where does Big Data come from
Web data
Social Media
Click stream data
Sensor data
Connected Device
3. Big Data Challenges
Size of Big data.
Unstructured or semi structured data.
Analyzing Big data.
4.
5. How Hadoop solves the Big Data
Problem
Hadoop is built on cluster of
machines.
It handles unstructured and semi
structured data.
Hadoop cluster can scale
horizontally to meet storage
requirements .
Hadoop clusters provide both
storage as well as computation.
7. Retail
Challenges :
Were higher priced items selling in certain markets ?
Should inventory be re-allocated or price optimized based on
geography ?
10. Services in Hadoop
Namenode : Stores and maintains the metadata for HDFS
Secondary namenode : Performs housekeeping functions for
namenode
Datanode : Stores actual HDFS data blocks
Jobtracker : Manages MapReduce jobs and distributes individual tasks
to task trackers.
Tasktracker : Responsible to instantiate and monitor Map and reduce
task.
13. Hadoop Fault tolernace
The Data stored in HDFS is replicated to more than one DataNode,
so that even if one data node goes down we have copy of data on
some other node.
The replication factor by default is 3 and is configurable
The namenode is Single Point of Failure in Cluster and hence the
logs and metadata are periodically backed up to secondary
namenode.
14. HDFS – Hadoop Distributed File
System
Hadoop is the distributed file system for storing huge data sets on
the cluster of commodity hardware with streaming data access
pattern.
18. Hadoop Ecosystems Introduction
Sqoop : Imports data from relational databases.
Flume : Collection and import of log and event data.
Map Reduce : Parallel computation on server clusters.
HDFS : Distributed redundant file system for Hadoop
Pig : High level programming language for Hadoop computations.
Hive : Data warehouse with SQL like access
19. Data Processing systems in Hadoop
Batch Processing
Map Reduce
Stream Processing
Apache Spark
Apache Storm