In next 3-5 year this technology will play a major role because data is growing in uncontrolled way like data generated from intelligent systems,social networks ,mobile phones leads and in last 5 years that generation has doubled and this leads to big data .
big data has 3 characteristics calles 3v's that is
velocity of data.
variety of data
variety of data
Big data problem is solved by hadoop which uses hadoop file distribution system(provides huge storage in distributed form) and through map reduce framework it solves the need of high computing power.
In next 3-5 year this technology will play a major role because data is growing in uncontrolled way like data generated from intelligent systems,social networks ,mobile phones leads and in last 5 years that generation has doubled and this leads to big data .
big data has 3 characteristics calles 3v's that is
velocity of data.
variety of data
variety of data
Big data problem is solved by hadoop which uses hadoop file distribution system(provides huge storage in distributed form) and through map reduce framework it solves the need of high computing power.
Hadoop is a framework for running applications on large clusters built of commodity hardware.The Hadoop framework transparently provides applications both reliability and data motion. Hadoop implements a computational paradigm named Map/Reduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. In addition, it provides a distributed file system (HDFS) that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Both Map/Reduce and the distributed file system are designed so that node failures are automatically handled by the framework.
My recent presentation about what is Big Data, Why so much Hype now, Startling Facts, Opportunity, History, Important Research Papers such as GFS, Map-Reduce , Technology Platforms and Organizations , Hadoop, Cassandra, Introduction to Hadoop, Contribution of Indians to various Big Data technologies working in Google, Cloudera, Hortonworks, Yahoo, Facebook, Aadhar - "All your answers lie in data - @Sameer Sawhney"
Twister4Azure is an iterative MapReduce framework, which support development and execution of Iterative MapReduce and traditional MapReduce application in Microsoft Azure cloud.
Gail Zhou on "Big Data Technology, Strategy, and Applications"Gail Zhou, MBA, PhD
Dr. Gail Zhou presented this topic at DevNexus on Feb 25, 2014. Big Data history, opportunities, and applications. Big Data key concepts, reference architecture with open source technology stacks. Hadoop architecture explained (HDFS, Map Reduce, and YARN). Big Data start-up challenges and strategies to overcome them. Technology update: Hadoop and Cassandra based technology offerings.
Big Data Meets HPC - Exploiting HPC Technologies for Accelerating Big Data Pr...inside-BigData.com
DK Panda from Ohio State University presented this deck at the Switzerland HPC Conference.
"This talk will provide an overview of challenges in accelerating Hadoop, Spark and Mem- cached on modern HPC clusters. An overview of RDMA-based designs for multiple com- ponents of Hadoop (HDFS, MapReduce, RPC and HBase), Spark, and Memcached will be presented. Enhanced designs for these components to exploit in-memory technology and parallel file systems (such as Lustre) will be presented. Benefits of these designs on various cluster configurations using the publicly available RDMA-enabled packages from the OSU HiBD project (http://hibd.cse.ohio-state.edu) will be shown."
Watch the video presentation: https://www.youtube.com/watch?v=glf2KITDdVs
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Real time big data analytical architecture for remote sensing applicationLeMeniz Infotech
Real time big data analytical architecture for remote sensing application
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
"Quantum Clustering - Physics Inspired Clustering Algorithm", Sigalit Bechler...Dataconomy Media
"Quantum Clustering - Physics Inspired Clustering Algorithm", Sigalit Bechler, Researcher at Similar Web
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Sigalit Bechler is a data science researcher with a diverse academic background - a B.Sc. in electrical engineering, a B.Sc. in physics (cum laude) from Tel Aviv University's prestigious program for parallel B.Sc. in Physics and in Electrical Engineering, an M.Sc. in condensed matter (cum laude), and have started her Ph.D. in bioinformatics. Prior to her M.Sc. I have served as a captain in a technology unit of the IDF. She is passionate about science and solving complex big data problems that require out of the box thinking, and like to dive deep into the details. She always take a positive, proactive approach, and put an emphasis on understanding the big picture as well.
Vodafone, Cyberpark ve Türkiye Teknoloji Geliştirme Vakfı işbirliğinde düzenlen etkinlikte büyük veri kavramı, Apache Hadoop Ekosistemi ve Türkiye ve Dünyadaki örnek uygulamalar anlatıldı.
-
1 Haziran 2016 - Onur Karadeli, Mustafa Murat Sever
Hadoop is a framework for running applications on large clusters built of commodity hardware.The Hadoop framework transparently provides applications both reliability and data motion. Hadoop implements a computational paradigm named Map/Reduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. In addition, it provides a distributed file system (HDFS) that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Both Map/Reduce and the distributed file system are designed so that node failures are automatically handled by the framework.
My recent presentation about what is Big Data, Why so much Hype now, Startling Facts, Opportunity, History, Important Research Papers such as GFS, Map-Reduce , Technology Platforms and Organizations , Hadoop, Cassandra, Introduction to Hadoop, Contribution of Indians to various Big Data technologies working in Google, Cloudera, Hortonworks, Yahoo, Facebook, Aadhar - "All your answers lie in data - @Sameer Sawhney"
Twister4Azure is an iterative MapReduce framework, which support development and execution of Iterative MapReduce and traditional MapReduce application in Microsoft Azure cloud.
Gail Zhou on "Big Data Technology, Strategy, and Applications"Gail Zhou, MBA, PhD
Dr. Gail Zhou presented this topic at DevNexus on Feb 25, 2014. Big Data history, opportunities, and applications. Big Data key concepts, reference architecture with open source technology stacks. Hadoop architecture explained (HDFS, Map Reduce, and YARN). Big Data start-up challenges and strategies to overcome them. Technology update: Hadoop and Cassandra based technology offerings.
Big Data Meets HPC - Exploiting HPC Technologies for Accelerating Big Data Pr...inside-BigData.com
DK Panda from Ohio State University presented this deck at the Switzerland HPC Conference.
"This talk will provide an overview of challenges in accelerating Hadoop, Spark and Mem- cached on modern HPC clusters. An overview of RDMA-based designs for multiple com- ponents of Hadoop (HDFS, MapReduce, RPC and HBase), Spark, and Memcached will be presented. Enhanced designs for these components to exploit in-memory technology and parallel file systems (such as Lustre) will be presented. Benefits of these designs on various cluster configurations using the publicly available RDMA-enabled packages from the OSU HiBD project (http://hibd.cse.ohio-state.edu) will be shown."
Watch the video presentation: https://www.youtube.com/watch?v=glf2KITDdVs
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Real time big data analytical architecture for remote sensing applicationLeMeniz Infotech
Real time big data analytical architecture for remote sensing application
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
Web : http://www.lemenizinfotech.com
Web : http://www.ieeemaster.com
Mail : projects@lemenizinfotech.com
Blog : http://ieeeprojectspondicherry.weebly.com
Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
"Quantum Clustering - Physics Inspired Clustering Algorithm", Sigalit Bechler...Dataconomy Media
"Quantum Clustering - Physics Inspired Clustering Algorithm", Sigalit Bechler, Researcher at Similar Web
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Sigalit Bechler is a data science researcher with a diverse academic background - a B.Sc. in electrical engineering, a B.Sc. in physics (cum laude) from Tel Aviv University's prestigious program for parallel B.Sc. in Physics and in Electrical Engineering, an M.Sc. in condensed matter (cum laude), and have started her Ph.D. in bioinformatics. Prior to her M.Sc. I have served as a captain in a technology unit of the IDF. She is passionate about science and solving complex big data problems that require out of the box thinking, and like to dive deep into the details. She always take a positive, proactive approach, and put an emphasis on understanding the big picture as well.
Vodafone, Cyberpark ve Türkiye Teknoloji Geliştirme Vakfı işbirliğinde düzenlen etkinlikte büyük veri kavramı, Apache Hadoop Ekosistemi ve Türkiye ve Dünyadaki örnek uygulamalar anlatıldı.
-
1 Haziran 2016 - Onur Karadeli, Mustafa Murat Sever
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
This is an updated version of Amr's Hadoop presentation. Amr gave this talk recently at NASA CIDU event, TDWI LA Chapter, and also Netflix HQ. You should watch the powerpoint version as it has animations. The slides also include handout notes with additional information.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
4. What is ?
It is an open source project by the
Apache Foundation to handle large
data processing
It was inspired by Google’s MapReduce
and Google File System (GFS) papers
It was originally conceived by Doug
Cutting
It is named after his son’s pet elephant
incidentally
6. So what’s the big deal?
Scalable: New nodes can be added as
needed, without changing the formats
Flexible: It is schema-less, and can
absorb any type of data, structured or
not, from any number of sources
Fault tolerant: System redirects work to
another location if a node fails
7. Hadoop = HDFS + MapReduce
HDFS: For storing massive datasets
using low-cost storage
MapReduce: The algorithm on which
Google built its empire
8. HDFS
It is a fault-tolerant storage system
Able to store huge amounts of
information
It creates clusters of machines and
coordinates work among them
If one fails, it continues to operate the
cluster without losing data or interrupting
work, by shifting work to the remaining
machines in the cluster
9. HDFS
It manages storage on the cluster by
breaking incoming files into
pieces, called blocks
Stores each of the blocks redundantly
across the pool of servers
It stores three complete copies of each
file by copying each piece to three
different servers
Hadoopis only one part under Apache FoundationAccording to IDC, the amount digital information produced in 2012 will be ten times that produced in 2006: 1800 exabytesThe majority of this data will be “unstructured” – complex data poorly-suited to management by structured storage systems like relational databases
1 Petabyte [where most SME corporations are?]1 Exabyte [where most large corporations are?]1 Zettabyte [where leaders like Facebook and Google are]
-Flexible: Data from multiple sources can be joined and aggregated in arbitrary ways enabling deeper analyses than any one system can provide.80% of the world’s data is unstructured, and most businesses don’t even attempt to use this data to their advantage. Imagine if you had a way to analyze that data?
HDFS assumes nodes will fail, so it achieves reliability by replicating data across multiple nodesMapReduce: It refers to two separate and distinct tasks that Hadoop programs perform. The first is the map job, which takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). The reduce job takes the output from a map as input and combines those data tuples into smaller set of tuples. As the sequence of the name MapReduce implies, the reduce job is always performed after a Map.MapReduce was first presented to the world via a 2004 white paper by Google where salient insights were blurt out. Yahoo re-implemented this technique and open sourced it via the Apache foundationAs an analogy, you can think of map and reduce tasks as the way a census was conducted in Roman times, where the census bureau would dispatch its people to each city in the empire. Each census taker in each city would be tasked to count the number of people in that city and then return their results to the capital city. There, the results from each city would be reduced to a single count (sum of all cities) to determine the overall population of the empire. This mapping of people to cities, in parallel, and then combining the results (reducing) is much more efficient than sending a single person to count every person in the empire in a serial fashion.Large volumes of complex data can hide important insights. Are there buying patterns in point-of-sale data that can forecast demand for products a particular stores?Do user logs from a website, or calling records in a mobile network, contain information about relationships among individual customers? Companies that can extract facts like these from the huge volume of data can better control processes and costs, can better predict demand and build better products
HDFS: Hadoop Distributed File SystemMapReduce: Parellel data-processing frameworkHadoop Common: A set of utilities that support the Hadoop subprojectsHbase: Hadoop database for random read/write accessHive: SQL-like queries and tables on large datasetsPig: Data flow language and compilerOozie: Workflow for interdependent Hadoop jobsSqoop: Integration of databases and data warehouses with HadoopFlume: Configurable streaming data collectionZookeeper: Coordination service for distributed applicationsHue: User interface framework and SDK for visual Hadoop applications
In the very simple example shown, any two servers can fail, and the entire file will still be available. HDFS notices when a block or a node is lost, and creates a new copy of missing data from the replicas it manages. Because the cluster stores several copies of every block, more clients can read them at the same time without creating bottlenecks.
Each of the server runs the analysis on its own block from the file. Results are collated and digested into a single result after each piece has been analyzedRunning the analysis on the nodes that actually store the data delivers much better performance than reading data over the network from a single centralized serverIt monitors jobs during execution, and will restart work lost due to node failure if necessary. In fact, if a particular node is running very slowly, it will restart its work on another server with a copy of the data
All above companies are using for variety of tasks like marketing, advertising, and sentiment and risk analysis. IBM used the software as the engine for its Watson computer, which competed with the champions of TV game show Jeopardy.
Foursquare aimed at letting your friends in almost every country know where you are and figuring where they are.As a platform, it is now aware of 25+ million venues worldwide, each of which can be described by unique signals about who is coming to these places, when, and for how long. To reward and incent users foursquare allows frequent users to collect points, prize “badges,” and eventually coupons, for check-ins