This document provides an overview of big data concepts, technologies, and data scientists. It discusses how big data has outpaced traditional data warehousing and business intelligence technologies due to the increasing volumes, varieties, and velocities of data. It introduces Hadoop as an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop like HDFS and MapReduce are explained at a high level. The document also discusses related open source projects that extend Hadoop's capabilities.
Hadoop Institutes: kelly technologies are the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
http://www.kellytechno.com/Hyderabad/Course/Hadoop-Training
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Hadoop Institutes: kelly technologies are the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
http://www.kellytechno.com/Hyderabad/Course/Hadoop-Training
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
The MapReduce model has become an important parallel processing model for large- scale data-intensive applications like data mining and web indexing. Hadoop, an open-source implementation of MapReduce, is widely applied to support cluster computing jobs requiring low response time. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into account for launching speculative map tasks, because it is assumed that most map tasks can quickly access their local data. Network delays due to data movement during running time have been ignored in the recent Hadoop research. Unfortunately, both the homogeneity and data locality assumptions in Hadoop are optimistic at best and unachievable at worst, potentially introducing performance problems in virtualized data centers. We show in this dissertation that ignoring the data-locality issue in heterogeneous cluster computing environments can noticeably reduce the performance of Hadoop. Without considering the network delays, the performance of Hadoop clusters would be significatly downgraded. In this dissertation, we address the problem of how to place data across nodes in a way that each node has a balanced data processing load. Apart from the data placement issue, we also design a prefetching and predictive scheduling mechanism to help Hadoop in loading data from local or remote disks into main memory. To avoid network congestions, we propose a preshuffling algorithm to preprocess intermediate data between the map and reduce stages, thereby increasing the throughput of Hadoop clusters. Given a data-intensive application running on a Hadoop cluster, our data placement, prefetching, and preshuffling schemes adaptively balance the tasks and amount of data to achieve improved data-processing performance. Experimental results on real data-intensive applications show that our design can noticeably improve the performance of Hadoop clusters. In summary, this dissertation describes three practical approaches to improving the performance of Hadoop clusters, and explores the idea of integrating prefetching and preshuffling in the native Hadoop system.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
This presentation helps you understand the basics of Hadoop.
What is Big Data?? How google search so fast and what is MapReduce algorithm? all these questions will be answered in the presentation.
Apache Hadoop, since its humble beginning as an execution engine for web crawler and building search indexes, has matured into a general purpose distributed application platform and data store. Large Scale Machine Learning (LSML) techniques and algorithms proved to be quite tricky for Hadoop to handle, ever since we started offering Hadoop as a service at Yahoo in 2006. In this talk, I will discuss early experiments of implementing LSML algorithms on Hadoop at Yahoo. I will describe how it changed Hadoop, and led to generalization of the Hadoop platform to accommodate programming paradigms other than MapReduce. I will unveil some of our recent efforts to incorporate diverse LSML runtimes into Hadoop, evolving it to become *THE* LSML platform. I will also make a case for an industry-standard LSML benchmark, based on common deep analytics pipelines that utilize LSML workload.
A presentation on Hadoop for scientific researchers given at Universitat Rovira i Virgili in Catalonia, Spain in October 2010. http://etseq.urv.cat/seminaris/seminars/3/
The MapReduce model has become an important parallel processing model for large- scale data-intensive applications like data mining and web indexing. Hadoop, an open-source implementation of MapReduce, is widely applied to support cluster computing jobs requiring low response time. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into account for launching speculative map tasks, because it is assumed that most map tasks can quickly access their local data. Network delays due to data movement during running time have been ignored in the recent Hadoop research. Unfortunately, both the homogeneity and data locality assumptions in Hadoop are optimistic at best and unachievable at worst, potentially introducing performance problems in virtualized data centers. We show in this dissertation that ignoring the data-locality issue in heterogeneous cluster computing environments can noticeably reduce the performance of Hadoop. Without considering the network delays, the performance of Hadoop clusters would be significatly downgraded. In this dissertation, we address the problem of how to place data across nodes in a way that each node has a balanced data processing load. Apart from the data placement issue, we also design a prefetching and predictive scheduling mechanism to help Hadoop in loading data from local or remote disks into main memory. To avoid network congestions, we propose a preshuffling algorithm to preprocess intermediate data between the map and reduce stages, thereby increasing the throughput of Hadoop clusters. Given a data-intensive application running on a Hadoop cluster, our data placement, prefetching, and preshuffling schemes adaptively balance the tasks and amount of data to achieve improved data-processing performance. Experimental results on real data-intensive applications show that our design can noticeably improve the performance of Hadoop clusters. In summary, this dissertation describes three practical approaches to improving the performance of Hadoop clusters, and explores the idea of integrating prefetching and preshuffling in the native Hadoop system.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
This presentation helps you understand the basics of Hadoop.
What is Big Data?? How google search so fast and what is MapReduce algorithm? all these questions will be answered in the presentation.
Apache Hadoop, since its humble beginning as an execution engine for web crawler and building search indexes, has matured into a general purpose distributed application platform and data store. Large Scale Machine Learning (LSML) techniques and algorithms proved to be quite tricky for Hadoop to handle, ever since we started offering Hadoop as a service at Yahoo in 2006. In this talk, I will discuss early experiments of implementing LSML algorithms on Hadoop at Yahoo. I will describe how it changed Hadoop, and led to generalization of the Hadoop platform to accommodate programming paradigms other than MapReduce. I will unveil some of our recent efforts to incorporate diverse LSML runtimes into Hadoop, evolving it to become *THE* LSML platform. I will also make a case for an industry-standard LSML benchmark, based on common deep analytics pipelines that utilize LSML workload.
A presentation on Hadoop for scientific researchers given at Universitat Rovira i Virgili in Catalonia, Spain in October 2010. http://etseq.urv.cat/seminaris/seminars/3/
Language Technologies for Big Data – A Strategic Agenda for the Multilingual ...Georg Rehm
Georg Rehm. Language Technologies for Big Data – A Strategic Agenda for the Multilingual Digital Single Market. BDVA Summit (Big Data Value Association), Valencia, Spain, December 2016. December 1, 2016.
Big Data: Technical Introduction to BigSheets for InfoSphere BigInsightsCynthia Saracco
Introduces BigSheets, a spreadsheet-style tool for business users working with Big Data. BigSheets is part of IBM's InfoSphere BigInsights platform, which is based on open source technologies (e.g., Apache Hadoop) and IBM-specific technologies.
John Sing's Edge 2013 presentation, detailing when/where/how external storage products and/or system software (i.e. GPFS) can be effectively used in a Hadoop storage environment. Many Hadoop situations absolutely required direct attached storage. However, there are many intelligent situations where shared external storage may make sense in a Hadoop environment. This presentation details how/why/where, and promotes taking an intelligent, Hadoop-aware approach to deciding between internal storage and external shared storage. Having full awareness of Hadoop considerations is essential to selecting either internal or external shared storage in Hadoop environment.
Bridging the Big Data Gap in the Software-Driven WorldCA Technologies
Implementing and managing a Big Data environment effectively requires essential efficiencies such as automation, performance monitoring and flexible infrastructure management. Discover new innovations that enable you to manage entire Big Data environments with unparalleled ease of use and clear enterprise visibility across a variety of data repositories.
To learn more about Mainframe solutions from CA Technologies, visit: http://bit.ly/1wbiPkl
The Transformation of your Data in modern IT (Presented by DellEMC)Cloudera, Inc.
Organizations have a wealth of data contained within the existing infrastructures. At DellEMC we’re helping customers remove the barriers of legacy datastores and transforming the customer experience in the modern datacentre. Learn how to unshackle the valuable data inside your existing data warehouse, leverage new techniques, applications and technology to enhance the financial impact of all your data sources
A brief intro on the idea of what is Big Data and it's potential. This is primarily a basic study & I have quoted the source of infographics, stats & text at the end. If I have missed any reference due to human error & you recognize another source, please mention.
Extracting value from Big Data is not easy. The field of technologies and vendors is fragmented and rapidly evolving. End-to-end, general purpose solutions that work out of the box don’t exist yet, and Hadoop is no exception. And most companies lack Big Data specialists. The key to unlocking real value lies with thinking smart and hard about the business requirements for a Big Data solution. There is a long list of crucial questions to think about. Is Hadoop really the best solution for all Big Data needs? Should companies run a Hadoop cluster on expensive enterprise-grade storage, or use cheap commodity servers? Should the chosen infrastructure be bare metal or virtualized? The picture becomes even more confusing at the analysis and visualization layer. The answer to Big Data ROI lies somewhere between the herd and nerd mentality. Thinking hard and being smart about each use case as early as possible avoids costly mistakes in choosing hardware and software. This talk will illustrate how Deutsche Telekom follows this segmentation approach to make sure every individual use case drives architecture design and the selection of technologies and vendors.
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization Denodo
Watch here: https://bit.ly/2NGQD7R
In an era increasingly dominated by advancements in cloud computing, AI and advanced analytics it may come as a shock that many organizations still rely on data architectures built before the turn of the century. But that scenario is rapidly changing with the increasing adoption of real-time data virtualization - a paradigm shift in the approach that organizations take towards accessing, integrating, and provisioning data required to meet business goals.
As data analytics and data-driven intelligence takes centre stage in today’s digital economy, logical data integration across the widest variety of data sources, with proper security and governance structure in place has become mission-critical.
Attend this session to learn:
- Learn how you can meet cloud and data science challenges with data virtualization.
- Why data virtualization is increasingly finding enterprise-wide adoption
- Discover how customers are reducing costs and improving ROI with data virtualization
Exploring the Wider World of Big Data- Vasalis KapsalisNetAppUK
Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.
Getting started with Hadoop on the Cloud with BluemixNicolas Morales
Silicon Valley Code Camp -- October 11, 2014.
Session: Getting started with Hadoop on the Cloud.
Hadoop and Cloud is an almost perfect marriage. Hadoop is a distributed computing framework that leverages a cluster built on commodity hardware. The Cloud simplifies provisioning of machines and software. Getting started with Hadoop on the Cloud makes it simple to provision your environment quickly and actually get started using Hadoop. IBM Bluemix has democratized Hadoop for the masses! This session will provide a brief introduction to what Hadoop is, how does cloud work and will then focus on how to get started via a series of demos. We will conclude with a discussion around the tutorials and public datasets - all of the tools needed to get you started quickly.
Learn more about BigInsights for Hadoop: https://developer.ibm.com/hadoop/
Developed by Google’s Artificial Intelligence division, the Sycamore quantum processor boasts 53 qubits1.
In 2019, it achieved a feat that would take a state-of-the-art supercomputer 10,000 years to accomplish: completing a specific task in just 200 seconds1
Fueling AI & Machine Learning: Legacy Data as a Competitive AdvantagePrecisely
The data fueling your AI or machine learning initiatives plays a critical role. Different data sources provide different outcomes. The most important thing a business can do to prepare for success with AI and machine learning is to understand and provide access to all of the data that you can possibly get to. In addition to newer data sources, like IoT and Social Media, what will set your results apart – and give your business a competitive advantage – is powering AI and machine learning with your historical and proprietary data: the data sitting in your mainframe, legacy, and other traditional systems.
View this on-demand webcast with Wikibon Analyst James Kobielus as we discuss:
• Using your historical customer data to train predictive AI/ML models for effective target marketing
• Leveraging social, mobile, and IoT data to give your marketing an extra level of personalization
• Making the most of your legacy and proprietary data while protecting customer privacy and ensuring regulatory compliance
Are you confused by Big Data? Get in touch with this new "black gold" and familiarize yourself with undiscovered insights through our complimentary introductory lesson on Big Data and Hadoop!
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
33. Pig, Hive, Jaql – Differences
Characteristic Pig Hive Jaql
Developed by Yahoo! Facebook IBM
Language Pig Latin HiveQL Jaql
Type of
language Data flow Declarative (SQL
dialect) Data flow
Data structures
supported Complex Better suited for
structured data
JSON, semi
structured
Schema Optional Not optional Optional
Sistemas transaccionales maduros, con años de historia, los datos van creciendo. De Gigabytes a Terabytes
Obstancles- Latency, user concurrency, single threaded
How do this without having companies hire specialists who know how to query Hadoop using Java or overcome latency.
Latency: via Hcatalog –
Query: Better interfaces
Won’t fix things like user concurrency –
THIS IS ASPIRATION BUT LOTS OF OBSTACLES PREVENTING –
- Latency via batch, user concurrency cause no workload mgmt or prioritization or query optimizer
Know coding
Skills, or more accurately a shortage of skills, is widely recognized as the leading inhibitor to the broader acceptance of Big Data solutions in the enterprise. To address the skills shortage IBM has sponsored community-driven effort to deliver Big Data education regardless of physical location or budget. We call this @your place, @your pace education and it has turned out to be a huge success with well over 8000 registered students and over 1800 students enrolled in the Hadoop Fundamentals class alone. We have seen people gain sufficient skills in a matter of a week to enter and complete the Hadoop Programming Challenge by submitting very innovative solutions. IBM’s sponsorship provides BigDataUniversity.com participants with a comprehensive set of education materials, access to free products for hands on labs and a cloud-based course management system to make the learning process easy and fun.