Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Big Data Characteristics And Process PowerPoint Presentation SlidesSlideTeam
We present you content-ready big data characteristics and process PowerPoint presentation that can be used to present content management techniques. It can be presented by IT consulting and analytics firms to their clients or company’s management. This relational database management PPT design comprises of 53 slides including introduction, facts, how big is big data, market forecast, sources, 3Vs and 5Vs small Vs big data, objective, technologies, workflow, four phases, types, information analytics process, impact, benefits, future, opportunities and challenges etc. Our data transformation PowerPoint templates are apt to present various topics such as information management concepts and technologies, transforming facts with intelligence, data analysis framework, data mining, technology platforms, data transfer and visualization, content management, Internet of things, data storage and analysis, information infrastructure, datasets, technology and cloud computing. Download big data characteristics and process PPT graphics to make an impressive presentation. Develop greater goodwill with our Big Data Characteristics And Process PowerPoint Presentation Slides. Folks feel friendlier towards you.
The new age big data technologies include predictive analytics, no SQL databases, search and knowledge discovery, stream analytics, in-memory data fabric, data virtualization and more.
Detailed description of big data, with the characteristics of it. What are the limitations of the traditional systems? Where we are using big data? And also the applications of big data.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Big Data Characteristics And Process PowerPoint Presentation SlidesSlideTeam
We present you content-ready big data characteristics and process PowerPoint presentation that can be used to present content management techniques. It can be presented by IT consulting and analytics firms to their clients or company’s management. This relational database management PPT design comprises of 53 slides including introduction, facts, how big is big data, market forecast, sources, 3Vs and 5Vs small Vs big data, objective, technologies, workflow, four phases, types, information analytics process, impact, benefits, future, opportunities and challenges etc. Our data transformation PowerPoint templates are apt to present various topics such as information management concepts and technologies, transforming facts with intelligence, data analysis framework, data mining, technology platforms, data transfer and visualization, content management, Internet of things, data storage and analysis, information infrastructure, datasets, technology and cloud computing. Download big data characteristics and process PPT graphics to make an impressive presentation. Develop greater goodwill with our Big Data Characteristics And Process PowerPoint Presentation Slides. Folks feel friendlier towards you.
The new age big data technologies include predictive analytics, no SQL databases, search and knowledge discovery, stream analytics, in-memory data fabric, data virtualization and more.
Detailed description of big data, with the characteristics of it. What are the limitations of the traditional systems? Where we are using big data? And also the applications of big data.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
In this paper, we discuss about the Big Data. We
analyze and reveals the benefits of Big Data. We analyze the
big data challenges and how Hadoop gives solution to it. This
research paper gives the comparison between relational
databases and Hadoop. This research paper also gives reason
of why Big Data and Hadoop.
General Terms
Data Explosion, Big Data, Big Data Analytics, Hadoop, Hadoop
Distributed File System, MapReduce
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
Big Data and Big Data Management (BDM) with current Technologies –ReviewIJERA Editor
The emerging phenomenon called ―Big Data‖ is pushing numerous changes in businesses and several other organizations, Domains, Fields, areas etc. Many of them are struggling just to manage the massive data sets. Big data management is about two things - ―Big data‖ and ―Data Management‖ and these terms work together to achieve business and technology goals as well. In previous few years data generation have tremendously enhanced due to digitization of data. Day by day new computer tools and technologies for transmission of data among several computers through Internet is been increasing. It‗s relevance and importance in the context of applicability, usefulness for decision making, performance improvement etc in all areas have emerged very fast to be relevant in today‗s era. Big data management also has numerous challenges and common complexities include low organizational maturity relative to big data, weak business support, and the need to learn new technology approaches. This paper will discuss the impacts of Big Data and issues related to data management using current technologies
Hadoop was born out of the need to process Big Data.Today data is being generated liked never before and it is becoming difficult to store and process this enormous volume and large variety of data, In order to cope this Big Data technology comes in.Today Hadoop software stack is go-to framework for large scale,data intensive storage and compute solution for Big Data Analytics Applications.The beauty of Hadoop is that it is designed to process large volume of data in clustered commodity computers work in parallel.Distributing the data that is too large across the nodes in clusters solves the problem of having too large data sets to be processed onto the single machine.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
Data is not consistent, sometimes searches or general interest in certain topics, say social media or other types of data experienced peaks and valleys. Data analysis techniques allow the data scientist to mine this type of unstable data and still draw meaningful conclusions from it.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
2. Acknowledgement
We would like to express our most sincere gratitude and appreciation to
our respected teacher Mr.Vinay Arora Sir for his guidance, patience and
encouragement throughout the development of the presentation.
Thank you Sir for being a constant source of inspiration throughout this
tedious process.
3. Table of Contents
1. Traditional Approach
2. The Beginning
3. What is Big Data
4. Characteristic of Big Data
5. Why Big Data
6. Big Data Analytics
7. Big Players
8. Hadoop as an Example
9. Components of Hadoop
10.References
4. The Beginning…
Big data burst upon the scene in the first
decade of the 21st century.
The first organizations to embrace it were
online and startup firms.
Firms like Google, eBay, LinkedIn and
Facebook were built around big data
from the beginning.
Big Data may well be the Next Big Thing
in the IT world.
Like many new information
technologies, big data can bring about
dramatic cost reductions, substantial
improvements in the time required to
perform a computing task and other
service offerings.
5. Traditional Approach
In this approach, an enterprise used to have a
computer to store and process big data.
Here data was stored in an RDBMS like Oracle
Database, MS SQL Server or DB2 .
Sophisticated softwares were written to interact with
the database, process the required data and present
it to the users.
This approach works well where we have less volume
of data that can be accommodated by standard
database servers.
6. What is Big Data
‘Big Data’ is similar to ‘small data’, but bigger in size
Big Data refers to technologies and initiatives that involve data that is too
diverse, fast-changing or massive for conventional technologies, skills and
infra- structure to address efficiently.
Big Data generates value from the storage and processing of very large
quantities of digital information that cannot be analyzed with traditional
computing techniques.
8. Volume
Big data implies enormous volumes of data.
Big Data requires processing high volumes of
low-density data, that is, data of unknown
value, such as twitter data feeds, clicks on a
web page, network traffic, sensor-enabled
equipment capturing data at the speed of
light and many more.
Today, Facebook ingests 500 terabytes of
new data every day.
A Boeing 737 will generate 240 terabytes of
flight data during a single flight across the US.
Every 2 days we create as much data as we
did from the beginning of time until 2003.
9. Velocity
It refers to the speed at which new data
is generated and the speed at which
data moves around.
Big data technology now allows us to
analyse the data while it is being
generated without ever putting it into
databases.
Machine to machine processes
exchange data between billions of
devices.
Infrastructure and sensors generate
massive log data in real-time.
On-line gaming systems support millions
of concurrent users, each producing
multiple inputs per second.
10. Variety
It refers to the many sources and types of data both structured and
unstructured.
Traditional database systems were designed to address smaller volumes of
structured data, fewer updates or a predictable, consistent data structure.
Now data comes in the form of emails, photos, videos, monitoring devices,
PDFs, audio, etc. This variety of unstructured data creates problems for
storage, mining and analyzing data.
The real world have data in many different formats and that is the
challenge we need to overcome with the Big Data.
11. Veracity
Veracity refers to the messiness or trustworthiness of the data.
With many forms of big data, quality and accuracy are less controllable,
for example Twitter posts with hashtags, abbreviations, typos and colloquial
speech.
Big data and analytics technology now allows us to work with these types
of data. The volumes often make up for the lack of quality or accuracy.
12. Sources of Big Data
Today organizations are utilizing, sharing and storing
more information in varying formats including:
E-mail and Instant Messaging
Social media channels
Video and audio files
This unstructured data adds up to as much as 85% of the
information that businesses store.
The ability to extract high value from this data to enable
innovation and competitive gain is the purpose of Big
Data analytics.
13. Big Data Analytics
Big data is really critical to our life and its emerging as
one of the most important technologies in modern
world.
Using the information kept in the social networking sites
like Facebook, the marketing agencies are learning
about the response for their campaigns, promotions and
other advertising mediums.
Analyzing the data like preferences and product
perception of their consumers, product companies and
retail organizations are planning their production.
Using the data regarding the previous medical history of
patients, hospitals are providing better and quick
service.
15. Hadoop
Hadoop is an open-source framework that allows to store and
process big data in a distributed environment 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.
Doug Cutting took the solution provided by Google and started an
Open Source Project called HADOOP in 2005
Operates on unstructured and structured data.
A large and active ecosystem.
Open source under the Apache License.
16.
17. Hadoop Distributed File System
Data is organized into files and directories
Files are divided into blocks,distributed across nodes.
Blocks replicated to handle failure
Reliable,redundant,distributed file system optimized for large files
18. MapReduce
The MapReduce framework consists of a single JobTracker and several
TaskTrackers in a cluster.
The JobTracker is responsible for resource management, tracking resource
consumption/availability and scheduling the job component tasks onto the
data nodes.
The TaskTracker execute the tasks as directed by the JobTracker and
provide task-status information periodically.