This document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
Big Data may well be the Next Big Thing in the IT world. 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 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.
Big Data may well be the Next Big Thing in the IT world. 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 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.
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
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.
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Disclaimer :
The images, company, product and service names that are used in this presentation, are for illustration purposes only. All trademarks and registered trademarks are the property of their respective owners.
Data/Image collected from various sources from Internet.
Intention was to present the big picture of Big Data & Hadoop
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
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.
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Disclaimer :
The images, company, product and service names that are used in this presentation, are for illustration purposes only. All trademarks and registered trademarks are the property of their respective owners.
Data/Image collected from various sources from Internet.
Intention was to present the big picture of Big Data & Hadoop
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.