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.
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.
General introduction to Big Data terms and technologies: Velocity, Volume, Variety (3V) and Veracity (4V), NoSQL, Data Science, main data stores (key-value, column, document, graph), Elasticsearch, ...
Presentation of data.be products leveraging Big Data & Elasticsearch
everyone need to some storage and data.this big data is increase the data capacity and processing power.
Big Data may well be the Next Big Thing in the IT world.
• 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.
• Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
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.
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.
General introduction to Big Data terms and technologies: Velocity, Volume, Variety (3V) and Veracity (4V), NoSQL, Data Science, main data stores (key-value, column, document, graph), Elasticsearch, ...
Presentation of data.be products leveraging Big Data & Elasticsearch
everyone need to some storage and data.this big data is increase the data capacity and processing power.
Big Data may well be the Next Big Thing in the IT world.
• 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.
• Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
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
SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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
2. CONTENT
INTRODUCTION
WHAT IS BIG DATA ?
TYPES OF BIG DATA
ARCHITECTURE OF BIG DATA
CHARACTERISTICS OF BIG DATA
BIG DATA – TOOLS
WHY BIG DATA?
HOW IS BIG DATA DIFFERENT ?
WHO’S GENERATING BIG DATA
THE MODEL HAS CHANGED …
CHALLENGES OF BIG DATA
BENEFITS OF BIG DATA
ADVANTAGES AND DISADVANTAGES OF BIG DATA
APPLICATIONS OF BIG DATA
CONCLUSION
3. INTRODUCTION
The term “Big Data” was first introduced to the
computing world by Roger Magoulas from O’Reilly
media in 2005.
Madden define the Big Data as:
"data that’s too big, too fast, or too hard for
existing tools to process”.
The term is used to refer to data management
technologies which have evolved over time.
4. WHAT IS BIG DATA ?
Big Data is a phrase used to
mean a massive volume of
both structured and
unstructured data that is so
large it is difficult to
process using
traditional database and
software techniques.
11. VOLUME
Volume refers to the quantity of data that is
being manipulated and analysed in order to
obtain a desired result.
It is the vast amount of data generated every
second that are larger than what the
conventional relational database
infrastructures can cope with.
Exponential increase in
collected/generated data
12. VELOCITY
“Velocity” is all about the speed the data.
Data is begin generated fast and need to
be processed fast.
EXAMPLE:
Healthcare monitoring: sensors monitoring your
activities and body any abnormal measurements
require immediate reaction
13. VARIETY
“Variety” is the third characteristic of
Big
Data. It represents the type of data
that is
stored, analyzed and used. The type of
data
stored and analyzed varies and it can
consist of location coordinates, video
files,
data sent from browsers, simulations
etc.
14. VALUE
The fourth “V” is “Value” and is all about
the quality of data that is stored and the
further use of it. Large quantity of data is
being stored from mobile phones call
records to TCP/IP logs.
15. VERACITY
“Veracity” is the fifth characteristic of Big
Data and came from the idea that the
possible consistency of data is good
enough for Big Data.
16. BIG DATA - TOOLS
HADOOP
-It was specifically built to handle very large data sets. Hadoop is
made up of two main parts: the Hadoop Distributed File System (HDFS)
and MapReduce.
NoSQL
- NoSQL databases have grown in popularity. These Not Only SQL
databases are not bound by traditional schema models allowing them to
collect unstructured datasets
MPP
- An MPP database is a database that is optimized to be
processed in parallel for many operations to be performed by many
processing units at a time.
17. WHY BIG DATA ?
Growth of Big Data is needed
Increase of storage capacities
Increase of processing power
Availability of data (different data types)
Every day we create 2.5 quintillion bytes of
data; 90% of the data in the world today has been
created in the last two years alone.
18. HOW IS BIG DATA DIFFERENT ?
Automatically generated by a machine
(e.g. Sensor embedded in an engine)
Typically an entirely new source of data
(e.g. Use of Internet)
Not designed to be friendly
(e.g. Text streams)
May not have much values
(e.g. Need to focus on the important part)
19. WHO’S GENERATING BIG DATA ?
Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Mobile devices
(tracking all objects all the time)
Sensor technology and
networks
(measuring all kinds of data)
• The progress and innovation is no longer hindered by the ability to collect data
• But, by the ability to manage, analyze, summarize, visualize, and discover knowledge
from the collected data in a timely manner and in a scalable fashion
20. THE MODEL HAS CHANGED…
The Model of Generating/Consuming Data has Changed
Old Model: Few companies are generating data, all others are consuming data
New Model: all of us are generating data, and all of us are consuming
data
21. CHALLENGES
The challenges in Big Data can be broadly divided in
to two categories:
engineering
semantic
Engineering challenges include data management
activities such as query, and storage efficiently.
semantic challenge is determining the meaning of
information from large volumes of unstructured data.
22. High volume of processing using a low-power
consumed digital processing architecture.
Discovery of data-adaptive machine learning
techniques that are able toanalyze data in real-time.
Design scalable data storages that provide efficient
data mining.
On the other hand, Patidar, Rane and Jain have
identified a number of key challenges in Big Data
management related to the cloud as follows:
Data security and privacy;
Approximate results;
Data exploration to enable deep analytics;
Enterprise data enrichment with web and social media;
Query optimization; and
Performance isolation for multi-tenancy.
24. ADVANTAGES AND DISADVANTAGES
ADVANTAGES
+ Big
+ Timely
+ Predictive (sometimes)
+ Cheap
DISADVANTAGES
- Unknown population representation
- Issues of data quality
- Typically not very multivariate (at the person level)
- Privacy and confidentiality issues
- Difficult to assess accuracy and uncertainty
25. APPLICATIONS
Banking and Securities
Communications, Media and Entertainment
Healthcare Providers
Education
Manufacturing and Natural Resources
Government
Insurance
Information Technology
Retail
Retail banking
Real estate
Transportation
Energy and Utilities
26.
27. CONCLUSION
Big Data is new and requires investigation and
understanding of both technical and business
requirements.
Indeed, Big Data is not a stand-alone technology; rather, it
is a combination of the last 50 years of technological
evolution.
The big advantage of Big Data is its ability to leverage
massive amounts of data without all the complex
programming that was required in the past.
