This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks, benefits and future. Big data is characterized by its volume, velocity and variety. It is generated from sources like users, applications, sensors and more. Tools like Hadoop and databases are used to store, process and analyze big data. Big data analytics can provide benefits across many industries and applications. However, it also poses risks around privacy, costs and skills that must be addressed. The future of big data is promising, with the market expected to grow significantly in the coming years.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
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 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.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
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 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.
How a SaaS Company Almost Killed It On Instagram Strikingly
We grew our following from 400 to 1500+ followers in 2 months. Want to know why User Generated Content saved our sanity, why you MUST join, or form, your Instagram Squad, and what tools we used to schedule Instagram posts, in advance? Keep reading.
Content1. Introduction2. What is Big Data3. Characte.docxdickonsondorris
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• 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.
• ‘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.
What is BIG DATA?
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• 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.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data stru.
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
Prepared By
Muhammad Abrar Uddin
Introduction
· 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’ 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.
What is BIG DATA
· Walmart handles more than 1 million customer transactions every hour.
· Facebook handles 40 billion photos from its user base.
· Decoding the human genome originally took 10years to process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
(
Volume
Data
quantity
) (
Velocity
Data
Speed
) (
Variety
Data
Types
)
1st Character of Big Data
Volume
· A typical PC might have had 10 gigabytes of storage in 2000.
· Today, Facebook ingests 500 terabytes of new data every day.
· Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
· The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video.
2nd Character of Big Data
Velocity
· Clickstreams and ad impressions capture user behavior at millions of events per second
· high-frequency stock trading algorithms reflect market changes within microseconds
· 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.
3rd Character of Big Data
Variety
· Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media.
· Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure.
· Big Data analysis includes different types of data
Storing Big Data
· Analyzing your data characteristics
· Selecting data sources for analysis
· Eliminating redundant data
· Establishing the role of NoSQL
· Overview of Big Data stores
· Data models: key value, graph, document, column-family
· Hadoop Distributed File System
· H.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
seminar on Big Data Technology
report on big data technology
webinar on big data technology
topic on big data technology
ppt presentation on big data technology
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.
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
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.”
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).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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/
2. Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
Big Data sources
1. Tools used in Big Data
2. Application of Big Data
3. Risks of Big Data
4. Benefits of Big Data
5. How Big Data Impact on IT
6. Future of Big Data
3. INTRODUCTION
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.
4. ‘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
What is BIG DATA?
5. What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
6. Three Characteristics of Big Data
V3s
Volume
•Data
quantity
Velocity
•Data
Speed
Variety
•Data
Types
7. 1st Character of Big Data
Volume
A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
8. 2nd Character of Big Data
Velocity
1. Clickstreams and ad impressions capture user behavior at
millions of events per second
2. high-frequency stock trading algorithms reflect market
changes within microseconds
3. machine to machine processes exchange data between
billions of devices
4. infrastructure and sensors generate massive log data in real-
time
5. on-line gaming systems support millions of concurrent users,
each producing multiple inputs per second.
9. 3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data structure.
• Big Data analysis includes different types of data
10. Storing Big Data
Analyzing your data characteristics
• Selecting data sources for analysis
• Eliminating redundant data
• Establishing the role of NoSQL
Overview of Big Data stores
• Data models: key value, graph, document,
column-family
• Hadoop Distributed File System
• HBase
• Hive
11. Selecting Big Data stores
• Choosing the correct data stores based on
your data characteristics
• Moving code to data
• Implementing polyglot data store solutions
• Aligning business goals to the appropriate
data store
12. Processing Big Data
Integrating disparate data stores
• Mapping data to the programming framework
• Connecting and extracting data from storage
• Transforming data for processing
• Subdividing data in preparation for Hadoop
MapReduce
Employing Hadoop MapReduce
• Creating the components of Hadoop MapReduce jobs
• Distributing data processing across server farms
• Executing Hadoop MapReduce jobs
• Monitoring the progress of job flows
13. The Structure of Big Data
Structured
• Most traditional data
sources
Semi-structured
• Many sources of big
data
Unstructured
• Video data, audio data
13
14. 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
15. How Is Big Data Different?
1) Automatically generated by a machine
(e.g. Sensor embedded in an engine)
2) Typically an entirely new source of data
(e.g. Use of the internet)
3) Not designed to be friendly
(e.g. Text streams)
4) May not have much values
• Need to focus on the important part
15
18. Big Data Analytics
• Examining large amount of data
• Appropriate information
• Identification of hidden patterns, unknown correlations
• Competitive advantage
• Better business decisions: strategic and operational
• Effective marketing, customer satisfaction, increased
revenue
19. • Where processing is hosted?
– Distributed Servers / Cloud (e.g. Amazon EC2)
• Where data is stored?
– Distributed Storage (e.g. Amazon S3)
• What is the programming model?
– Distributed Processing (e.g. MapReduce)
• How data is stored & indexed?
– High-performance schema-free databases (e.g. MongoDB)
• What operations are performed on data?
– Analytic / Semantic Processing
Types of tools used in
Big-Data
20. A Application Of Big Data analytics
Homeland
Security
Smarter
Healthcare
Multi-channel
sales
Telecom
Manufacturing
Traffic Control
Trading
Analytics
Search
Quality
21. Risks of Big Data
• Will be so overwhelmed
• Need the right people and solve the right problems
• Costs escalate too fast
• Isn’t necessary to capture 100%
• Many sources of big data
is privacy
• self-regulation
• Legal regulation
21
22. Leading Technology Vendors
Example Vendors
• IBM – Netezza
EMC – Greenplum
• Oracle – Exadata
Commonality
• MPP architectures
• Commodity Hardware
• RDBMS based
• Full SQL compliance
23. How Big data impacts on IT
• Big data is a troublesome force presenting
opportunities with challenges to IT organizations.
• By 2015 4.4 million IT jobs in Big Data ; 1.9 million
is in US itself
• India will require a minimum of 1 lakh data
scientists in the next couple of years in addition
to data analysts and data managers to support
the Big Data space.
24. Potential Value of Big Data
• $300 billion potential annual
value to US health care.
• $600 billion potential annual
consumer surplus from using
personal location data.
• 60% potential in retailers’
operating margins.
25. India – Big Data
• Gaining attraction
• Huge market opportunities for IT services
(82.9% of revenues) and analytics firms
(17.1 % )
• Current market size is $200 million. By 2015 $1
billion
• The opportunity for Indian service providers lies
in offering services around Big Data
implementation and analytics for global
multinationals
26. Benefits of Big Data
•Real-time big data isn’t just a process for storing
petabytes or exabytes of data in a data warehouse, It’s
about the ability to make better decisions and take
meaningful actions at the right time.
•Fast forward to the present and technologies like Hadoop
give you the scale and flexibility to store data before you
know how you are going to process it.
•Technologies such as MapReduce,Hive and Impala enable
you to run queries without changing the data structures
underneath.
27. Benefits of Big Data
• Our newest research finds that organizations are using big
data to target customer-centric outcomes, tap into internal
data and build a better information ecosystem.
• Big Data is already an important part of the $64 billion
database and data analytics market
• It offers commercial opportunities of a comparable
scale to enterprise software in the late 1980s
• And the Internet boom of the 1990s, and the social media
explosion of today.
28. Future of Big Data
• $15 billion on software firms only specializing in
data management and analytics.
• This industry on its own is worth more than $100
billion and growing at almost 10% a year which is
roughly twice as fast as the software business as a
whole.
• In February 2012, the open source analyst firm
Wikibon released the first market forecast for Big
Data , listing $5.1B revenue in 2012 with growth to
$53.4B in 2017
• The McKinsey Global Institute estimates that data
volume is growing 40% per year, and will grow 44x
between 2009 and 2020.