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 – 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.
At the Technology Trends seminar, with HCMC University of Polytechnics' lecturers, KMS Technology's CTO delivered a topic of Big Data, Cloud Computing, Mobile, Social Media and In-memory Computing.
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.
COMEX2017 Smart Talks by Amjid Ali , Muscat, Oman. Covering Introduction to big data, Big Data Definitions, Big Data Revolution, Big Data Timeline, Hadoop and Map Reduce covers importance of storage and DNA, Oceanstore 9000, Microsoft R, Spark,
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.
At the Technology Trends seminar, with HCMC University of Polytechnics' lecturers, KMS Technology's CTO delivered a topic of Big Data, Cloud Computing, Mobile, Social Media and In-memory Computing.
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.
COMEX2017 Smart Talks by Amjid Ali , Muscat, Oman. Covering Introduction to big data, Big Data Definitions, Big Data Revolution, Big Data Timeline, Hadoop and Map Reduce covers importance of storage and DNA, Oceanstore 9000, Microsoft R, Spark,
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.
Whether you are interested in healthcare data analytics or looking to get started with big data and marketing, these fundamental principles from data experts will contribute to your success. http://www.qubole.com/new-series-big-data-tips/
Oracle Exadata, Oracle Database 11g and Oracle Real Application Clusters enable consolidation of multiple applications on clustered server and storage pools-providing unbeatable fault tolerance, performance and scalability. Learn how these technologies can be used to consolidate your databases onto a private cloud—and realize the efficiencies of mixed workload consolidation, workload and resource management, and dynamic provisioning for elastic scalability.
"Apache Spark™ is a fast and general engine for large-scale data processing."" Above statement is taken from Apache Spark welcome page. It's one of those definitions that, while describing the product in one sentence and being 100 % true, tell still little to the wondering noob.
Why take interest in Apache Spark? Apache Spark promise being up to 100x faster than Hadoop MapReduce in certain scenarios. It provide comprehensible programming model (familiar to everyone who is used to functional programming) and vast ecosystem of tools.
In my talk I will try to reveal secrets of Apache Spark for the very beginners.
We will do first quick introduction to the set of problems commonly known as BigData: what they try to solve, what are their obstacles and challenges and how those can be addressed. We will quickly take a pick on MapReduce: theory and implementation. We will then move to Apache Spark. We will see what was the main factor that drove its creators to introduce yet another large-scala processing engine. We will see how it works, what are its main advantages. Presentation will be mix of slides and code examples.
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.
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.
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
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."
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
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
Every day we roughly create 2.5 Quintillion bytes of data; 90% of the worlds collected data has been generated only in the last 2 years. In this slide, learn the all about big data
in a simple and easiest way.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
1. Special Issues on BIG DATA
Represented by:
Vedanand Singh
Twitter@VedanandSingh
Facebook/VedanandJSingh
CSE, 7th Sem.
United Group of Institutions , Gr. Noida India
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
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
3. • 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
• 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?
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
• 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.
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. Why Big Data
•FB generates 10TB daily
•Twitter generates 7TB of data
Daily
•IBM claims 90% of today’s
stored data was generated
in just the last two years.
16. 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
16
18. Data generation points Examples
Mobile Devices
Readers/Scanners
Science facilities
Microphones
Cameras
Social Media
Programs/ Software
19. 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
20. • 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
21. A Application Of Big Data analytics
Homeland
Security
Smarter
Healthcare
Multi-channel
sales
Telecom
Manufacturing
Traffic Control
Trading
Analytics
Search
Quality
22. 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
22
23. Leading Technology Vendors
Example Vendors
• IBM – Netezza
• EMC – Greenplum
• Oracle – Exadata
Commonality
• MPP architectures
• Commodity Hardware
• RDBMS based
• Full SQL compliance
24. 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.
25. 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.
26. 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
27. 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.
28. 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.
29. 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.