Bigdata
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this presentation will help you to teach someone the basics of big data. it has less information on the slides with for graphical representation. slide 6 - shows how a traditional centralized system fail to process big data slide 16 - data produced and transmitted from sensing devices slide 17 - The Internet of Things is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors Smart alarm gets a notification from traffic detection sensors and wake you up early in the morning. “never get late to office due to traffic” slide 26 - The #ShareaCoke campaign began in Australia in 2011 and then moved throughout the world Targeting “millennials” (people born from 1980–2000) the campaign started with generic terms and 150 of the country’s most popular first names From the hashtags in Instagram and twitter they analyzed that the sales are going high and then even started to customize coke can online for the customers slide 29 - to find new cures and better understand and predict disease patterns. slide 30 - Lets the sportsman know their ranking and improve accordingly/ learn their mistakes
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Big data
Big data refers to the massive amounts of structured and unstructured data being generated by users and devices. The amount of digital data created is growing exponentially and more than 90% of data created in the last two years is unstructured. Harnessing useful insights from big data in a timely manner is challenging due to the volume of data and need for fast analysis. Examples show how companies are using big data analytics to improve customer service, inventory, and operations. Skills in data science and analytics are in high demand as the big data market value grows significantly in coming years.
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ExcelR is a proud partner of Universiti Malaysia Saravak (UNIMAS), Malaysia’s 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings.
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Lets ideate and discuss more:
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This document provides an overview of big data including:
- It defines big data and describes its three key characteristics: volume, velocity, and variety.
- It explains how big data is stored, selected, and processed using techniques like Hadoop and NoSQL databases.
- It discusses some common sources of big data, tools used to analyze it, and applications of big data analytics across different industries.
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BIG DATA
Prepared By
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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.
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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.
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· ‘Big Data’ is similar to ‘small data’, but bigger in
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· 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
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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.
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Data-Ed: Demystifying Big Data
Yes, we face a data deluge and big data seems to be largely about how to deal with it. But 99% of what has been written about big data is focused on selling hardware and services. The truth is that until the concept of big data can be objectively defined, any measurements, claims of success, quantifications, etc. must be viewed skeptically and with suspicion. While both the need for and approaches to these new requirements are faced by virtually every organization, jumping into the fray ill-prepared has (to date) reproduced the same dismal IT project results.
The very real, very rapid, very great increases in data of all forms (charts showing data types and volume increases)
Challenges faced by virtually all data management programs
Means by which big data techniques can compliment existing data management practices
Necessary but insufficient pre-requisites to exploiting big data techniques
Prototyping nature of practicing big data techniques
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
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Big Data
This document provides an overview of big data, including:
- A brief history of big data from the 1920s to the coining of the term in 1989.
- An introduction explaining that big data requires different techniques and tools than traditional "small data" due to its larger size.
- A definition of big data as the storage and analysis of very large digital datasets that cannot be processed with traditional methods.
- The three key characteristics (3Vs) of big data: volume, velocity, and variety.
Big data Mining
- Mining big data presents many current challenges including issues with variety, scalability, velocity, and privacy. Effective big data mining requires new tools and algorithms to handle large, diverse datasets generated at high speeds from various sources.
- Key challenges include dealing with heterogeneous and unstructured data from different sources, designing techniques that can scale to extremely large datasets, mining data fast enough to be valuable, and addressing privacy concerns when combining personal information from multiple datasets.
- Future work aims to develop new techniques to overcome scalability, speed, and privacy challenges in mining increasingly large and complex big data sources to unlock valuable insights.
Ictam big data
Introductory presentation on what Big Data is and what it isn't. Suggestions on spotting Big Data Opportunities and how to create a Big Data Strategy.
Bigdata " new level"
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."
bigdatappt.pptx
Big data refers to large volumes of diverse data that are created quickly and in large quantities. It is characterized by 3 V's - volume, velocity, and variety. Volume refers to the large amount of data, velocity refers to the speed at which data is generated and processed, and variety refers to different types of data including structured, unstructured, and semi-structured data. Big data is generated from various sources like users, sensors, applications and requires distributed storage, processing using tools like Hadoop and MapReduce. Analyzing big data can provide competitive advantages through insights from hidden patterns, better decision making and improved business operations. Programming languages like Python, Java, R, Scala are commonly used for big data applications.
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一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
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1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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Bigdata
- 1. Big Data Nimansa Athukorala Team – OTC Nimansa 1
- 2. Content 1. Introduction to Big Data 2. History 3. Types of Data 4. 3 Vs in Big Data (characteristics of Big data ) 5. Uses of Big Data 6. Disadvantages in Big data 7. Recap Nimansa 2
- 3. Introduction to Big Data Nimansa 3
- 4. What is Big Data ? Massive volume of both structured and unstructured data that is so large, it is difficult to process using Traditional database and Software techniques -Wikipedia Nimansa 4
- 5. 230 Million Tweets per day 294 Billion Emails per day 72 Hours of video uploaded every minute 100 Tera bytes uploaded every day Stats from : www.geekfence.com Nimansa 5
- 6. Nimansa 6
- 8. 5 Exabyte of data were created from dawn of time until 2003 Nimansa 8
- 9. 1 Exabyte = Billion Gigabytes (1e+9) & we create 5 exabyte or more every 2 days now Said by - Eric Schmidt (Chairman of Google) Nimansa 9
- 10. Evolution of Data sources Nimansa 10
- 11. Types of Data Nimansa 11
- 12. Types of Data •Activity Data •Conversation Data •Photo and Video image Data •Sensor Data •The Internet of things Data Nimansa 12
- 13. Activity Data Listening to Music using Spotify Online shopping and online money transferring Reading a eBook using a Kindle Nimansa 13
- 15. Photo and Video image Data Nimansa 15
- 17. The Internet of things Data Nimansa 17
- 18. 3 Vs in Big Data Nimansa 18
- 19. 3 Vs – Characteristics of Big Data •Volume •Velocity •Variety Nimansa 19
- 20. Volume •Refers to the vast amounts of data generated every second. •Data across the world is which are stored and analyzed Nimansa 20
- 21. Velocity Refers to the speed at which new data is generated Nimansa 21
- 23. Variety Refers to the different types of data we can now use Nimansa 23
- 24. Uses of Big Data Nimansa 24
- 26. Understanding and Optimizing Business Processes #ShareaCoke Nimansa 26
- 27. Nimansa 27
- 28. Personal Quantification and Performance Optimization Nimansa 28
- 29. Improving Healthcare and Public Health Nimansa 29
- 32. •The analysis extracted through big data can be deceitful •Processing massive amounts of data is time consuming and costly •Personnel data might be exposed to external parties Eg - Facebook users' data breach ( 31st March 2018 ) •Some social media users might get uncomfortable with the advertisements displayed related to search history Nimansa 32
- 33. Recap Nimansa 33
- 34. • Big data is a Massive volume of data that can not be handled using traditional techniques • Until 2003 we created 5 Exabyte of data but now we create 5 Exabyte of data every 2 days • There are 5 types of data • Big data has 3 characteristics often refer as 3 Vs • Big data is widely used for marketing, finance , healthcare and many more Nimansa 34
- 35. References • http://eminenture.weebly.com/blog/5-advantages-and-disadvantages-of-big- data?utm_campaign=Submission&utm_medium=Community&utm_source=GrowthHackers.com • http://timesofindia.indiatimes.com/articleshow/63433451.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst • https://www.google.lk/search?q=machine+learning+vs+big+data&rlz=1C1CHBD_enLK798LK798&oq=machine+learning+vs+big+data&aqs=chr ome.0.0j69i60j0l4.10047j1j1&sourceid=chrome&ie=UTF-8 • https://www.ahajournals.org/doi/10.1161/CIR.0000000000000558 • https://www.datapine.com/blog/big-data-examples-in-healthcare/ • https://www.educba.com/big-data-vs-machine-learning/ • https://medium.com/@kwindle/share-a-coke-and-a-word-how-coca-cola-captured-millennials-through-word-of-mouth-marketing- 44896573d21c Nimansa 35
- 36. Nimansa 36