Bigdata dwh
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The document discusses the topic of big data, including an introduction to big data and big data analytics. It describes the characteristics of big data, including velocity, volume, and variety. Examples are given of how big data is used by consumers in applications like iOS7, Spotify, Amazon, Netflix, and Google Now. Other potential applications of big data are also mentioned.
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Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
Introduction to Harnessing Big Data
Everyone knows there's too much big data. But what's the best way to harness the power of big data? This presentation discusses three analytic engines that companies big and small are using to capture, store, transform and use big data. Also included are case studies of big data in action.
Big data
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
Keynote talk at Financial Times Forum - BigData and Advanced Analytics at SIB...
The document provides an overview of big data and predictive analytics. It discusses the volume, velocity, and variety characteristics of big data. It also covers some of the issues with big data like its messy and unstructured nature. Several case studies are presented, including how Yahoo! uses big data for online marketing through behavioral targeting. The goal is to turn the three V's of big data (volume, velocity, variety) into business value by understanding context, sentiment, and user intent from large amounts of diverse data.
Big data destruction of bus. models
Big data offers opportunities for companies to gain competitive advantages through improved customer intimacy, product innovation, and operations. The document discusses how various companies are leveraging big data across industries. It notes that 45% of companies have implemented big data initiatives in the past two years and over 90% of Fortune 500 companies will have initiatives underway soon. Harnessing big data's potential requires understanding where it can create value within a company and having the right organizational structure, technology investments, and plan to capture those benefits.
uae views on big data
Big data comes from a variety of sources and in different formats. It is characterized by its volume, velocity, and variety. Organizations are using big data to gain business insights through analytics. This allows them to increase revenue, reduce costs, optimize processes, and manage risks. Examples of big data uses include marketing campaign analysis, customer segmentation, and fraud detection. Companies must overcome technological and organizational challenges to successfully leverage big data.
What is AI without Data?
The document discusses how data has become a central business asset and strategic advantage. It notes that the growth of data from sources like the Internet of Things means that variety, not just volume or velocity, will be important. New business processes will revolve around data, which will become more valuable over the next decade. It also provides examples of how companies like eBay and Groupon have used data for competitive advantages like identifying top sellers.
Data science and its potential to change business as we know it. The Roadmap ...
The document summarizes a presentation on data science and its potential to change business. It discusses how organizations can increase their data science maturity and capabilities to gain more value from data. As data volumes continue growing exponentially, data science can help organizations move from simple reporting to predictive analytics in order to make real-time decisions. The presentation examines how data science is an emerging field that incorporates techniques from many areas and how organizations can assess their analytics maturity.
e-Marketing Research
E-marketing is a process of planning and executing the conception, distribution, promotion, and pricing of products and services in a computerized, networked environment, such as the Internet and the World Wide Web, to facilitate exchanges and satisfy customer demands.
Changing nature of data and its implications on analytics
Changing nature of data and its implications on analytics by Sameer Kumar - Senior Solution Architect, Ashnik
Content1. Introduction2. What is Big Data3. Characte.docx
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.
2015 BigInsights Big Data Study
This document summarizes the key findings of the 2015 Big Data End User Study conducted by BigInsights. The study explored how organizations in the Asia Pacific region are adopting and using big data technologies. It found that data volumes are growing rapidly across industries and organizations are pursuing big data initiatives to drive business benefits like improved customer insights and supply chain optimization. However, challenges remain around integrating diverse data types and delivering big data infrastructure. The report provides insights into how organizations are applying big data analytics, the benefits they expect to achieve, and the challenges they face.
Applying Blockchain to the Media, Marketing & Insights Industries
Rolfe Swinton, Director of Data Assets at GfK North America has presented to the marketing community an overview of what Blockchain is and areas of potential help and impact on the consumer insight and marketing industries.
Aziksa hadoop for buisness users2 santosh jha
This document discusses big data, including its drivers, characteristics, use cases across different industries, and lessons learned. It provides examples of companies like Etsy, Macy's, Canadian Pacific, and Salesforce that are using big data to gain insights, increase revenues, reduce costs and improve customer experiences. Big data is being used across industries like financial services, healthcare, manufacturing, and media/entertainment for applications such as customer profiling, fraud detection, operations optimization, and dynamic pricing. While big data projects show strong financial benefits, the document cautions that not all projects are well-structured and Hadoop alone is not sufficient to meet all business analysis needs.
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Data Lake Architecture – Modern Strategies & Approaches
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Changing nature of data and its implications on analytics
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Applying Blockchain to the Media, Marketing & Insights Industries
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Phenomics assisted breeding in crop improvement
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
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the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
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在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样
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二.真实使馆公证(即留学回国人员证明,不成功不收费)
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四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
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Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
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I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
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Bigdata dwh
- 1. 11/15/2018 1
- 2. Introduction • Quratulain leghari • 16 BS IT (098) • Topic Big data 11/15/2018 2
- 3. Content • Introduction to big data • Big data analytics • Characteristics of Big data • How big data is used • Applications 11/15/2018 3
- 4. 11/15/2018 4
- 5. Introduction to Big Data • Big data is a term that describes the large volume of data. 11/15/2018 5
- 6. Big data analytics • Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. 11/15/2018 6
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- 8. Characteristics of Big data • Velocity • Volume • Variety 11/15/2018 8
- 9. How big data is used • Big Data for Consumers • Ios7 • Spotify • Amazon.com • Netflex • Google now 11/15/2018 9
- 10. Big Data for Consumers 11/15/2018 10
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- 17. Other Applications of Big Data 11/15/2018 17
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- 19. Any question, suggestion, comment 11/15/2018 19
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