In order to meet the needs of consumers for always-on, low-latency insights, big data has moved from a model of a central network to one that focuses on the edges of the network and from the cloud to a fog that reaches the full extent of the network.
- [Instructor] Sometimes, when you're thinking … about strategy, you get to choose between going for … a single big win, or maybe many small wins. … For example, in baseball, … you can go for the glorious home run, … or you can play small ball … and just focus on getting runners on base … and systematically moving them forward. … If you're writing novels, … you can hope to have a massive bestseller, … which is hard, or you can write a dozen books … that sell well within a particular niche. … And when it comes to helping people, … you can engage in a giant, newsworthy, grand gesture, … or you can focus on a thousand small acts of kindness. … You have a similar strategic choice in big data. … You can either put all of your data … and all of your processing hardware … in a single, massive server farm … or other central location. … Or, you can spread out the work to many, … many small processors. … In big data, this latter approach goes by … a couple of different names. … One is Edge Computing. …
Author
Barton Poulson
Released
9/19/2019- Identify the components that make up big data.
- Examine how big data has grown over the last few years.
- Explain the importance of using big data in business organizations.
- Distinguish between knowledge requirements for using big data and for understanding data science.
- Justify the need for training on big data within an organization.
- Analyze the factors that go into utilizing big data on a project.
- Differentiate outcomes that are derived from big data from outcomes that are derived from observing behaviors.
Skill Level Beginner
Duration
Views
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Introduction
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How big data shapes AI1m 46s
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1. Defining Big Data
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2. How Is Big Data Used?
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Big data for applications4m 41s
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3. Big Data and Data Science
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4. Ethics in Big Data
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Big data and privacy5m 52s
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Data governance6m 2s
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5. Data Logistics
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An evolving data landscape5m 48s
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6. Analyzing Big Data
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Visualizing big data5m 13s
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Data mining4m 39s
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Text analytics4m 18s
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Sentiment analysis4m 48s
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Predictive analytics4m 7s
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Anomaly detection3m 59s
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Conclusion
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Next steps2m 56s
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Video: Edge computing and fog computing