EMC Isilon Database Converged deck

Editor's Notes

• #3: Just to put the exponential growth of the digital universe into context… Like the physical universe, the digital universe is large – doubling in size every two years, and by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes – containing nearly as many digital bits as there are stars in the universe. If the Digital Universe were represented by the memory in a stack of tablets, in 2013 it would have stretched two-thirds of the way to the Moon. By 2020, there would be 6.6 stacks from the Earth to the Moon. With this much data floating around, we need structure to sort it, make sense of it, and tell the story. That’s where data visualization comes in.
• #5: We live in an amazing time, but looking forward to 2020… Estimated 30B – 200B devices 7 billion people 1 million new businesses from where we are today These people using these devices within these businesses are constantly connected This gives rise to new ways of doing business: new disruptive technology, new disruptive business models <CLICK>
• #6: We’re already starting to see this today Looking at the likes of Nest: a thermostat that knows when you are in and out of your house, and can regulate the temperature in your home much more efficiently than ever done in the past Wearables such as Fitbits, Jawbones and the like There’s sports clothing companies that come to us that say they think in 10 years they will be more of a software company, with clothing that contains embedded telemetric devices, that communicate not just who they are, and where they are, but what time they get up, when they eat, when they sweat. Sports companies will know almost everything about you, whereas in the past they’ve known almost nothing about you. Another example: contact lenses that regulate blood sugar And another: intelligent machines. Let’s drill in on that for a bit… <CLICK>
• #7: Many industries are facing massive change. The thing that is driving the change is software and new applications – mobile and web applications – that create new possibilities. These are just a few examples: Nest is a software-defined thermostat. Thermostat’s entire job is measure temperature in a range and send a current to turn on and off the furnace / ac when the temperature is out of range. But, Nest built a thermostat with a web application to control it from anywhere and intelligence in the thermostat to recognize patterns and even know when you are home, so it can automatically adjust the temperature for you. That innovation is why Google bought Nest for $3.2B in Case in 2014. Tesla is a software-defined car. A mobile app allows you to control the car from anywhere, turning on the AC/heat before you arrive, opening and closing doors. They can also improve the car’s capabilities and efficiency by upgrading the car’s software instead of forcing you to get a new car. Uber allows you to call a towncar from any location. You call the car, the car shows up, you get in, tell them where you are going and get out. Your credit card is automatically hit, then you rate the driver and they rate you. This has turned the taxi industry on it’s ear. The entertainment industry is another big change. In the 80s, we all went to Blockbuster and hoped our new release was available. In the 2000s, they started redbox, which really hurt Blockbuster. Now you simply sit at home and everyone can watch the new release on the same day it comes out, streaming in to the home. Blockbuster is gone. Redboxes are fading. It’s all online streaming to your TV, your phone, your tablet…
• #8: What all products have figured out is its about Outcomes. A client won’t install Hadoop just to buy some servers. They are hoping (realistically or not) that they can improve something of their business
• #9: Why would Hadoop want to move towards Enterprise Storage Reference Architectures or Why would Databases w/ Enterprise Storage Reference Architectures move towards Hadoop Vendors Adoption for Hadoop are based upon available skills and operational support
• #10: For SQL Databases, all of the data growth is mainly unstructured For Hadoop, its hard for companies to get started due to operations and lack of skills with their incumbent talent pool.