1
00:00:04,160 --> 00:00:07,160
Speaker 1: Get in touch with technology with tech Stuff from how

2
00:00:07,240 --> 00:00:14,640
Speaker 1: stuff Works dot com. Hey there, this is Jonathan Strickland

3
00:00:14,640 --> 00:00:17,080
Speaker 1: and you are listening to tech Stuff. I am an

4
00:00:17,079 --> 00:00:19,680
Speaker 1: executive producer with How Stuff Works and I love all

5
00:00:19,720 --> 00:00:23,959
Speaker 1: things tech, and today we're going to rejoin the overview

6
00:00:24,160 --> 00:00:28,440
Speaker 1: of cloud computing. What exactly is cloud computing and how

7
00:00:28,480 --> 00:00:31,080
Speaker 1: does it work. We talked a little bit about the

8
00:00:31,120 --> 00:00:33,559
Speaker 1: history of cloud computing in the last episode and kind

9
00:00:33,560 --> 00:00:36,000
Speaker 1: of give some definitions of it. So if you haven't

10
00:00:36,040 --> 00:00:37,960
Speaker 1: listened to that, go check that out. It is a

11
00:00:38,000 --> 00:00:40,519
Speaker 1: bonus episode. This is also a bonus episode. I am

12
00:00:40,520 --> 00:00:44,080
Speaker 1: in Las Vegas, Nevada. Since that's the correct way it's pronounced,

13
00:00:44,120 --> 00:00:49,440
Speaker 1: I've learned, and I am covering Sweet World eighteen. It's

14
00:00:49,479 --> 00:00:52,920
Speaker 1: a big cloud computing conference held by net Sweet and

15
00:00:53,000 --> 00:00:54,960
Speaker 1: so as part of that, I thought I would do

16
00:00:55,000 --> 00:00:57,800
Speaker 1: a couple of episodes about cloud computing, and in this one,

17
00:00:57,840 --> 00:01:00,920
Speaker 1: we're going to look a little bit more into what

18
00:01:01,000 --> 00:01:04,480
Speaker 1: cloud computing can do these days. Turns out it is

19
00:01:04,520 --> 00:01:09,280
Speaker 1: a big, big business, a multibillion dollar business these days,

20
00:01:09,760 --> 00:01:12,200
Speaker 1: and I'll talk a little bit more about that and

21
00:01:12,440 --> 00:01:17,080
Speaker 1: uh enjoy So the ability to connect computers together also

22
00:01:17,120 --> 00:01:21,920
Speaker 1: gave rise to computing models similar to cloud computing. For example,

23
00:01:22,000 --> 00:01:25,479
Speaker 1: there's cluster computing. That's where you couple lots of different

24
00:01:25,520 --> 00:01:29,280
Speaker 1: machines together to work as a unified system. Typically, I

25
00:01:29,280 --> 00:01:32,000
Speaker 1: mean the coupling can be loose or tight. It doesn't

26
00:01:32,160 --> 00:01:37,120
Speaker 1: really it doesn't necessitate tightly coupling machines together, but frequently

27
00:01:37,160 --> 00:01:39,520
Speaker 1: that is the way it's done. And often these machines

28
00:01:39,520 --> 00:01:42,119
Speaker 1: are all in the same location, so they may all

29
00:01:42,160 --> 00:01:44,679
Speaker 1: be in the same data center. Clusters just tend to

30
00:01:44,720 --> 00:01:50,160
Speaker 1: be more tightly connected than other computer models. So there's

31
00:01:50,200 --> 00:01:54,480
Speaker 1: also grid computing. Grid computing is less tightly coupled than

32
00:01:54,480 --> 00:01:57,760
Speaker 1: cluster computing. You may have computers that are part of

33
00:01:57,760 --> 00:02:01,040
Speaker 1: this grid in remote locations there and nowhere close to

34
00:02:01,080 --> 00:02:05,800
Speaker 1: each other, and they're all working together to solve various problems.

35
00:02:05,840 --> 00:02:09,000
Speaker 1: But typically they're working kind of the way a multi

36
00:02:09,000 --> 00:02:12,360
Speaker 1: core processor works. And by that I mean that the

37
00:02:12,639 --> 00:02:16,359
Speaker 1: various computers in the grid are working on different parts

38
00:02:16,880 --> 00:02:19,840
Speaker 1: of a problem. You could have the problem divide it

39
00:02:19,919 --> 00:02:23,760
Speaker 1: up into various segments, and the various grid computers are

40
00:02:23,800 --> 00:02:26,160
Speaker 1: working on each of them are working on a segment

41
00:02:26,240 --> 00:02:29,600
Speaker 1: of that problem. This you can see examples of this,

42
00:02:29,680 --> 00:02:33,240
Speaker 1: and things like the various at home projects like folding

43
00:02:33,280 --> 00:02:37,799
Speaker 1: at Home or set at Home that use user computers

44
00:02:38,360 --> 00:02:42,360
Speaker 1: as uh as units in a grid computing system to

45
00:02:42,560 --> 00:02:48,160
Speaker 1: solve very difficult problems that would normally take a supercomputer

46
00:02:48,919 --> 00:02:51,240
Speaker 1: ages to complete if it were working on it just

47
00:02:51,320 --> 00:02:55,760
Speaker 1: by itself. So both cluster computers and grid computers, both

48
00:02:55,760 --> 00:03:00,520
Speaker 1: of those models are sort of like virtual supercomputers because

49
00:03:00,600 --> 00:03:06,079
Speaker 1: they are grouping the assets of various regular powered machines

50
00:03:06,120 --> 00:03:09,160
Speaker 1: together to make it more than what it was, so

51
00:03:09,240 --> 00:03:12,200
Speaker 1: you can create kind of a virtual supercomputer now. In

52
00:03:13,480 --> 00:03:18,480
Speaker 1: Professor Ramna Chilapa at Emory University here in Atlanta described

53
00:03:18,520 --> 00:03:21,560
Speaker 1: cloud computing as a method that would be defined not

54
00:03:21,760 --> 00:03:25,920
Speaker 1: by technological limits, but rather by economic factors. So, in

55
00:03:25,960 --> 00:03:29,720
Speaker 1: other words, the professor was saying that the costs associated

56
00:03:29,760 --> 00:03:33,920
Speaker 1: with scaling processes would make it imperative for businesses to

57
00:03:34,000 --> 00:03:37,839
Speaker 1: offload that burden by making use of cloud computing providers,

58
00:03:37,840 --> 00:03:41,160
Speaker 1: and that this would in turn create the opportunity for

59
00:03:41,200 --> 00:03:44,560
Speaker 1: such providers to kind of coalesce and become the partner

60
00:03:44,640 --> 00:03:47,240
Speaker 1: these companies needed to grow and do work. In other words,

61
00:03:47,480 --> 00:03:51,400
Speaker 1: he was saying the economic environment out there is such

62
00:03:52,160 --> 00:03:56,080
Speaker 1: that there is a need for services that can provide

63
00:03:56,120 --> 00:04:00,360
Speaker 1: these sort of cloud computing processes and storage. Uh So,

64
00:04:00,760 --> 00:04:03,840
Speaker 1: because there's a need, sooner or later, they're going to

65
00:04:03,880 --> 00:04:07,040
Speaker 1: be businesses filling that need. It's it's like nature of

66
00:04:07,120 --> 00:04:10,760
Speaker 1: whorring a vacuum kind of concept. So cloud computing isn't

67
00:04:10,800 --> 00:04:14,280
Speaker 1: just about technology but also the bottom line, and that

68
00:04:14,320 --> 00:04:17,479
Speaker 1: should not come as a surprise since many factors that

69
00:04:17,520 --> 00:04:21,159
Speaker 1: we frequently associate with technological advances are actually tied closely

70
00:04:21,200 --> 00:04:25,799
Speaker 1: to economic factors. For example, Moore's law, which we frequently

71
00:04:25,880 --> 00:04:30,320
Speaker 1: simplify by saying processing power doubles every two years or so,

72
00:04:31,080 --> 00:04:34,640
Speaker 1: was originally an observation about how economic factors would drive

73
00:04:34,720 --> 00:04:37,479
Speaker 1: the necessity to develop a means to double the number

74
00:04:37,480 --> 00:04:40,800
Speaker 1: of transistors on a square inch of silicon substrate. In

75
00:04:40,839 --> 00:04:45,640
Speaker 1: other words, money makes the world go round. In Evan

76
00:04:45,680 --> 00:04:49,680
Speaker 1: Goldberg founded a company called net Ledger. The company was

77
00:04:49,720 --> 00:04:52,400
Speaker 1: offering up software as a service, which means it's a

78
00:04:52,440 --> 00:04:55,120
Speaker 1: good time to tackle a few common buzzwords associated with

79
00:04:55,120 --> 00:04:57,480
Speaker 1: cloud computing. I'll get back to Mr Goldberg in just

80
00:04:57,560 --> 00:05:00,320
Speaker 1: a minute. Now. For one thing, you'll find a lot

81
00:05:00,360 --> 00:05:04,160
Speaker 1: of as a service buzz terms associated with cloud computing.

82
00:05:04,360 --> 00:05:07,280
Speaker 1: Software as a service is a big one that is

83
00:05:07,320 --> 00:05:10,440
Speaker 1: also known as S A A S, with both s

84
00:05:10,480 --> 00:05:13,040
Speaker 1: as capitalized in the lower A the as in lower

85
00:05:13,040 --> 00:05:17,120
Speaker 1: case UM. And this model, you don't sell software packages

86
00:05:17,200 --> 00:05:20,840
Speaker 1: to a customer. Instead, you set up essentially a subscription

87
00:05:20,920 --> 00:05:24,839
Speaker 1: service so that the customer can access the software. You

88
00:05:24,960 --> 00:05:28,160
Speaker 1: maintain ownership of the software itself. You as the provider.

89
00:05:28,360 --> 00:05:31,360
Speaker 1: You own the software. You're not selling instances of it

90
00:05:31,440 --> 00:05:35,239
Speaker 1: to people. You provide access to the software in return

91
00:05:35,920 --> 00:05:39,640
Speaker 1: for money. Then there's platform as a service that's P

92
00:05:40,080 --> 00:05:42,080
Speaker 1: A A S. Again the P and the S or

93
00:05:42,200 --> 00:05:44,360
Speaker 1: upper case the a's or lower case. That's the case

94
00:05:44,400 --> 00:05:49,200
Speaker 1: for all of these as a service buzzwords. With platform

95
00:05:49,240 --> 00:05:52,919
Speaker 1: as a service, you create a virtual platform for the

96
00:05:52,920 --> 00:05:58,240
Speaker 1: customer for the purposes of developing programs or apps. So

97
00:05:58,360 --> 00:06:01,280
Speaker 1: developers use the virtual platform them when creating whatever app

98
00:06:01,320 --> 00:06:03,440
Speaker 1: they're working on at the time, and it can serve

99
00:06:03,440 --> 00:06:06,520
Speaker 1: as a place where developers share tools and processes to

100
00:06:06,560 --> 00:06:09,760
Speaker 1: help speed up development. There's a variant of this called

101
00:06:09,880 --> 00:06:13,800
Speaker 1: mobile back end as a service that, as the name suggests,

102
00:06:13,839 --> 00:06:16,839
Speaker 1: performs the task of being the back end operations of

103
00:06:16,839 --> 00:06:21,240
Speaker 1: a mobile application. But this is all about creating the

104
00:06:21,279 --> 00:06:26,440
Speaker 1: tools the developer needs in order to create and test

105
00:06:26,640 --> 00:06:30,640
Speaker 1: and then deploy the software they are making. Next, you've

106
00:06:30,680 --> 00:06:35,240
Speaker 1: got the infrastructure as a service or i a a S.

107
00:06:35,720 --> 00:06:39,160
Speaker 1: This is even more robust than the platform as a service,

108
00:06:39,200 --> 00:06:42,400
Speaker 1: and it's really meant for businesses for enterprises. So let's

109
00:06:42,440 --> 00:06:46,560
Speaker 1: imagine that you start your own business and you're making toys,

110
00:06:46,960 --> 00:06:49,160
Speaker 1: and you start out small, maybe you're even working out

111
00:06:49,160 --> 00:06:50,920
Speaker 1: of your own home, and you're doing all of this

112
00:06:50,960 --> 00:06:55,320
Speaker 1: by hand. But your business grows, your demand increases, and

113
00:06:55,520 --> 00:06:59,279
Speaker 1: you are physically incapable of meaning that demand all by yourself.

114
00:06:59,320 --> 00:07:02,360
Speaker 1: So you need to scale up operations. And this kind

115
00:07:02,360 --> 00:07:06,240
Speaker 1: of starts slowly. You know, at first it's not too hectic.

116
00:07:06,360 --> 00:07:10,200
Speaker 1: You can hire on some people and take on that responsibility.

117
00:07:10,240 --> 00:07:13,160
Speaker 1: But if demand continues to increase, you have to take

118
00:07:13,200 --> 00:07:15,680
Speaker 1: the next step. You have to start forming relationships with

119
00:07:15,720 --> 00:07:18,880
Speaker 1: factories to make your toys. And this is both to

120
00:07:19,000 --> 00:07:21,800
Speaker 1: increase the supply as well as to reduce the cost.

121
00:07:21,880 --> 00:07:24,720
Speaker 1: By producing them in bulk, you can reduce the cost

122
00:07:24,800 --> 00:07:28,800
Speaker 1: on a per unit basis to produce and also still

123
00:07:28,840 --> 00:07:31,680
Speaker 1: meet the demand that your customers have. You have to

124
00:07:31,760 --> 00:07:34,080
Speaker 1: manage supply chains. Though you have to make sure all

125
00:07:34,080 --> 00:07:37,080
Speaker 1: the components you need to make your toys are getting

126
00:07:37,120 --> 00:07:39,480
Speaker 1: to the factories. You have to figure out how to

127
00:07:39,520 --> 00:07:42,880
Speaker 1: get the manufactured toys to retailers or to customers. You

128
00:07:42,920 --> 00:07:46,400
Speaker 1: have to manage all the money that's involved in those transactions,

129
00:07:46,400 --> 00:07:50,560
Speaker 1: whether it's payments to services like the factories or accepting

130
00:07:50,600 --> 00:07:54,280
Speaker 1: payments from customers. And for some businesses, this becomes a

131
00:07:54,320 --> 00:07:59,560
Speaker 1: barrier to growth because it's so overwhelming. The transformation of

132
00:07:59,640 --> 00:08:02,640
Speaker 1: small company to mid size company or mid size company

133
00:08:02,640 --> 00:08:06,239
Speaker 1: to large company can be really daunting. So the lure

134
00:08:06,480 --> 00:08:09,600
Speaker 1: of infrastructure as a service is that companies that have

135
00:08:09,640 --> 00:08:15,680
Speaker 1: already designed systems, usually suites of programs that streamlined various processes,

136
00:08:15,920 --> 00:08:19,320
Speaker 1: will share that knowledge and capability with you for a price.

137
00:08:19,440 --> 00:08:21,960
Speaker 1: So you pay a subscription and then you get all

138
00:08:22,120 --> 00:08:24,520
Speaker 1: access to all these tools that may help you do

139
00:08:24,600 --> 00:08:29,240
Speaker 1: things like track your supply chain, to track production and

140
00:08:29,360 --> 00:08:32,120
Speaker 1: even see how things have changed over time. Like there

141
00:08:32,160 --> 00:08:35,320
Speaker 1: are some very sophisticated programs out there that allow you

142
00:08:35,360 --> 00:08:39,240
Speaker 1: to get very granular with data. Data analysis is a

143
00:08:39,360 --> 00:08:43,320
Speaker 1: huge part of cloud computing services because it provides another

144
00:08:43,960 --> 00:08:47,160
Speaker 1: value to the customer if you can say, not only

145
00:08:47,200 --> 00:08:50,439
Speaker 1: will we show you what the UH you know where

146
00:08:50,480 --> 00:08:53,280
Speaker 1: you are at any given time, will show you trends

147
00:08:53,440 --> 00:08:56,959
Speaker 1: and will even start to predict risks that might be

148
00:08:57,200 --> 00:09:00,240
Speaker 1: in place, like maybe we see that because of this

149
00:09:00,320 --> 00:09:03,120
Speaker 1: one element in the supply chain, you're going to have

150
00:09:03,280 --> 00:09:06,960
Speaker 1: a bottleneck in the manufacturing process, which might mean you

151
00:09:07,040 --> 00:09:09,440
Speaker 1: need to be able to communicate out to customers that

152
00:09:09,480 --> 00:09:13,120
Speaker 1: there's currently a shortage of whatever the product is and

153
00:09:13,160 --> 00:09:15,040
Speaker 1: that you will be turning this around as soon as

154
00:09:15,040 --> 00:09:17,920
Speaker 1: you can. Or it might even be something about how

155
00:09:18,160 --> 00:09:22,840
Speaker 1: producing UH your product in one region might be more

156
00:09:22,920 --> 00:09:27,200
Speaker 1: economically viable than another region, even within the same country,

157
00:09:27,400 --> 00:09:29,920
Speaker 1: so that you can get your products to market with

158
00:09:30,000 --> 00:09:33,880
Speaker 1: a lower cost, not just economic, but environmental. It could

159
00:09:33,920 --> 00:09:38,040
Speaker 1: be something as as UH intrinsic as how many miles

160
00:09:38,440 --> 00:09:40,520
Speaker 1: have to be traveled in order to get your product

161
00:09:40,600 --> 00:09:44,040
Speaker 1: to market. So it gets really really complicated, and you

162
00:09:44,040 --> 00:09:47,040
Speaker 1: can see how if you're running your own business, once

163
00:09:47,080 --> 00:09:51,880
Speaker 1: you start layering on these different considerations, it rapidly gets

164
00:09:51,960 --> 00:09:55,760
Speaker 1: outside of most people's comfort zone, so that that's kind

165
00:09:55,760 --> 00:09:58,960
Speaker 1: of the selling point for infrastructure as a service. There's

166
00:09:59,000 --> 00:10:02,280
Speaker 1: a more recent off ring that's called function as a

167
00:10:02,320 --> 00:10:05,320
Speaker 1: service or f a a S. This creates a level

168
00:10:05,360 --> 00:10:09,280
Speaker 1: of abstraction between the developer and virtual machines, so that

169
00:10:09,320 --> 00:10:13,240
Speaker 1: developers only have to worry about creating very narrowly functional

170
00:10:13,360 --> 00:10:18,360
Speaker 1: blocks of code, and essentially they're just creating the instructions

171
00:10:18,400 --> 00:10:21,360
Speaker 1: that sit on top of this service, and the service

172
00:10:21,400 --> 00:10:25,480
Speaker 1: does everything else. Like it removes the necessity to program

173
00:10:25,520 --> 00:10:30,640
Speaker 1: for a specific stack of technology, so the developer just

174
00:10:30,679 --> 00:10:32,880
Speaker 1: has to worry about the code that he or she

175
00:10:32,960 --> 00:10:35,680
Speaker 1: needs to do whatever it is they want to do.

176
00:10:36,080 --> 00:10:40,360
Speaker 1: And typically this kind of service executes upon a real

177
00:10:40,400 --> 00:10:43,840
Speaker 1: world event happening. So something happens in the real world

178
00:10:44,240 --> 00:10:49,000
Speaker 1: that triggers the function, and the function then gets executed

179
00:10:49,040 --> 00:10:52,480
Speaker 1: by this service. So an example it would be, let's

180
00:10:52,480 --> 00:10:55,280
Speaker 1: say you go to a website and there's a form

181
00:10:55,440 --> 00:10:57,360
Speaker 1: you want to fill out in order to get access

182
00:10:57,440 --> 00:10:59,920
Speaker 1: to something. So you fill out the form, you can

183
00:11:00,000 --> 00:11:03,640
Speaker 1: complete it, and you submit it. That submission could be

184
00:11:03,679 --> 00:11:06,840
Speaker 1: the real world event that then triggers this function, this

185
00:11:06,920 --> 00:11:10,960
Speaker 1: hypothetical function I'm making up now. Unlike infrastructure as a

186
00:11:11,000 --> 00:11:14,079
Speaker 1: service that's a perpetual model that means you have to

187
00:11:14,160 --> 00:11:16,280
Speaker 1: keep paying a subscription for as long as you are

188
00:11:16,320 --> 00:11:20,120
Speaker 1: relying upon that infrastructure. It is a day today thing.

189
00:11:20,679 --> 00:11:25,119
Speaker 1: Function as a service typically only triggers a fee whenever

190
00:11:25,520 --> 00:11:29,720
Speaker 1: the function itself gets triggered, so you're not paying every

191
00:11:29,720 --> 00:11:32,800
Speaker 1: single day for this thing to exist. You're paying only

192
00:11:32,840 --> 00:11:35,240
Speaker 1: when people are using it, which means that you can

193
00:11:35,280 --> 00:11:38,160
Speaker 1: save money if it's not something that people are going

194
00:11:38,200 --> 00:11:40,959
Speaker 1: crazy and using all the time. If it is, then

195
00:11:41,120 --> 00:11:43,400
Speaker 1: maybe you have to look at a different model for

196
00:11:43,640 --> 00:11:46,640
Speaker 1: economic purposes. But in general, it means that you can

197
00:11:46,679 --> 00:11:48,920
Speaker 1: save a lot of money this way because it's very lightweight.

198
00:11:48,960 --> 00:11:52,880
Speaker 1: It just exists on the top of the provider's services.

199
00:11:53,160 --> 00:11:55,680
Speaker 1: Now there are other service models out there. I'm getting

200
00:11:55,679 --> 00:11:58,360
Speaker 1: really tired of saying the word service, but it's that's

201
00:11:58,520 --> 00:12:02,240
Speaker 1: just the term used. So there's storage as a service,

202
00:12:02,600 --> 00:12:05,960
Speaker 1: that's what sounds like. That's your online document databases or

203
00:12:06,000 --> 00:12:09,520
Speaker 1: photo albums or file storage or whatever. There's also video

204
00:12:09,559 --> 00:12:12,920
Speaker 1: as a service. There's compute as a service. There's probably

205
00:12:12,960 --> 00:12:15,240
Speaker 1: half a dozen more, but it all comes down to

206
00:12:15,280 --> 00:12:18,480
Speaker 1: the concept of someone else running a process on lots

207
00:12:18,520 --> 00:12:21,079
Speaker 1: of computers. You pay that someone a fee to make

208
00:12:21,160 --> 00:12:23,480
Speaker 1: use of those assets, and it frees you up from

209
00:12:23,520 --> 00:12:26,720
Speaker 1: having to build out your own massive computer system. Hey guys,

210
00:12:26,760 --> 00:12:29,760
Speaker 1: before we go any further, I need to take a

211
00:12:29,880 --> 00:12:40,319
Speaker 1: quick break to thank our sponsor. Let's get back to

212
00:12:40,440 --> 00:12:45,240
Speaker 1: net Ledger and Evan Goldberg. The company ran accounting software

213
00:12:45,400 --> 00:12:48,720
Speaker 1: that had a web enabled user interface, so customers could

214
00:12:48,720 --> 00:12:51,760
Speaker 1: pay to use the service over the Internet rather than

215
00:12:51,760 --> 00:12:54,760
Speaker 1: purchase a full software package and installed on their own machines.

216
00:12:55,080 --> 00:12:57,240
Speaker 1: And one of the selling points of this approaches there's

217
00:12:57,240 --> 00:12:59,560
Speaker 1: never a need to upgrade your software. And by that

218
00:12:59,640 --> 00:13:02,200
Speaker 1: I mean if you buy a software package, like a

219
00:13:02,240 --> 00:13:06,680
Speaker 1: conventional traditional software package, chances are sooner or later there's

220
00:13:06,679 --> 00:13:09,920
Speaker 1: going to be an updated version coming along, which creates

221
00:13:09,960 --> 00:13:12,720
Speaker 1: a lot of pressure on customers. So do you upgrade

222
00:13:12,880 --> 00:13:15,960
Speaker 1: to the latest version? If so, you probably have to

223
00:13:15,960 --> 00:13:19,000
Speaker 1: spend more money to do it. If you don't upgrade

224
00:13:19,040 --> 00:13:21,040
Speaker 1: to the latest version, you might find that some of

225
00:13:21,080 --> 00:13:24,439
Speaker 1: your data you work with becomes incompatible with your older

226
00:13:24,520 --> 00:13:29,640
Speaker 1: legacy systems because it's meant for the newer versions of

227
00:13:29,679 --> 00:13:32,600
Speaker 1: the software. So there could be features that the newer

228
00:13:32,720 --> 00:13:35,920
Speaker 1: version has that your old version does not have and

229
00:13:35,920 --> 00:13:40,080
Speaker 1: it doesn't support, and so you start running into compatibility issues.

230
00:13:40,320 --> 00:13:43,520
Speaker 1: But with software as a service, the provider makes all

231
00:13:43,520 --> 00:13:45,880
Speaker 1: the upgrades to the service on their end on the

232
00:13:45,960 --> 00:13:49,000
Speaker 1: on the back end, So you're accessing the software through

233
00:13:49,040 --> 00:13:52,480
Speaker 1: the Internet, whether it's through the web or through an app,

234
00:13:52,800 --> 00:13:56,000
Speaker 1: whatever it may be. You don't have to worry about

235
00:13:56,400 --> 00:13:59,920
Speaker 1: installing upgrades or patches necessarily. I mean sometimes you do,

236
00:14:00,040 --> 00:14:01,960
Speaker 1: but that's what the app world. If you're talking about

237
00:14:02,000 --> 00:14:05,640
Speaker 1: web browsers, you typically don't. So all you're doing is

238
00:14:05,679 --> 00:14:09,360
Speaker 1: just accessing the service. All of the upgrades are being

239
00:14:09,400 --> 00:14:12,160
Speaker 1: done behind the scenes on the provider side. So one

240
00:14:12,160 --> 00:14:14,400
Speaker 1: of the benefits of this model is that you always

241
00:14:14,400 --> 00:14:18,000
Speaker 1: have access to the latest version of the service as

242
00:14:18,000 --> 00:14:21,520
Speaker 1: long as you remain a customer. Net Ledgers features grew

243
00:14:21,920 --> 00:14:25,600
Speaker 1: the company evolved into net Suite, which better indicated that

244
00:14:25,640 --> 00:14:29,000
Speaker 1: the company was offering up multiple internet based services. A

245
00:14:29,040 --> 00:14:31,720
Speaker 1: short time after Goldberg found a net Suite, there was

246
00:14:31,760 --> 00:14:35,840
Speaker 1: another cloud based services company that popped up called Salesforce

247
00:14:35,920 --> 00:14:39,800
Speaker 1: dot Com. The founders of salesforce dot Com included one

248
00:14:39,920 --> 00:14:44,280
Speaker 1: former Oracle executive named Mark bennie Off and three software

249
00:14:44,280 --> 00:14:49,080
Speaker 1: developers named Frank Dominguez, Parker Harris, and Dave Mollenhoff, who

250
00:14:49,080 --> 00:14:53,280
Speaker 1: had come from Left Coast Software. Their first product was

251
00:14:53,480 --> 00:14:57,560
Speaker 1: a type of sales automation software, and Salesforce rapidly gained

252
00:14:57,560 --> 00:15:01,080
Speaker 1: attention as it was offering up enterprise level services over

253
00:15:01,120 --> 00:15:05,160
Speaker 1: a relatively simple web based interface. The company became incredibly

254
00:15:05,200 --> 00:15:07,960
Speaker 1: successful and was able to weather the dot com crash

255
00:15:08,080 --> 00:15:12,760
Speaker 1: and grow into a multibillion dollar global company. Net Suite

256
00:15:12,800 --> 00:15:15,080
Speaker 1: also made it through the crash and became a much

257
00:15:15,160 --> 00:15:18,200
Speaker 1: larger organization over time, and in two thousand and sixteen,

258
00:15:18,240 --> 00:15:21,000
Speaker 1: the company was acquired by Oracle. I'll talk more about

259
00:15:21,080 --> 00:15:24,480
Speaker 1: both net Suite and Oracle in upcoming episodes. But in

260
00:15:24,520 --> 00:15:28,600
Speaker 1: two thousand two, Amazon made a move to maximize company efficiency.

261
00:15:28,960 --> 00:15:32,040
Speaker 1: The standard practice at the time was to have enough capacity,

262
00:15:32,200 --> 00:15:36,520
Speaker 1: like computer capacity, to do your work while only using

263
00:15:36,560 --> 00:15:41,040
Speaker 1: ten of your capacity. But that's not very efficient. It

264
00:15:41,080 --> 00:15:44,720
Speaker 1: means that nine cent of your computer capacity goes unused,

265
00:15:44,760 --> 00:15:47,240
Speaker 1: and so Amazon began to explore other options that would

266
00:15:47,240 --> 00:15:51,480
Speaker 1: allow them to leverage that. The answer turned out to

267
00:15:51,480 --> 00:15:55,200
Speaker 1: be cloud storage and cloud computing, and in two thousand

268
00:15:55,320 --> 00:15:59,720
Speaker 1: six they expanded this by introducing Amazon Web Services, which

269
00:15:59,800 --> 00:16:01,920
Speaker 1: had a whole bunch of different web based services that

270
00:16:01,960 --> 00:16:05,800
Speaker 1: companies can use, including mechanical turk so quick side note

271
00:16:05,800 --> 00:16:08,440
Speaker 1: about Mechanical Turk because I just love this story. So

272
00:16:08,520 --> 00:16:11,640
Speaker 1: Amazon markets it as a tool that allows customers to

273
00:16:11,760 --> 00:16:15,720
Speaker 1: leverage human intelligence for specific tasks because humans tend to

274
00:16:15,760 --> 00:16:19,760
Speaker 1: be better at certain things than computers. And by better,

275
00:16:19,800 --> 00:16:22,080
Speaker 1: I mean we can do that particular type of work

276
00:16:22,160 --> 00:16:26,000
Speaker 1: much more quickly and reliably. But it can be expensive

277
00:16:26,320 --> 00:16:29,720
Speaker 1: to hire humans to do those tasks, especially if the

278
00:16:29,720 --> 00:16:34,160
Speaker 1: tasks are very simple, and if you only need people

279
00:16:34,280 --> 00:16:36,840
Speaker 1: for a short amount of time, then hiring a huge workforce,

280
00:16:36,920 --> 00:16:40,720
Speaker 1: a temporary workforce, that's that's an an enormous expense, and

281
00:16:41,040 --> 00:16:44,480
Speaker 1: not just money, but also in time. And so Amazon

282
00:16:44,560 --> 00:16:48,720
Speaker 1: Mechanical Turk is an on demand workforce service. It's kind

283
00:16:48,720 --> 00:16:51,920
Speaker 1: of like a group of people employed by Amazon to

284
00:16:52,040 --> 00:16:55,000
Speaker 1: do whatever it is you need them to do with

285
00:16:55,120 --> 00:17:00,880
Speaker 1: your human task issues. And then then once that's, once

286
00:17:00,920 --> 00:17:03,160
Speaker 1: that projects done, they go on to do something else.

287
00:17:03,400 --> 00:17:05,679
Speaker 1: And it sounds a little creepy if I'm being totally honest,

288
00:17:05,760 --> 00:17:07,960
Speaker 1: But the reason why I wanted to talk about them

289
00:17:08,000 --> 00:17:11,040
Speaker 1: was because of what mechanical Turk is a reference to.

290
00:17:11,240 --> 00:17:16,199
Speaker 1: It's actually referencing an old piece of clockwork chicanery. The

291
00:17:16,280 --> 00:17:20,080
Speaker 1: original device was called simply the turk, and it appeared

292
00:17:20,119 --> 00:17:24,600
Speaker 1: to be a clockwork automaton that had the figure of

293
00:17:24,640 --> 00:17:27,639
Speaker 1: a turk sitting at a chessboard. It was made by

294
00:17:27,680 --> 00:17:31,399
Speaker 1: a guy named wolf Gang Fawn Kimberland. He built it

295
00:17:31,440 --> 00:17:34,680
Speaker 1: in the late seventeen hundreds, like in the seventeen seventies,

296
00:17:35,080 --> 00:17:37,879
Speaker 1: and upon casual glance you would think it was a

297
00:17:38,040 --> 00:17:42,680
Speaker 1: robot of ingenious design, capable of defeating even skilled players

298
00:17:42,680 --> 00:17:45,879
Speaker 1: in chess. But in reality, there was a human being

299
00:17:46,000 --> 00:17:49,320
Speaker 1: hidden within the cabinet of the machine who was guiding

300
00:17:49,400 --> 00:17:53,440
Speaker 1: the turk's movements, and a playing chess against a human opponents.

301
00:17:53,480 --> 00:17:56,920
Speaker 1: So really it was just two humans playing chess, only

302
00:17:56,960 --> 00:17:59,639
Speaker 1: one of the humans was hidden out of sight. Just

303
00:18:00,040 --> 00:18:02,119
Speaker 1: I love that story, so I wanted to tell it quickly.

304
00:18:02,400 --> 00:18:05,400
Speaker 1: Around the same time the Amazon was introducing web services,

305
00:18:05,440 --> 00:18:08,560
Speaker 1: Google debuted Google Docs, which was actually based off two

306
00:18:08,560 --> 00:18:12,680
Speaker 1: earlier products. One of them was Google Spreadsheets, which Google acquired.

307
00:18:12,680 --> 00:18:15,400
Speaker 1: They bought it from a company called two Web Technologies,

308
00:18:15,960 --> 00:18:20,160
Speaker 1: and they also acquired a company called Rightly that ended

309
00:18:20,240 --> 00:18:22,760
Speaker 1: up being the basis for the Google Docs part, the

310
00:18:22,800 --> 00:18:26,880
Speaker 1: actual document offering of the product. How those moves began

311
00:18:26,920 --> 00:18:29,680
Speaker 1: to bring cloud computing into the world of the home

312
00:18:29,840 --> 00:18:34,600
Speaker 1: user for the first time. Earlier implementations were almost completely

313
00:18:34,640 --> 00:18:38,040
Speaker 1: focused on business to business operations, which means most of

314
00:18:38,119 --> 00:18:40,880
Speaker 1: us never see it right when we talk about business

315
00:18:40,880 --> 00:18:45,399
Speaker 1: to business stuff, it's interesting because it tells us how

316
00:18:45,480 --> 00:18:48,679
Speaker 1: the stuff we interact with, how that happens, how it

317
00:18:48,680 --> 00:18:51,560
Speaker 1: gets done in the background, but we don't see it directly.

318
00:18:52,040 --> 00:18:55,720
Speaker 1: That's why when we talk about companies like IBM, most

319
00:18:55,760 --> 00:19:00,359
Speaker 1: of us have very little experience working directly with IBM

320
00:19:00,359 --> 00:19:05,120
Speaker 1: products unless it's within the realm of the office, because

321
00:19:05,200 --> 00:19:10,840
Speaker 1: IBM doesn't really make products for the home consumer. The

322
00:19:10,920 --> 00:19:12,840
Speaker 1: same sort of thing here was that cloud computing for

323
00:19:12,880 --> 00:19:14,720
Speaker 1: the longest time was not for the home consumer. It

324
00:19:14,760 --> 00:19:18,920
Speaker 1: was for businesses. But with the emergence of these kind

325
00:19:19,000 --> 00:19:22,600
Speaker 1: of applications, we were starting to see people get access

326
00:19:22,680 --> 00:19:25,280
Speaker 1: to that sort of stuff for the first time in

327
00:19:25,320 --> 00:19:28,520
Speaker 1: a big way. And that's when it became necessary to

328
00:19:28,520 --> 00:19:31,879
Speaker 1: figure out how to explain that technology to people. And

329
00:19:31,960 --> 00:19:35,840
Speaker 1: that's when cloud computing really became a buzzword, and also

330
00:19:35,920 --> 00:19:38,359
Speaker 1: how I came to write sixteen articles about the stuff.

331
00:19:39,119 --> 00:19:43,040
Speaker 1: These days, many companies are involved with cloud services, along

332
00:19:43,040 --> 00:19:47,920
Speaker 1: with net Suite and Salesforce there's Amazon, Google, Microsoft has

333
00:19:47,960 --> 00:19:52,800
Speaker 1: its Azure platform, IBM is a big one, and there's

334
00:19:52,920 --> 00:19:55,800
Speaker 1: tons of others. And the services being offered by these

335
00:19:55,800 --> 00:19:59,520
Speaker 1: companies have grown significantly over time as well. For example,

336
00:19:59,760 --> 00:20:01,600
Speaker 1: i EM offers you the chance to work on a

337
00:20:01,680 --> 00:20:05,040
Speaker 1: quantum computer over the cloud, which still blows my mind.

338
00:20:05,760 --> 00:20:07,560
Speaker 1: We got just a bit more to talk about with

339
00:20:07,600 --> 00:20:10,080
Speaker 1: cloud computing, but before I get into it, let's take

340
00:20:10,080 --> 00:20:20,040
Speaker 1: another quick break to thank our sponsor. One thing I

341
00:20:20,040 --> 00:20:21,919
Speaker 1: feel we should talk about is the different types of

342
00:20:21,960 --> 00:20:25,680
Speaker 1: cloud computing models. And I almost said different types of clouds,

343
00:20:25,680 --> 00:20:28,320
Speaker 1: but I need to be more serious. That's a cloud joke.

344
00:20:28,840 --> 00:20:32,040
Speaker 1: One type is the public cloud model. Now, these are

345
00:20:32,080 --> 00:20:34,760
Speaker 1: services in which all the assets are run by a

346
00:20:34,840 --> 00:20:38,200
Speaker 1: third party. So I talked about Google Docs. That's a

347
00:20:38,240 --> 00:20:41,879
Speaker 1: great example of a public cloud computer model. So if

348
00:20:41,920 --> 00:20:46,760
Speaker 1: your company relies on Google Suite for all of its processes, really,

349
00:20:47,280 --> 00:20:49,800
Speaker 1: then you'll be relying on a public cloud. Then you've

350
00:20:49,800 --> 00:20:54,639
Speaker 1: got private clouds and those models. The company itself, whatever

351
00:20:54,680 --> 00:20:58,240
Speaker 1: company it is that's running the processes, it actually owns

352
00:20:58,720 --> 00:21:02,680
Speaker 1: all the equipment and the services that run on that equipment.

353
00:21:03,359 --> 00:21:06,520
Speaker 1: So let's say you work for a company ABC, The

354
00:21:06,640 --> 00:21:11,080
Speaker 1: private cloud is also owned by ABC, and it's got

355
00:21:11,119 --> 00:21:15,280
Speaker 1: all these different data centers and they're running all these processes.

356
00:21:15,520 --> 00:21:20,359
Speaker 1: So you're still accessing apps and programs and storage that

357
00:21:20,440 --> 00:21:23,960
Speaker 1: exists on other computers. It's just that you work for

358
00:21:24,000 --> 00:21:27,399
Speaker 1: the same company that owns those computers. It's not owned

359
00:21:27,440 --> 00:21:29,600
Speaker 1: by a third party. Now, you would want to use

360
00:21:29,600 --> 00:21:33,119
Speaker 1: the private cloud approach if you had mission critical applications

361
00:21:33,160 --> 00:21:35,880
Speaker 1: that you wanted under your full control and you didn't

362
00:21:35,920 --> 00:21:38,040
Speaker 1: want to entrust that to a third party. So if

363
00:21:38,040 --> 00:21:41,720
Speaker 1: you're handling really sensitive information or the processes you rely upon,

364
00:21:41,880 --> 00:21:44,920
Speaker 1: or really big trade secrets, that might be the way

365
00:21:44,920 --> 00:21:46,919
Speaker 1: you want to go. Or if your needs are just

366
00:21:47,040 --> 00:21:50,600
Speaker 1: so particular that there's not really a provider out there

367
00:21:50,640 --> 00:21:53,720
Speaker 1: that can meet your needs because they're so different from

368
00:21:53,720 --> 00:21:56,359
Speaker 1: what everyone else needs, this would be the way you

369
00:21:56,400 --> 00:21:59,879
Speaker 1: would go. There's a third model called the hybrid cloud

370
00:22:00,040 --> 00:22:03,480
Speaker 1: that merges those two. You have both public and private

371
00:22:03,520 --> 00:22:07,320
Speaker 1: cloud entities. So a company with a hybrid cloud approach

372
00:22:07,760 --> 00:22:11,760
Speaker 1: would have a public cloud stuff to do to handle

373
00:22:11,760 --> 00:22:14,840
Speaker 1: certain tasks, private cloud to handle other tasks, and as

374
00:22:14,880 --> 00:22:17,240
Speaker 1: you might imagine, the more critical elements to the company's

375
00:22:17,240 --> 00:22:20,320
Speaker 1: operations would probably run on the private cloud, while more

376
00:22:20,400 --> 00:22:24,040
Speaker 1: mundane processes might be pushed to the public cloud service,

377
00:22:24,560 --> 00:22:28,080
Speaker 1: and there's some sort of layer of communication and automation

378
00:22:28,400 --> 00:22:31,239
Speaker 1: that allows for the exchange of data between those two

379
00:22:31,320 --> 00:22:34,440
Speaker 1: clouds in a controlled manner so that the output from

380
00:22:34,480 --> 00:22:37,400
Speaker 1: one can work with the information on the other. In fact,

381
00:22:37,440 --> 00:22:40,920
Speaker 1: without that communication channel between the two clouds, you do

382
00:22:41,000 --> 00:22:44,560
Speaker 1: not actually have a hybrid cloud. You would instead have

383
00:22:44,680 --> 00:22:47,679
Speaker 1: what is called a multi cloud approach. You would have

384
00:22:48,040 --> 00:22:52,160
Speaker 1: two distinct cloud services that do not communicate with each other.

385
00:22:52,440 --> 00:22:54,359
Speaker 1: And there are valid reasons to go with a multi

386
00:22:54,359 --> 00:22:58,960
Speaker 1: cloud approach where there is no communication between clouds. So again,

387
00:22:59,040 --> 00:23:02,000
Speaker 1: let's talk about a really big company. Really big companies

388
00:23:02,040 --> 00:23:05,800
Speaker 1: could have departments. They're so large and have such specific

389
00:23:05,840 --> 00:23:10,160
Speaker 1: needs they do not need to communicate directly with other

390
00:23:10,200 --> 00:23:14,359
Speaker 1: departments to get their their work done. Their their work

391
00:23:14,400 --> 00:23:17,160
Speaker 1: may not relate directly with other departments, so in those

392
00:23:17,200 --> 00:23:19,680
Speaker 1: cases it might make more sense to have a separate

393
00:23:19,720 --> 00:23:25,520
Speaker 1: public or private cloud available to those departments either, so

394
00:23:25,600 --> 00:23:28,840
Speaker 1: you can have multi cloud approach that uses either private

395
00:23:28,960 --> 00:23:32,240
Speaker 1: or public or both. The important element here is that

396
00:23:32,280 --> 00:23:35,720
Speaker 1: there isn't that communication channel between them. So as companies

397
00:23:35,720 --> 00:23:38,879
Speaker 1: grow they often find that the departments inside them end

398
00:23:38,960 --> 00:23:41,600
Speaker 1: up being larger than the original company was when it

399
00:23:41,640 --> 00:23:45,000
Speaker 1: first got started, and at that size, finding processes that

400
00:23:45,160 --> 00:23:48,919
Speaker 1: work and scale is critical. Now, at the top of

401
00:23:48,920 --> 00:23:50,760
Speaker 1: the show, I mentioned that one of the big concerns

402
00:23:50,760 --> 00:23:54,200
Speaker 1: about cloud computing was security, and with so many companies

403
00:23:54,200 --> 00:23:57,639
Speaker 1: in trusting data and processes to third parties, security is

404
00:23:57,680 --> 00:24:01,200
Speaker 1: an absolute necessity without a company would go out of business.

405
00:24:01,720 --> 00:24:04,879
Speaker 1: Third parties would collapse if they were found to be insecure,

406
00:24:04,960 --> 00:24:07,320
Speaker 1: and like I said, that could create a domino effect,

407
00:24:07,359 --> 00:24:11,280
Speaker 1: a disastrous result among the third party's customer base. So

408
00:24:11,320 --> 00:24:16,080
Speaker 1: typically cloud computing applications rely upon profiles that require authentication

409
00:24:16,160 --> 00:24:19,480
Speaker 1: through some means, most commonly through a user name and password.

410
00:24:20,000 --> 00:24:23,240
Speaker 1: There are cloud based services that actually managed this as well.

411
00:24:23,680 --> 00:24:26,640
Speaker 1: It's called identity as a Service or i d a

412
00:24:26,640 --> 00:24:30,240
Speaker 1: a s NOW. Those services and not only managed user

413
00:24:30,320 --> 00:24:33,760
Speaker 1: log in information, but can designate different levels of access.

414
00:24:33,840 --> 00:24:36,680
Speaker 1: So for example, the head of a department might need

415
00:24:36,720 --> 00:24:41,080
Speaker 1: administrator level access to that department's data, while an employee

416
00:24:41,080 --> 00:24:44,920
Speaker 1: further down the organizational chain might only require limited access.

417
00:24:45,359 --> 00:24:47,520
Speaker 1: So you need to have a way of denoting that

418
00:24:47,960 --> 00:24:52,440
Speaker 1: so that you don't have some low level employee suddenly

419
00:24:52,480 --> 00:24:57,280
Speaker 1: have access to say, everyone's pace stub. That would be bad.

420
00:24:58,000 --> 00:25:02,240
Speaker 1: Cloud providers take security seriously and it shows because they

421
00:25:02,400 --> 00:25:05,320
Speaker 1: might prove to be attempting target to hackers. But the

422
00:25:05,359 --> 00:25:09,080
Speaker 1: big providers of technically been more resilient to attacks than

423
00:25:09,119 --> 00:25:12,640
Speaker 1: private enterprise centers have been in the past. The weak

424
00:25:12,720 --> 00:25:16,120
Speaker 1: spot tends not to be the data centers or the

425
00:25:16,160 --> 00:25:21,080
Speaker 1: providers or the security around them, but rather that tenuous

426
00:25:21,200 --> 00:25:25,440
Speaker 1: link between provider and customer. It's that old problem where

427
00:25:25,480 --> 00:25:27,600
Speaker 1: you find out that the weakest link in the security

428
00:25:27,600 --> 00:25:33,160
Speaker 1: system is the people. It's always the people, because people

429
00:25:33,280 --> 00:25:36,920
Speaker 1: might choose really bad passwords, which creates a security vulnerability

430
00:25:36,920 --> 00:25:39,760
Speaker 1: that's difficult to protect against. You might use something like

431
00:25:39,760 --> 00:25:43,919
Speaker 1: two factor authentication to help fight against that problem. That

432
00:25:43,960 --> 00:25:45,800
Speaker 1: can help. That's where a person needs not only a

433
00:25:45,800 --> 00:25:49,600
Speaker 1: password but some other form of authentication like a physical

434
00:25:49,680 --> 00:25:53,000
Speaker 1: token in order to access the services. And at that

435
00:25:53,119 --> 00:25:56,240
Speaker 1: stage and an authorized person would need to get hold

436
00:25:56,280 --> 00:25:58,680
Speaker 1: not just to the password, but also of some sort

437
00:25:58,680 --> 00:26:01,520
Speaker 1: of physical object a token or a cell phone or

438
00:26:01,560 --> 00:26:05,680
Speaker 1: something that the target owns. So it doesn't eliminate risk,

439
00:26:05,840 --> 00:26:08,920
Speaker 1: but it cuts down on it significantly. There are a

440
00:26:08,960 --> 00:26:12,199
Speaker 1: lot of other buzzword like topics that tie in with

441
00:26:12,200 --> 00:26:15,240
Speaker 1: cloud computing. There's big data that's all about how to

442
00:26:15,320 --> 00:26:18,119
Speaker 1: leverage the huge amounts of information that's being mined on

443
00:26:18,160 --> 00:26:20,119
Speaker 1: a daily basis. How do you do that in a

444
00:26:20,160 --> 00:26:24,720
Speaker 1: way that's meaningful and efficient. That's frequently associated with cloud computing.

445
00:26:24,840 --> 00:26:28,120
Speaker 1: Artificial intelligence and machine learning also get thrown into the mix.

446
00:26:28,400 --> 00:26:31,200
Speaker 1: There are services that use machine learning to sort through

447
00:26:31,320 --> 00:26:34,119
Speaker 1: big data on the cloud, for example. That way you

448
00:26:34,160 --> 00:26:37,560
Speaker 1: get a big, heaping handful of buzzwords all at once,

449
00:26:38,119 --> 00:26:41,159
Speaker 1: that's all in an effort to produce a particular result.

450
00:26:41,240 --> 00:26:44,840
Speaker 1: Sometimes you use machine learning and big data for legitimate

451
00:26:44,840 --> 00:26:49,120
Speaker 1: research purposes that can further our scientific understanding. Sometimes it's

452
00:26:49,160 --> 00:26:51,840
Speaker 1: for less lofty reasons. It might be to find the

453
00:26:51,880 --> 00:26:54,280
Speaker 1: best way to advertise to different groups of people in

454
00:26:54,359 --> 00:26:58,159
Speaker 1: order to more effectively sell products to them. Sometimes you

455
00:26:58,240 --> 00:27:02,160
Speaker 1: can get downright creepy, which as the recent Cambridge Analytical story.

456
00:27:02,600 --> 00:27:04,280
Speaker 1: Have to do a full episode about what that was

457
00:27:04,320 --> 00:27:06,639
Speaker 1: all about. Why raised such a fuss at some point,

458
00:27:07,480 --> 00:27:11,960
Speaker 1: But that's that's for another time. It's a complicated issue

459
00:27:11,960 --> 00:27:14,000
Speaker 1: that I can't just if I ran into it here,

460
00:27:14,040 --> 00:27:16,720
Speaker 1: we would easily go another hour, and UH actually have

461
00:27:16,800 --> 00:27:18,800
Speaker 1: to be at the conference keynote in a little bit,

462
00:27:18,880 --> 00:27:21,200
Speaker 1: so I can't do that now. At the top of

463
00:27:21,240 --> 00:27:23,240
Speaker 1: the show, I also mentioned that there can be an

464
00:27:23,280 --> 00:27:28,359
Speaker 1: issue about ownership, like who owns the data on these services,

465
00:27:28,400 --> 00:27:31,560
Speaker 1: and usually there's some pretty complicated terms of service that

466
00:27:31,640 --> 00:27:35,359
Speaker 1: lays all this out, where you're essentially granting a license

467
00:27:35,480 --> 00:27:40,520
Speaker 1: to the third party to have a sort of physical

468
00:27:40,560 --> 00:27:44,399
Speaker 1: possession of all that information. And the reason that's necessary

469
00:27:44,520 --> 00:27:47,119
Speaker 1: is because the way cloud computing works, the way you

470
00:27:47,160 --> 00:27:52,639
Speaker 1: can access it through different devices and in different places,

471
00:27:53,000 --> 00:27:55,160
Speaker 1: you have to give the company permission to be able

472
00:27:55,240 --> 00:27:59,679
Speaker 1: to show that information. So if I store a file

473
00:27:59,760 --> 00:28:02,720
Speaker 1: on my personal computer and I'm the only one who

474
00:28:02,720 --> 00:28:06,200
Speaker 1: has physical access to my personal computer, I'm reasonably assured

475
00:28:06,359 --> 00:28:08,440
Speaker 1: that I'm the only person who can see it. There's

476
00:28:08,440 --> 00:28:11,560
Speaker 1: no real permissions that need to be made or anything

477
00:28:11,600 --> 00:28:15,080
Speaker 1: like that. If I'm storing my data on someone else's

478
00:28:15,160 --> 00:28:18,560
Speaker 1: machine and I want it to be clear that still

479
00:28:18,760 --> 00:28:22,480
Speaker 1: my data even though it's sitting on someone else's machine,

480
00:28:22,920 --> 00:28:25,040
Speaker 1: then we have to draw up these agreements. And one

481
00:28:25,040 --> 00:28:28,240
Speaker 1: of those agreements might be well, if you request to

482
00:28:28,840 --> 00:28:31,560
Speaker 1: see and work with this data, I need to have

483
00:28:31,640 --> 00:28:35,240
Speaker 1: the permission to actually serve it to you. If you

484
00:28:35,400 --> 00:28:37,439
Speaker 1: choose to share it with someone, I have to have

485
00:28:37,520 --> 00:28:40,280
Speaker 1: the permission that allows me to share it so that

486
00:28:40,400 --> 00:28:44,760
Speaker 1: I'm not legally responsible If you later on say, oh

487
00:28:44,800 --> 00:28:47,360
Speaker 1: I didn't I don't want that person to have it. Well,

488
00:28:47,400 --> 00:28:49,880
Speaker 1: if you chose to share it, by the nature of

489
00:28:49,920 --> 00:28:54,280
Speaker 1: the cloud computing model, then you know that that's what happened,

490
00:28:54,440 --> 00:28:57,960
Speaker 1: that it got shared. So it's largely to protect the

491
00:28:58,040 --> 00:29:01,560
Speaker 1: third party providers, but it's also just this very complicated

492
00:29:01,600 --> 00:29:04,560
Speaker 1: language that gives them the permission they need to do

493
00:29:04,640 --> 00:29:07,320
Speaker 1: the business as they do it. So it can get

494
00:29:07,360 --> 00:29:10,480
Speaker 1: pretty complicated. On casual glance, it looks like you're signing

495
00:29:10,520 --> 00:29:15,360
Speaker 1: over all your data to another party, um, and you're

496
00:29:15,440 --> 00:29:18,720
Speaker 1: not really doing that, at least not in most agreements.

497
00:29:18,840 --> 00:29:22,120
Speaker 1: Any ethical agreement would not include that sort of information

498
00:29:22,160 --> 00:29:26,320
Speaker 1: in it. But that's pretty much the overview of cloud computing.

499
00:29:26,320 --> 00:29:29,080
Speaker 1: Will conclude our story on that. It's a fascinating model

500
00:29:29,120 --> 00:29:30,800
Speaker 1: that has its roots all the way back in the

501
00:29:30,840 --> 00:29:36,760
Speaker 1: early days of of of mainframe computers. I hope you

502
00:29:36,800 --> 00:29:39,000
Speaker 1: found this episode helpful and understanding what the heck that

503
00:29:39,000 --> 00:29:41,960
Speaker 1: buzzword meant in the first place. If you have suggestions

504
00:29:42,000 --> 00:29:44,360
Speaker 1: for future episodes of tech Stuff, whether it is to

505
00:29:44,480 --> 00:29:48,560
Speaker 1: explain something within tech, to talk about a specific technology

506
00:29:48,640 --> 00:29:50,960
Speaker 1: or a company or a person in technology. Maybe you

507
00:29:51,000 --> 00:29:53,440
Speaker 1: have someone in mind that I should interview or have

508
00:29:53,560 --> 00:29:56,120
Speaker 1: on as a guest. Reach out and let me know.

509
00:29:56,760 --> 00:29:59,440
Speaker 1: You can send me and email the addresses tech Stuff

510
00:29:59,520 --> 00:30:02,080
Speaker 1: at how stuff works dot com or draw me a

511
00:30:02,120 --> 00:30:05,120
Speaker 1: line on Facebook or Twitter. The handle for both of

512
00:30:05,160 --> 00:30:09,480
Speaker 1: those is text stuff hs W. Follow us on Instagram.

513
00:30:09,520 --> 00:30:11,960
Speaker 1: We've got an Instagram account that we post to regularly,

514
00:30:12,040 --> 00:30:15,560
Speaker 1: so check that out. And remember, on normal weeks, I

515
00:30:15,640 --> 00:30:18,640
Speaker 1: record this show on Wednesdays and Fridays. You can actually

516
00:30:18,640 --> 00:30:21,440
Speaker 1: watch me record it live in the studio. You can

517
00:30:21,520 --> 00:30:25,080
Speaker 1: just go to the link Twitch dot tv slash tech Stuff.

518
00:30:25,080 --> 00:30:27,920
Speaker 1: You'll see the schedule there, and there's a chat room

519
00:30:27,920 --> 00:30:30,000
Speaker 1: and everything. You can join in be part of the crowd.

520
00:30:30,400 --> 00:30:32,080
Speaker 1: And I look forward to seeing you and I'll talk

521
00:30:32,120 --> 00:30:40,520
Speaker 1: to you again really soon. For more on this and

522
00:30:40,560 --> 00:30:43,080
Speaker 1: bouthands of other topics, is it how staff works dot

523
00:30:43,160 --> 00:30:52,880
Speaker 1: com