1
00:00:00,850 --> 00:00:02,340
My name is Matthew Graham.

2
00:00:02,340 --> 00:00:07,500
And in this set of six modules, I will
be talking about data and databases.

3
00:00:07,500 --> 00:00:13,250
This first module we'll be doing
a general consideration of data and

4
00:00:13,250 --> 00:00:16,620
some theory about, behind databases.

5
00:00:17,980 --> 00:00:21,720
This is an overview of
the module structure.

6
00:00:21,720 --> 00:00:22,757
The second one,
we will be talking about data models.

7
00:00:22,757 --> 00:00:29,910
In the third module, we will be
talking about relational databases,

8
00:00:29,910 --> 00:00:33,850
in particular, since they are the most
common database technology used currently.

9
00:00:35,150 --> 00:00:38,750
In the fourth and fifth modules,
I'll be talking about

10
00:00:39,890 --> 00:00:44,760
SQL which is the language
you use to query databases.

11
00:00:44,760 --> 00:00:47,360
Module four will be the basics of SQL.

12
00:00:47,360 --> 00:00:51,250
Module five will be about some
more advanced features of SQL.

13
00:00:51,250 --> 00:00:55,830
And then, in Module six we will
consider alternative databases to

14
00:00:55,830 --> 00:01:00,975
relational databases to consider
such thing as, as NoSQL, and

15
00:01:00,975 --> 00:01:05,260
NewSQL, and other trends that
are current in big data analytics.

16
00:01:07,070 --> 00:01:13,940
Building on top of relational databases
but alternative solutions to that.

17
00:01:13,940 --> 00:01:17,550
So, it behooves us, I think, to try and

18
00:01:17,550 --> 00:01:20,840
understand exactly what it
is when we talk about data.

19
00:01:20,840 --> 00:01:22,710
What do we mean by what is data?

20
00:01:23,850 --> 00:01:29,070
We have some understanding that we
understand what we mean by that,

21
00:01:29,070 --> 00:01:33,970
but really, what were talking about
of the values of qualitative or

22
00:01:33,970 --> 00:01:36,748
quantitative variables
belonging to a set of items.

23
00:01:36,748 --> 00:01:44,202
You have some object that
you want to describe.

24
00:01:44,202 --> 00:01:50,210
You've measured, you've observed
it in the scientific context and

25
00:01:50,210 --> 00:01:56,086
those things that you've measured
have values which may be integers,

26
00:01:56,086 --> 00:02:02,730
they may be numbers, they may be strings,
they may be more composite objects.

27
00:02:02,730 --> 00:02:06,270
And those are the things that we
refer to when we talk about data.

28
00:02:06,270 --> 00:02:07,664
It is that that we use.

29
00:02:10,910 --> 00:02:13,830
An important aspect of data is metadata.

30
00:02:13,830 --> 00:02:15,690
This is also data.

31
00:02:16,810 --> 00:02:19,080
And arguably it's more important.

32
00:02:19,080 --> 00:02:22,660
Metadata is the set of
data which tells you

33
00:02:22,660 --> 00:02:28,288
something about the global properties of
the data set that you're dealing with.

34
00:02:28,288 --> 00:02:32,511
If I was taking data at a telescope,
the metadata might be the,

35
00:02:32,511 --> 00:02:34,505
the instrumental settings or

36
00:02:34,505 --> 00:02:40,300
the the weather conditions or the quality
of the night, that sort of thing.

37
00:02:40,300 --> 00:02:45,926
It's a, a, a set of data that is about
the actual binary data, the imaging data,

38
00:02:45,926 --> 00:02:50,475
or the spectroscopy data that's
being taken by the telescope but

39
00:02:50,475 --> 00:02:55,100
it is constant for all the images
that I might take in a night.

40
00:02:55,100 --> 00:03:00,715
Similarly, if I was doing a biology
experiment, then if I was doing a gene,

41
00:03:00,715 --> 00:03:03,620
let's say of many genes the actual.

42
00:03:05,100 --> 00:03:08,710
Gene strings that I'm getting from
the gene of A would be very different from

43
00:03:08,710 --> 00:03:15,040
gene to gene but the actual experimental
setup, maybe the same in all the cases and

44
00:03:15,040 --> 00:03:19,070
then I would have metadata arising
from the instrumental settings on

45
00:03:19,070 --> 00:03:23,204
the experiment,
about the data sets that I'm getting.

46
00:03:25,015 --> 00:03:27,310
Metadata is also data, however, and so

47
00:03:27,310 --> 00:03:30,730
all the things that we can do with
data we can also do with metadata.

48
00:03:32,960 --> 00:03:38,320
There is this idea that, the,
the purpose of doing data analytics or

49
00:03:38,320 --> 00:03:43,948
doing data science is to extract
information out of data.

50
00:03:43,948 --> 00:03:48,160
And there's, there's this particular
pyramid structure that you can see,

51
00:03:48,160 --> 00:03:51,930
where you have data on the bottom layer,
and the idea is,

52
00:03:51,930 --> 00:03:56,990
is that the cognitive value of
the what you're doing increases as you

53
00:03:56,990 --> 00:04:01,490
progress up the pyramid, so
from data we extract information.

54
00:04:01,490 --> 00:04:04,040
We combine information
together to gain knowledge and

55
00:04:04,040 --> 00:04:07,660
then ultimately we're after wisdom
derived from the knowledge.

56
00:04:07,660 --> 00:04:11,550
It's possibly a somewhat philosophical or
fanciful notion but

57
00:04:11,550 --> 00:04:16,000
certainly the types of techniques
that you're hearing about in this

58
00:04:16,000 --> 00:04:21,350
course are relevant for
getting information out of the data.

59
00:04:24,350 --> 00:04:29,560
An example of the power of metadata,
over data is,

60
00:04:29,560 --> 00:04:33,090
in the left-hand side you can
see images of comet home 17p.

61
00:04:33,090 --> 00:04:40,512
This is a comet that went,
was observed in 2007,

62
00:04:42,120 --> 00:04:47,600
Many people posted images on Flickr
that they had taken with cell phones and

63
00:04:47,600 --> 00:04:52,570
very amateur camera about the,
of the, of the comet.

64
00:04:52,570 --> 00:04:57,530
And, it was thought that there must be

65
00:04:57,530 --> 00:05:01,320
some way that we could use this
information to improve our knowledge.

66
00:05:01,320 --> 00:05:06,310
Of the, orbit of this particular comet.

67
00:05:06,310 --> 00:05:10,898
And although the the images
themselves are all ones and zeros.

68
00:05:10,898 --> 00:05:15,110
Standard camera image.

69
00:05:15,110 --> 00:05:18,570
What is useful is not that data precisely.

70
00:05:18,570 --> 00:05:22,259
What is more useful is the metadata for
each image.

71
00:05:23,410 --> 00:05:26,670
And saying when it was taken,
where it was taken.

72
00:05:28,670 --> 00:05:33,560
Using some notion of which particular
bit of the comet's orbit it was

73
00:05:33,560 --> 00:05:38,120
capturing latitude and longitude
of where the camera was situated.

74
00:05:38,120 --> 00:05:39,380
That sort of thing.

75
00:05:39,380 --> 00:05:42,470
And from the metadata
attached to each image.

76
00:05:42,470 --> 00:05:48,800
It has been possible to
combine the images into

77
00:05:48,800 --> 00:05:51,600
a single hall,
a single scientific data set.

78
00:05:52,960 --> 00:05:55,800
Properly calibrated,
each of those individual images,

79
00:05:55,800 --> 00:06:02,200
properly registered onto a scientific
basis, on to an underlying framework.

80
00:06:02,200 --> 00:06:09,060
And therefore,
put together a much more complete dataset.

81
00:06:09,060 --> 00:06:13,560
And then apply analytics to
that more complete dataset.

82
00:06:13,560 --> 00:06:20,860
And get a much more accurate, orbital
solution for this particular object.

83
00:06:20,860 --> 00:06:24,410
And that demonstrates the power
of metadata as a form of data in

84
00:06:24,410 --> 00:06:25,380
its own right.

85
00:06:26,990 --> 00:06:29,898
So, as I say,
metadata can be very important.

86
00:06:29,898 --> 00:06:33,070
Particularly useful when you're dealing
with these very large datasets as we are.

87
00:06:35,390 --> 00:06:38,550
To give some idea of the scale of
the data that we're working with,

88
00:06:38,550 --> 00:06:43,870
that we're possibly considering as
the large end of big data analytics.

89
00:06:45,270 --> 00:06:50,580
We talk happily today about gigabytes
of data, and terabytes of data.

90
00:06:51,940 --> 00:06:55,040
This laptop has a Terabyte disk in it.

91
00:06:55,040 --> 00:06:56,660
But a lot of the data we're talking about,
or

92
00:06:56,660 --> 00:07:01,220
thinking about, for the next five,
10 years, is in the Peta scale.

93
00:07:01,220 --> 00:07:03,170
And possibly even larger,

94
00:07:03,170 --> 00:07:08,455
going towards the So,
a Petabyte of data seems a large amount.

95
00:07:08,455 --> 00:07:12,150
It's enough to store the DNA
of the entire US population.

96
00:07:12,150 --> 00:07:18,690
If I took the DNA of every member of
the 300 million population of the US,

97
00:07:18,690 --> 00:07:21,290
I could put that into
a petabyte data set and then I

98
00:07:21,290 --> 00:07:26,210
could clone each of those individuals
twice over also take that DNA, put it in.

99
00:07:27,780 --> 00:07:29,460
And that would define a petabyte of data.

100
00:07:30,670 --> 00:07:36,410
If I was listening to music on
my MP3 player, a petabyte of

101
00:07:36,410 --> 00:07:41,180
of of songs would last me about
2,000 years of continuous playback.

102
00:07:41,180 --> 00:07:43,720
A petabyte of data is about 13 and

103
00:07:43,720 --> 00:07:47,520
a third years of high definition video,
such as you're watching now.

104
00:07:49,600 --> 00:07:55,960
And in the social media
world where there actually

105
00:07:55,960 --> 00:08:00,490
more used to these very large amounts
of streaming data, a petabyte of

106
00:08:00,490 --> 00:08:04,600
data is about one tweet per person on
the planet per day for seven weeks.

107
00:08:05,960 --> 00:08:11,020
So, obviously, in a year, Twitter is
creating, if everyone on the planet was

108
00:08:11,020 --> 00:08:15,900
tweeting, that would create somewhere
between seven and eight petabytes of data.

109
00:08:15,900 --> 00:08:17,620
So, it gives you an idea of
what a petabyte of data is.

110
00:08:17,620 --> 00:08:20,140
It may seem a lot.

111
00:08:20,140 --> 00:08:25,460
But in a scientific context in,
in astronomy, the large.

112
00:08:26,970 --> 00:08:33,670
Large Synoptic Sky Telescope LSST, will
produce a petabyte of data, in 60 nights.

113
00:08:33,670 --> 00:08:36,130
Comes online, at the end of this decade.

114
00:08:36,130 --> 00:08:41,140
And SKA, the Square kilometer Array Radio
Telescope will produce a petabyte of

115
00:08:41,140 --> 00:08:42,160
data in about 90 seconds.

116
00:08:42,160 --> 00:08:46,730
So, these are much more
believable data sets, than.

117
00:08:48,410 --> 00:08:50,160
You know, 13 years of video.

118
00:08:50,160 --> 00:08:55,370
But it demonstrates that these scale,
datasets,

119
00:08:55,370 --> 00:08:57,950
will become very commonplace
in the next decade.

120
00:08:57,950 --> 00:09:00,540
And we therefore need ways
of managing that data.

121
00:09:00,540 --> 00:09:02,360
And, and working with that data.

122
00:09:02,360 --> 00:09:05,970
In addition to, techniques for,
for analyzing it.

123
00:09:09,690 --> 00:09:15,660
Programmatically, when we work with data,
we, we think of turning those values

124
00:09:15,660 --> 00:09:24,120
of the variables connected to the set of
objects into integers and floats or bytes.

125
00:09:25,150 --> 00:09:29,010
We may put them into numerical arrays.

126
00:09:29,010 --> 00:09:34,190
The string for to put them into character
strings, so we have a set of primitives in

127
00:09:34,190 --> 00:09:39,160
all of our computer languages for
dealing with the values of those data.

128
00:09:39,160 --> 00:09:42,780
And then those values we put
into more complicated structures

129
00:09:43,790 --> 00:09:47,340
to contain notions of how we

130
00:09:47,340 --> 00:09:51,300
can structure that data into something
that's meaningful at a programmatic level.

131
00:09:51,300 --> 00:09:59,551
So we define data structures such as
arrays, or maps, lists, sets, collections.

132
00:09:59,551 --> 00:10:03,700
We will define queues for
certain operations.

133
00:10:05,250 --> 00:10:06,250
On larger scales,

134
00:10:06,250 --> 00:10:11,250
the more complicated systems we have data
structures such as trees and graphs.

135
00:10:11,250 --> 00:10:12,030
And the list goes on and on.

136
00:10:12,030 --> 00:10:17,440
We do this because it makes
our lives easier to have

137
00:10:17,440 --> 00:10:23,150
a programmatic representation of
what the structure of the data is.

138
00:10:23,150 --> 00:10:24,600
So that we can

139
00:10:25,860 --> 00:10:29,180
write good computer code against it
to make the analysis more efficient.

140
00:10:30,750 --> 00:10:31,340
In, in that fashion.

141
00:10:33,600 --> 00:10:34,500
But that's programmatically.

142
00:10:36,810 --> 00:10:42,760
There is a notion that data should
also possibly be structured before

143
00:10:42,760 --> 00:10:47,510
it comes into the programmer and into the
computer program the programmatic arena.

144
00:10:48,680 --> 00:10:51,680
To make it easier as well for
purposes of data management.

145
00:10:51,680 --> 00:10:55,590
It should be said, that there is no un,
unique solution for working with data.

146
00:10:57,840 --> 00:11:02,020
You can represent the same chunk of data,

147
00:11:02,020 --> 00:11:05,332
through any one of these
programmatic structures.

148
00:11:05,332 --> 00:11:11,250
there, they will transition or map,
from one to the other, quite easily.

149
00:11:13,220 --> 00:11:17,360
It depends on the problem being addressed,
what you're trying to do with it,

150
00:11:17,360 --> 00:11:19,550
and also personal preference.

151
00:11:19,550 --> 00:11:24,020
I may favor, an array over a list.

152
00:11:24,020 --> 00:11:25,780
Or I may be programming in Python,

153
00:11:25,780 --> 00:11:30,079
which has its own particular language
preferences for the, for data structure.

154
00:11:31,320 --> 00:11:33,410
So there's no unique solution.

155
00:11:33,410 --> 00:11:36,240
Similarly when we come to
talking about data storage and

156
00:11:36,240 --> 00:11:41,760
data access, data management there is
again not necessarily a unique solution.

157
00:11:41,760 --> 00:11:44,330
It may depend on the problem
being addressed and

158
00:11:44,330 --> 00:11:47,510
also your personal preference.

159
00:11:47,510 --> 00:11:50,560
But broadly I would argue that
you can consider data to be

160
00:11:50,560 --> 00:11:52,548
regarded as structured.

161
00:11:52,548 --> 00:11:55,490
Or, or connected.

162
00:11:55,490 --> 00:12:01,720
By structured I, I mean something similar
that you could put it into tabular form.

163
00:12:01,720 --> 00:12:04,530
A lot of data can be
represented in that way.

164
00:12:04,530 --> 00:12:10,040
By connected data I mean slightly
more loose structure, but

165
00:12:10,040 --> 00:12:12,140
you can still associate
bits of data together.

166
00:12:13,250 --> 00:12:16,130
A good example is Wikipedia.

167
00:12:17,480 --> 00:12:20,060
For a lot of entries in Wikipedia
there is a set of facts.

168
00:12:21,080 --> 00:12:24,800
And for a lot of those entries,
those set of facts may be similar.

169
00:12:24,800 --> 00:12:28,770
Let us take all the descriptions
of famous people.

170
00:12:28,770 --> 00:12:33,550
There may be for each of them where
they were born, when they were born.

171
00:12:33,550 --> 00:12:37,150
Where they died, when they died,
who they married, who their parents were.

172
00:12:37,150 --> 00:12:42,650
All sorts of things like that, You could
imagine tabulating that information and

173
00:12:42,650 --> 00:12:48,040
then you, for each one, you have a tabular
data structure that you can repeat.

174
00:12:48,040 --> 00:12:51,580
But there may be other facts that
are associated with that person.

175
00:12:51,580 --> 00:12:55,720
Where they went to school,
what their area of expertise was,

176
00:12:55,720 --> 00:12:57,880
which soccer team they played for.

177
00:12:57,880 --> 00:13:00,410
That may not be true for everyone, but

178
00:13:00,410 --> 00:13:06,230
you still want to structure those facts
together in, in a meaningful way.

179
00:13:06,230 --> 00:13:08,280
And in a way that can be utilized.

180
00:13:08,280 --> 00:13:11,239
And in that case,
you'd be talking about connected data and

181
00:13:11,239 --> 00:13:16,758
you would be working with
data structure that way.

182
00:13:16,758 --> 00:13:21,347
There is a thing called
the linked open data movement,

183
00:13:21,347 --> 00:13:26,432
which I will talk more about
in my final module which

184
00:13:26,432 --> 00:13:31,950
works on that sort of basis and
is useful for doing web discovery.

185
00:13:33,180 --> 00:13:36,490
So let's talk about more of
the meat of what we're here to,

186
00:13:36,490 --> 00:13:41,870
to, focus on in these modules,
which are databases, so I've talked about,

187
00:13:41,870 --> 00:13:46,410
having programmatic structures for
working with data, and how, with data,

188
00:13:46,410 --> 00:13:51,750
we can also have, notions of that there
is some sort of structure behind them.

189
00:13:51,750 --> 00:13:52,880
So what is a database?

190
00:13:52,880 --> 00:13:55,410
A database is a structured
collection of data

191
00:13:56,630 --> 00:14:01,870
residing on a computer system at can be
easily be accessed, managed, and updated.

192
00:14:03,020 --> 00:14:07,070
So we're taking that innate
structure in our data and

193
00:14:07,070 --> 00:14:08,640
we're formalizing it in some way.

194
00:14:10,268 --> 00:14:12,410
That is a database model.

195
00:14:12,410 --> 00:14:13,740
And there are different database models,

196
00:14:15,390 --> 00:14:17,410
depending on how you want
to work with your data.

197
00:14:17,410 --> 00:14:18,990
I'll talk about that in the next module.

198
00:14:20,540 --> 00:14:22,310
But the whole idea is
that in the database,

199
00:14:22,310 --> 00:14:25,490
data is organized according
to the database model.

200
00:14:25,490 --> 00:14:26,820
It's structured.

201
00:14:26,820 --> 00:14:28,950
And then you have a database
management system.

202
00:14:30,240 --> 00:14:31,170
A DBMS.

203
00:14:31,170 --> 00:14:33,090
Which is a software layer,

204
00:14:33,090 --> 00:14:38,630
software package, designed to store and
manage databases.

205
00:14:38,630 --> 00:14:44,470
So your data store is in a database,
and you have the DBMS to work with

206
00:14:45,620 --> 00:14:51,120
the database to provide you with the,
the software tools, the software

207
00:14:51,120 --> 00:14:55,150
interfaces to access the data,
to manage the data, to update the data.

208
00:14:56,430 --> 00:14:57,920
And do those sort of things.

209
00:14:59,540 --> 00:15:01,370
This is a great cartoon from XKCD.

210
00:15:01,370 --> 00:15:06,030
It shows some of the dangers of,
of databases.

211
00:15:06,030 --> 00:15:09,580
Hopefully by the time we
finish this set of modules,

212
00:15:09,580 --> 00:15:11,540
the punchline will be more obvious to you.

213
00:15:14,120 --> 00:15:16,210
Why should I use a DBMS.

214
00:15:16,210 --> 00:15:18,710
Why should I bother to learn.

215
00:15:18,710 --> 00:15:24,340
Database technology, surely if I
structured the database myself, or

216
00:15:24,340 --> 00:15:28,970
if I have a good idea of how data
is structured on my hard drive,

217
00:15:28,970 --> 00:15:32,910
I don't need to bother with the,
the necessities of

218
00:15:32,910 --> 00:15:36,240
the [INAUDIBLE] necessities of
a database management system.

219
00:15:38,120 --> 00:15:40,650
Well, a DBMS buys you a number of things.

220
00:15:44,680 --> 00:15:49,040
It takes you away from the data in
the sense that you do not need to

221
00:15:50,440 --> 00:15:52,220
worry about the data being somewhere,

222
00:15:52,220 --> 00:15:56,170
the data is independent, just sort of
abstraction layer on top of the data.

223
00:15:57,890 --> 00:16:01,870
And allows you to consider
it in a more abstract way.

224
00:16:03,620 --> 00:16:09,310
It is normally optimized for
efficient access.

225
00:16:09,310 --> 00:16:12,636
I certainly could write a program which
would go through a directory structure

226
00:16:12,636 --> 00:16:15,148
trying to find the bits of data
that meet the type of search or

227
00:16:15,148 --> 00:16:17,190
query that I'm trying to do.

228
00:16:17,190 --> 00:16:21,610
But because a database management
system is optimized for

229
00:16:21,610 --> 00:16:23,600
that sort of work it would
be a more efficient way.

230
00:16:23,600 --> 00:16:28,560
It also allows concurrent access in
the sense that there may be many

231
00:16:28,560 --> 00:16:32,700
people who want to access the data that
I'm after because I'm using a DBMS that

232
00:16:32,700 --> 00:16:36,200
may offer a number of client's
access to the data that I'm after.

233
00:16:38,320 --> 00:16:41,450
DBMS will buy you data integrity,
security and safety.

234
00:16:41,450 --> 00:16:43,560
This is important.

235
00:16:43,560 --> 00:16:47,200
It'll, make sure that you don't
do anything silly with your data,

236
00:16:47,200 --> 00:16:49,230
like deleting it accidentally.

237
00:16:49,230 --> 00:16:53,799
It will make sure it's secure, if you want
it to be, so no one can change the values.

238
00:16:55,100 --> 00:16:58,160
That only possibly certain people
can get access to your data.

239
00:16:58,160 --> 00:16:59,260
If it's proprietary data.

240
00:17:01,790 --> 00:17:05,710
It allows uniform data
administration in the sense that

241
00:17:05,710 --> 00:17:09,580
I can put many different data
sets into a D.A.B.M.S. but

242
00:17:09,580 --> 00:17:14,850
I'm going to use the same
interface to manage that data.

243
00:17:17,270 --> 00:17:22,040
It means reduced application development
time because if I know that my data is

244
00:17:22,040 --> 00:17:28,920
sitting in a DBMS such as MySQL
then I only need to learn the MySQL

245
00:17:28,920 --> 00:17:34,580
interface once to be able to access any
data that is sitting in a MySQL database.

246
00:17:34,580 --> 00:17:36,690
Might as well be DBMS system anyway.

247
00:17:37,810 --> 00:17:42,030
So I don't need to continually rewrite
pieces of code factors stating different

248
00:17:42,030 --> 00:17:46,570
context, I only need to do it
once through the DBMS interface.

249
00:17:47,580 --> 00:17:51,250
And finally,
it gives me access to data analysis tools.

250
00:17:51,250 --> 00:17:52,389
There are a number of.

251
00:17:53,400 --> 00:17:56,640
Commercial applications which are build
for working with particular DBMS's.

252
00:17:56,640 --> 00:18:03,930
Some are better than others, but
it means that I can try those on my data.

253
00:18:03,930 --> 00:18:07,926
And it doesn't really matter what
my data reads, it is just data,

254
00:18:07,926 --> 00:18:10,996
numerical non numerical,.

255
00:18:10,996 --> 00:18:16,490
And the data analysis tool, which worked
for the particular type of numerical data

256
00:18:16,490 --> 00:18:20,950
that I can apply and I don't need to worry
about building the necessary interfaces or

257
00:18:20,950 --> 00:18:23,170
necessarily actually encoding
the algorithms either.

258
00:18:23,170 --> 00:18:27,820
It's already been done for me by someone,
and I can use those tools.

259
00:18:27,820 --> 00:18:31,400
So there are a number of advantages,
for using a DBMS over just.

260
00:18:32,420 --> 00:18:38,430
Putting your data on your hard drive and,
and, and doing the analysis way with it.

261
00:18:38,430 --> 00:18:40,350
Scales of databases.

262
00:18:40,350 --> 00:18:46,130
There's a great quote from Jim Gray,

263
00:18:46,130 --> 00:18:53,130
the Turing Award winner for database work.

264
00:18:53,130 --> 00:18:57,100
From Microsoft Research and Tony Hey from
2006 where they said that databases and

265
00:18:57,100 --> 00:19:01,280
sweet spots for managing data
form about 1 GB to about 100 TB.

266
00:19:01,280 --> 00:19:06,370
We've probably pushed the envelope at the,
the high end on that

267
00:19:06,370 --> 00:19:09,828
with some new stuff which I'll be talking
about in the sixth module in this series.

268
00:19:09,828 --> 00:19:14,250
But, essentially, You can have

269
00:19:14,250 --> 00:19:19,360
a very lightweight database management
system, SQLite is such an example,

270
00:19:19,360 --> 00:19:24,180
it's part of the Mac OS operating
system automatically, so

271
00:19:24,180 --> 00:19:28,220
you've got a database there, a relational
database that you could start with.

272
00:19:28,220 --> 00:19:30,770
If you want to move to something
slightly larger scale.

273
00:19:31,920 --> 00:19:37,664
MySQL, I've already mentioned,
PostgreSQL the other alternative,

274
00:19:37,664 --> 00:19:42,920
both from open source both widely used for

275
00:19:42,920 --> 00:19:48,720
free if you need something
slightly more enterprise scale,.

276
00:19:48,720 --> 00:19:55,720
SQLServer from Microsoft, or Oracle,
IBM all have their own solutions as well.

277
00:19:55,720 --> 00:19:57,260
Perfectly usable.

278
00:19:57,260 --> 00:20:01,720
And then for moving to the Web-scale,
there are things like Hive and

279
00:20:01,720 --> 00:20:07,180
Hadoop and then alternative non-relational
technologies like SciDB, and

280
00:20:07,180 --> 00:20:09,840
Redis, key-value stores.

281
00:20:09,840 --> 00:20:13,250
MonetDB, which is
column-oriented database.

282
00:20:13,250 --> 00:20:16,670
NuoDB which is a new SQL database.

283
00:20:17,770 --> 00:20:22,510
And I'll be talking about,
those again in, in the sixth module.

284
00:20:22,510 --> 00:20:28,290
So, there is a, a range of technology
solutions out there for you depending on.

285
00:20:29,430 --> 00:20:33,860
How much data you have and, and how much
money you have in certain circumstances.

286
00:20:36,320 --> 00:20:38,270
That certainly that whatever
your needs there is, a,

287
00:20:38,270 --> 00:20:39,470
a database solution out there for you.

288
00:20:39,470 --> 00:20:45,180
And I'll leave it there for the moment