1. A D D I N G N O S Q L T O
Y O U R A R S E N A L

2. A D D I N G N O S Q L T O
Y O U R A R S E N A L

3. A K A
T E N D ATA B A S E S
I N H A L F A N H O U R

4. SQL
D A TA B A S E # 1 :

5. T H E
I N D U S T RY
S TA N D A R D

6. R D B M S
( R E L AT I O N A L D ATA B A S E
M A N A G E M E N T S Y S T E M )

7. R D B M S
• Schema-driven
• Set-based operations
• ACID transactionality

8. S C H E M A
D R I V E N

9. Name Species

10. S E T- B A S E D
O P E R AT I O N
R E A D D A TA O U T W I T H

11. E V E RY R O W I S A “ T H I N G ”
Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess

12. “ W H E R E ” ( I N T E R S E C T I O N )
Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess

13. U N I O N S
Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess
5 Nemo
6 Moby Dick
7 Wanda

14. J O I N S
Name Species
Species Coolness
Rating
1 Puss 0
2 Dinah 0
3 Einstein 10
4 Jess 0

15. C A R T E S I A N P R O D U C T S
0 10
0 10
0 10

16. C A R T E S I A N P R O D U C T S
0 10
0 10
0 10

17. – R O N E R N E S T
( & T H E S Q L C O M M U N I T Y AT L A R G E )
“Cursors are evil.”

18. A C I D
W R I T E D A TA I N W I T H

19. Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess

20. DonaldPlutoMickey
{ }

21. Ducks aren’t mammals

22. Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess

23. The database is always in a valid state, as defined
by a whole number of queries
regardless of:
(1) invalid data;
(2) concurrent requests;
(3) system failures

24. The database is always in a valid state, as defined
by a whole number of queries
regardless of:
(1) invalid data;
(2) concurrent requests;
(3) system failures

25. The database is always in a valid state, as defined
by a whole number of queries
regardless of:
(1) invalid data;
(2) concurrent requests;
(3) system failures

26. The database is always in a valid state, as defined
by a whole number of queries
regardless of:
(1) invalid data;
(2) concurrent requests;
(3) system failures

27. A C I D
• Atomicity
• Consistency
• Isolation
• Durability

28. W H AT I S W R O N G
W I T H S Q L ?

29. N O T H I N G

30. N O T H I N G *
* As long as you use it for the right job

31. – M A S L O W ’ S H A M M E R
“If all you have is a hammer,
everything looks like a nail.”

32. T O C O M E
• 10 different ‘flavours’ of
NoSQL Databases
• Just enough to whet the
appetite!

33. MongoDB
D A TA B A S E # 2 :

34. D O C U M E N T
S T O R E

35. E V E RY R O W I S A “ T H I N G ”
Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess

36. E V E RY R O W I S A “ T H I N G ”
N A M E = P U S S
C O O L N E S S = 0
!
N A M E = J E S S
C O O L N E S S = 0
!
N A M E = D I N A H
C O O L N E S S = 0
!
N A M E = E I N S T E I N
C O O L N E S S = 1 0
!
D O C U M E N T

37. B E WA R E !

38. T H AT ’ S T H E P O I N T

39. D E N O R M A L I S E D D ATA
F O R E X A M P L E

40. E V E RY R O W I S A “ T H I N G ”
N A M E = P U S S
C O O L N E S S = 0
!
N A M E = J E S S
C O O L N E S S = 0
!
N A M E = D I N A H
C O O L N E S S = 0
!
N A M E = E I N S T E I N
C O O L N E S S = 1 0
!
D O C U M E N T

42. E A S Y
S H A R D I N G

44. G E O S PAT I A L
I N D E X E S

46. S C H E M A L E S S

47. Eloquera
D A TA B A S E # 3 :

48. O B J E C T
D ATA B A S E

49. E V E RY R O W I S A “ T H I N G ”
Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess

50. E V E RY R O W I S A “ T H I N G ”
N A M E = P U S S
C O O L N E S S = 0
!
N A M E = J E S S
C O O L N E S S = 0
!
N A M E = D I N A H
C O O L N E S S = 0
!
N A M E = E I N S T E I N
C O O L N E S S = 1 0
!
D O C U M E N T

51. E V E RY R O W I S A “ T H I N G ”
O B J E C T
public class Thing {
public int coolness { get; set; }
public string name { get; set; }
public Species species { get; set;}
}

52. T R A N S PA R E N C Y T O T H E D B

53. neo4j
D A TA B A S E # 4 :

54. G R A P H D ATA B A S E

56. N E O 4 J
I M P L E M E N T E D B Y …

58. T H E D ATA
I S T H E
R E L AT I O N S

62. Voldemort
D A TA B A S E # 5 :

63. – D Y N A M O : A M A Z O N ’ S H I G H LY AVA I L A B L E K E Y- VA L U E
S T O R E
“Reliability at massive scale is one of the biggest
challenges we face at Amazon.com. Even the
slightest outage has significant financial
consequences and impacts customer trust.”

64. – D Y N A M O : A M A Z O N ’ S H I G H LY AVA I L A B L E K E Y- VA L U E
S T O R E
“Experience at Amazon has shown that data stores
that provide ACID guarantees tend to have poor
availability”

65. – D Y N A M O : A M A Z O N ’ S H I G H LY AVA I L A B L E K E Y- VA L U E
S T O R E
“Dynamo targets applications that operate with
weaker consistency if this results in high
availability.”

66. C O N S I S T E N C Y
A
B C

67. C O N S I S T E N C Y
A
B C

68. D Y N A M O I M P L E M E N TAT I O N S

69. V O L D E M O R T

70. K E Y / VA L U E
S T O R E

71. store.put(key, value)

72. value = store.get(key)

73. store.delete(key)

74. B E WA R E :
I T ’ S V E RY
L I M I T E D …

75. L O W
L AT E N C Y

76. H I G H
AVA I L A B I L I T Y

77. HBase/Hadoop
D A TA B A S E # 6 :

85. B I G
D ATA
W H E N T O U S E H A D O O P …

86. – C H R I S S T U C C H I O
“Don't use Hadoop - your data isn't that big.”

88. L I N E A R
S C A L A B I L I T Y

89. A U T O M AT I C
S H A R D I N G
A N D S T R O N G
C O N S I S T E N C Y

90. B U I LT- I N
E F F I C I E N T
Q U E RY
M E T H O D S

91. Marmotta
D A TA B A S E # 7 :

92. L I N K E D
M E D I A
F R A M E W O R K

93. – L I N K E D M E D I A G U I D E L I N E S
Use URIs as names for things.
Use HTTP URIs, so that people can look up those names.

94. – L I N K E D M E D I A G U I D E L I N E S
When someone looks up a URI, provide useful
information, using the standards (RDF, SPARQL).

95. – L I N K E D M E D I A G U I D E L I N E S
Include links to other URIs, so that they can discover
more things.

97. C O O L
S K AT I N G
V I D E O

98. C O O L
S K AT I N G
V I D E O
C O O L
S K AT E R
C O O L
S K AT I N G
E V E N T

99. C O O L
S K AT I N G
V I D E O
C O O L
S K AT E R
W I N D S U R F E R
( A K A C O O L
S K AT E R ’ S
H U S B A N D )
C O O L
S K AT I N G
E V E N T
S P O N S O R O F
C O O L S K AT I N G
E V E N T

100. C O O L
S K AT I N G
V I D E O
C O O L
S K AT E R
W I N D S U R F E R
( A K A C O O L
S K AT E R ’ S
H U S B A N D )
W R I T E U P O F
W I N D S U R F I N G
E V E N T
C O O L
S K AT I N G
E V E N T
S P O N S O R O F
C O O L S K AT I N G
E V E N T
I N T E R V I E W
W I T H C E O O F
S P O N S O R

101. A PA C H E
M A R M O T TA
O U T O F T H E B O X …

102. T R I P L E
VA L U E
S T O R E

103. T R I P L E
VA L U E S T O R E
• Video A contains Alice
McSkaterton
• Alice McSkaterton is
married to Brock
Windsurferling
• Article B contains Brock
Windsurferling

104. T R I P L E
VA L U E S T O R E
• Video A contains Alice
McSkaterton
• Alice McSkaterton is
married to Brock
Windsurferling
• Article B contains Brock
Windsurferling
• ENGINE SAYS VIDEO A IS
RELATED TO ARTICLE B

105. ElasticSearch
D A TA B A S E # 8 :

106. D O C U M E N T
S T O R E

107. E V E RY R O W I S A “ T H I N G ”
N A M E = P U S S
C O O L N E S S = 0
!
N A M E = J E S S
C O O L N E S S = 0
!
N A M E = D I N A H
C O O L N E S S = 0
!
N A M E = E I N S T E I N
C O O L N E S S = 1 0
!
D O C U M E N T

108. A PA C H E
L U C E N E

109. “Apache Lucene is a high-performance, full-
featured text search engine library … It is a
technology suitable for nearly any application that
requires full-text search”

110. F O C U S E D
A R O U N D
T E X T
S E A R C H I N G
Q U E R I E S

111. {
"query": {
"match": {"hobbies": "skateboard"}
}
}

112. {
"query": {
{"fuzzy": {"hobbies": “skateboarig"}}
}
}

113. {
"query": {
{"match": {"hobbies": {"query": "writing
reddit comments", "type": "phrase"}}}
}
}

114. TempoDB
D A TA B A S E # 9 :

115. T I M E S E R I E S
D ATA B A S E

116. T I M E S TA M P /
VA L U E
PA I R S

118. Timestamp Value
2014-06-10T12:00:00+0100 17
2014-06-10T12:15:00+0100 17
2014-06-10T12:30:00+0100 20
2014-06-10T12:45:00+0100 22
2014-06-10T13:00:00+0100 24
2014-06-10T13:15:00+0100 28
2014-06-10T13:30:00+0100 32

119. T I M E S E R I E S
D ATA B A S E A S
A S E R V I C E
!
T E M P O D B

120. S P E C I A L I S E D Q U E R I E S

121. T I M E R O L L U P S

122. Timestamp Value
2014-06-10T12:00:00+0100 17
2014-06-10T12:15:00+0100 17
2014-06-10T12:30:00+0100 20
2014-06-10T12:45:00+0100 22
2014-06-10T13:00:00+0100 24
2014-06-10T13:15:00+0100 28
2014-06-10T13:45:00+0100 36
2014-06-10T12:00:00+0100 17
2014-06-10T12:15:00+0100 17
2014-06-10T12:30:00+0100 20
2014-06-10T12:45:00+0100 22
2014-06-10T13:00:00+0100 24
2014-06-10T13:15:00+0100 28
2014-06-10T13:45:00+0100 36
2014-06-10T12:00:00+0100 17
2014-06-10T12:15:00+0100 17
2014-06-10T12:30:00+0100 20
2014-06-10T12:45:00+0100 22
2014-06-10T13:00:00+0100 24
2014-06-10T13:15:00+0100 28
2014-06-10T13:45:00+0100 36

123. Timestamp Average Max Min
2014-06-10T12:00:00+0100 35 36 17
2014-06-11T12:00:00+0100 21 22 20
2014-06-12T12:30:00+0100 20.5 21 19
2014-06-13T12:45:00+0100 20 20 20
2014-06-14T13:00:00+0100 18.5 19 18

124. T E M P O R A L I N T E R P O L AT I O N

125. Timestamp Value
2014-06-10T12:00:00+0100 17
2014-06-10T12:15:00+0100 17
2014-06-10T12:30:00+0100 20
2014-06-10T12:45:00+0100 22
2014-06-10T13:00:00+0100 24
2014-06-10T13:15:00+0100 28
2014-06-10T13:45:00+0100 36

126. Timestamp Value
2014-06-10T12:00:00+0100 17
2014-06-10T12:15:00+0100 17
2014-06-10T12:30:00+0100 20
2014-06-10T12:45:00+0100 22
2014-06-10T13:00:00+0100 24
2014-06-10T13:15:00+0100 28
2014-06-10T13:30:00+0100 31.5
2014-06-10T13:45:00+0100 36

127. PostgreSQL
D A TA B A S E # 1 0 :

128. A L L T H E
G O O D S T U F F
O F S Q L

129. – P E T E R WAY N E R
“The smart NoSQL developers simply noted that
NoSQL stood for "Not Only SQL." If the masses
misinterpreted the acronym, that was their
problem.”

130. O P E N
S O U R C E A N D
M AT U R E

131. F O R E I G N
D ATA
W R A P P E R S

132. F O R E I G N D ATA W R A P P E R S
neo4j File Store
Legacy Oracle
System

133. F O R E I G N D ATA W R A P P E R S
neo4j File Store
Legacy Oracle
System

134. F O R E I G N D ATA W R A P P E R S
neo4j File Store
Legacy Oracle
System
S E L E C T S T U F F F R O M N E O 4 J
J O I N S T U F F F R O M F I L E S T O R E
J O I N S T U F F F R O M O R A C L E

135. F O R E I G N D ATA W R A P P E R S
neo4j
e.g. Patient
Data
File Store
e.g. Academic
Results
Legacy Oracle
System
e.g. Clinical Trials
S E L E C T S T U F F F R O M N E O 4 J
J O I N S T U F F F R O M F I L E S T O R E
J O I N S T U F F F R O M O R A C L E

136. F O R E I G N D ATA
W R A P P E R S
• SQL Databse Wrappers
• NoSQL Databases (Mongo,
neo4j etc.)
• Hadoop
• Files (JSON,
FixedLengthText)
• Web services
• Twitter

137. In conclusion…

138. R E A S O N S T O U S E O T H E R
D ATA B A S E S
• Geospatial indexes
• Schemaless data for query-time
efficiency
• Transparent Sharding
• Be transparent to the database
backend.
• More intuitive for the domain
• Cheap ‘joins’
• Low latency for simple data
• High availability in distributed
systems
• Dealing with very large datasets
• Meeting standards such as Linked
Media
• Support for time series databases
• Utilise pre-built text searching
functionality.
• Interface for other data sources

139. A N Y Q U E S T I O N S ?
T H A N K Y O U …