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A D D I N G N O S Q L T O
Y O U R A R S E N A L
A D D I N G N O S Q L T O
Y O U R A R S E N A L
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
SQL
D A TA B A S E # 1 :
T H E
I N D U S T RY
S TA N D A R D
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 )
R D B M S
• Schema-driven
• Set-based operations
• ACID transactionality
S C H E M A
D R I V E N
Name Species
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
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
“ 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
U N I O N S
Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess
5 Nemo
6 Moby Dick
7 Wanda
J O I N S
Name Species
Species Coolness
Rating
1 Puss 0
2 Dinah 0
3 Einstein 10
4 Jess 0
C A R T E S I A N P R O D U C T S
0 10
0 10
0 10
C A R T E S I A N P R O D U C T S
0 10
0 10
0 10
– 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.”
A C I D
W R I T E D A TA I N W I T H
Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess
DonaldPlutoMickey
{ }
Ducks aren’t mammals
Name Species
1 Puss
2 Dinah
3 Einstein
4 Jess
The database is always in a valid state, as defined
by a whole number of queries
regardless of:
(1) invalid data;
(2) conc...
The database is always in a valid state, as defined
by a whole number of queries
regardless of:
(1) invalid data;
(2) conc...
The database is always in a valid state, as defined
by a whole number of queries
regardless of:
(1) invalid data;
(2) conc...
The database is always in a valid state, as defined
by a whole number of queries
regardless of:
(1) invalid data;
(2) conc...
A C I D
• Atomicity
• Consistency
• Isolation
• Durability
W H AT I S W R O N G
W I T H S Q L ?
N O T H I N G
N O T H I N G *
* As long as you use it for the right job
– 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.”
T O C O M E
• 10 different ‘flavours’ of
NoSQL Databases
• Just enough to whet the
appetite!
MongoDB
D A TA B A S E # 2 :
D O C U M E N T
S T O R E
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
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...
B E WA R E !
T H AT ’ S T H E P O I N T
D E N O R M A L I S E D D ATA
F O R E X A M P L E
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...
E A S Y
S H A R D I N G
G E O S PAT I A L
I N D E X E S
S C H E M A L E S S
Eloquera
D A TA B A S E # 3 :
O B J E C T
D ATA B A S E
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
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...
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 {...
T R A N S PA R E N C Y T O T H E D B
neo4j
D A TA B A S E # 4 :
G R A P H D ATA B A S E
N E O 4 J
I M P L E M E N T E D B Y …
T H E D ATA
I S T H E
R E L AT I O N S
Voldemort
D A TA B A S E # 5 :
– 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 ...
– 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 ...
– 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 ope...
C O N S I S T E N C Y
A
B C
C O N S I S T E N C Y
A
B C
D Y N A M O I M P L E M E N TAT I O N S
V O L D E M O R T
K E Y / VA L U E
S T O R E
store.put(key, value)
value = store.get(key)
store.delete(key)
B E WA R E :
I T ’ S V E RY
L I M I T E D …
L O W
L AT E N C Y
H I G H
AVA I L A B I L I T Y
HBase/Hadoop
D A TA B A S E # 6 :
B I G
D ATA
W H E N T O U S E H A D O O P …
– C H R I S S T U C C H I O
“Don't use Hadoop - your data isn't that big.”
L I N E A R
S C A L A B I L I T Y
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
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
Marmotta
D A TA B A S E # 7 :
L I N K E D
M E D I A
F R A M E W O R K
– 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 ...
– 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 (...
– 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.
C O O L
S K AT I N G
V I D E O
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
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...
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...
A PA C H E
M A R M O T TA
O U T O F T H E B O X …
T R I P L E
VA L U E
S T O R E
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...
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...
ElasticSearch
D A TA B A S E # 8 :
D O C U M E N T
S T O R E
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...
A PA C H E
L U C E N E
“Apache Lucene is a high-performance, full-
featured text search engine library … It is a
technology suitable for nearly a...
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
{
"query": {
"match": {"hobbies": "skateboard"}
}
}
{
"query": {
{"fuzzy": {"hobbies": “skateboarig"}}
}
}
{
"query": {
{"match": {"hobbies": {"query": "writing
reddit comments", "type": "phrase"}}}
}
}
TempoDB
D A TA B A S E # 9 :
T I M E S E R I E S
D ATA B A S E
T I M E S TA M P /
VA L U E
PA I R S
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+01...
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
S P E C I A L I S E D Q U E R I E S
T I M E R O L L U P S
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+01...
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....
T E M P O R A L I N T E R P O L AT I O N
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+01...
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+01...
PostgreSQL
D A TA B A S E # 1 0 :
A L L T H E
G O O D S T U F F
O F S Q L
– 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
misinter...
O P E N
S O U R C E A N D
M AT U R E
F O R E I G N
D ATA
W R A P P E R S
F O R E I G N D ATA W R A P P E R S
neo4j File Store
Legacy Oracle
System
F O R E I G N D ATA W R A P P E R S
neo4j File Store
Legacy Oracle
System
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 ...
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. Cli...
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,
F...
In conclusion…
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
• Trans...
A N Y Q U E S T I O N S ?
T H A N K Y O U …
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
10 d bs in 30 minutes
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10 d bs in 30 minutes

  1. 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. 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. 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. 4. SQL D A TA B A S E # 1 :
  5. 5. T H E I N D U S T RY S TA N D A R D
  6. 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. 7. R D B M S • Schema-driven • Set-based operations • ACID transactionality
  8. 8. S C H E M A D R I V E N
  9. 9. Name Species
  10. 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. 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. 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. 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. 14. J O I N S Name Species Species Coolness Rating 1 Puss 0 2 Dinah 0 3 Einstein 10 4 Jess 0
  15. 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. 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. 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. 18. A C I D W R I T E D A TA I N W I T H
  19. 19. Name Species 1 Puss 2 Dinah 3 Einstein 4 Jess
  20. 20. DonaldPlutoMickey { }
  21. 21. Ducks aren’t mammals
  22. 22. Name Species 1 Puss 2 Dinah 3 Einstein 4 Jess
  23. 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. 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. 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. 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. 27. A C I D • Atomicity • Consistency • Isolation • Durability
  28. 28. W H AT I S W R O N G W I T H S Q L ?
  29. 29. N O T H I N G
  30. 30. N O T H I N G * * As long as you use it for the right job
  31. 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. 32. T O C O M E • 10 different ‘flavours’ of NoSQL Databases • Just enough to whet the appetite!
  33. 33. MongoDB D A TA B A S E # 2 :
  34. 34. D O C U M E N T S T O R E
  35. 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. 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. 37. B E WA R E !
  38. 38. T H AT ’ S T H E P O I N T
  39. 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. 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
  41. 41. E A S Y S H A R D I N G
  42. 42. G E O S PAT I A L I N D E X E S
  43. 43. S C H E M A L E S S
  44. 44. Eloquera D A TA B A S E # 3 :
  45. 45. O B J E C T D ATA B A S E
  46. 46. 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
  47. 47. 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
  48. 48. 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;} }
  49. 49. T R A N S PA R E N C Y T O T H E D B
  50. 50. neo4j D A TA B A S E # 4 :
  51. 51. G R A P H D ATA B A S E
  52. 52. N E O 4 J I M P L E M E N T E D B Y …
  53. 53. T H E D ATA I S T H E R E L AT I O N S
  54. 54. Voldemort D A TA B A S E # 5 :
  55. 55. – 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.”
  56. 56. – 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”
  57. 57. – 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.”
  58. 58. C O N S I S T E N C Y A B C
  59. 59. C O N S I S T E N C Y A B C
  60. 60. D Y N A M O I M P L E M E N TAT I O N S
  61. 61. V O L D E M O R T
  62. 62. K E Y / VA L U E S T O R E
  63. 63. store.put(key, value)
  64. 64. value = store.get(key)
  65. 65. store.delete(key)
  66. 66. B E WA R E : I T ’ S V E RY L I M I T E D …
  67. 67. L O W L AT E N C Y
  68. 68. H I G H AVA I L A B I L I T Y
  69. 69. HBase/Hadoop D A TA B A S E # 6 :
  70. 70. B I G D ATA W H E N T O U S E H A D O O P …
  71. 71. – C H R I S S T U C C H I O “Don't use Hadoop - your data isn't that big.”
  72. 72. L I N E A R S C A L A B I L I T Y
  73. 73. 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
  74. 74. 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
  75. 75. Marmotta D A TA B A S E # 7 :
  76. 76. L I N K E D M E D I A F R A M E W O R K
  77. 77. – 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.
  78. 78. – 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).
  79. 79. – 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.
  80. 80. C O O L S K AT I N G V I D E O
  81. 81. 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
  82. 82. 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
  83. 83. 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
  84. 84. A PA C H E M A R M O T TA O U T O F T H E B O X …
  85. 85. T R I P L E VA L U E S T O R E
  86. 86. 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
  87. 87. 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
  88. 88. ElasticSearch D A TA B A S E # 8 :
  89. 89. D O C U M E N T S T O R E
  90. 90. 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
  91. 91. A PA C H E L U C E N E
  92. 92. “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”
  93. 93. 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
  94. 94. { "query": { "match": {"hobbies": "skateboard"} } }
  95. 95. { "query": { {"fuzzy": {"hobbies": “skateboarig"}} } }
  96. 96. { "query": { {"match": {"hobbies": {"query": "writing reddit comments", "type": "phrase"}}} } }
  97. 97. TempoDB D A TA B A S E # 9 :
  98. 98. T I M E S E R I E S D ATA B A S E
  99. 99. T I M E S TA M P / VA L U E PA I R S
  100. 100. 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
  101. 101. 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
  102. 102. S P E C I A L I S E D Q U E R I E S
  103. 103. T I M E R O L L U P S
  104. 104. 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
  105. 105. 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
  106. 106. T E M P O R A L I N T E R P O L AT I O N
  107. 107. 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
  108. 108. 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
  109. 109. PostgreSQL D A TA B A S E # 1 0 :
  110. 110. A L L T H E G O O D S T U F F O F S Q L
  111. 111. – 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.”
  112. 112. O P E N S O U R C E A N D M AT U R E
  113. 113. F O R E I G N D ATA W R A P P E R S
  114. 114. F O R E I G N D ATA W R A P P E R S neo4j File Store Legacy Oracle System
  115. 115. F O R E I G N D ATA W R A P P E R S neo4j File Store Legacy Oracle System
  116. 116. 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
  117. 117. 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
  118. 118. 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
  119. 119. In conclusion…
  120. 120. 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
  121. 121. A N Y Q U E S T I O N S ? T H A N K Y O U …

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