This presentation gives an introduction to how the NoSQL database movement came about. The original presentation contained some use-cases from industry where NoSQL databases are put into use. Due to lack of time, the later part was not presented and thus removed from this slide-share.

1. A sneak peek into scalable DB setups
for various applications
Dr. Parinaz Ameri

2. Agenda
● A brief history
● Basic requirements of Modern DBs
● Industrial usage of NoSQL main models
● What to expect?

3. Navigational DBs
1980
Rise of relational
DBs
1990 2000 2010 2020
3

4. General Characteristics
Relational Schema SQL Language
4

5. Orders
Customers
Order Lines
Credit Cards
5
Impedance Mismatch

6. Id Name zip_code
1 Rick 30062
2 Lisa 30074
3 Sam 30006
6
Contacts:
Normalization and Joins
Id Phone_Number
1 555-111-1234
2 555-222-1234
2 555-345-1234
3 555-333-1234
Numbers:
SELECT Name, Phone_Number FROM contacts LEFT JOIN Numbers On
Contacts.contact_id=Numbers.contact_id WHERE contacts.contact_id=3

7. 7
Computation and Joins
Source: xinuos

8. Navigational DBs
1980
Rise of relational
DBs
1990
Object-oriented
DBs
Dominance of
relationalen DBs
2000 2010 2020
Rise of
Internet
Web2.0
IoT
Social Media
8

9. 9

10. 10
BigTable
DynamoDB

11. NoSQL Term
11

12. NoSQL-Definition?
12
NoSQL refers to a series of database management concepts that process data in a
performant and reliable manner [1].

13. Common Characteristics of NoSQL DBs
13
Non-Relational
Schemaless
Cluster-Friendly
Elimination of
Join Operations
Weaker
Concurrency Model

14. Big Data: Volume
14
Scale up

15. Scale up vs. Scale out
15

16. Horizontal Scalability
16
Source: dzone

17. Linear Scalability
17
Source: DTBD

18. Shared-Nothing Architecture
18
Source: DTBD

19. Sharding
19
Source: DTBD

20. Replication
20
Source: DTBD

21. Common Characteristics of NoSQL DBs
21
Non-Relational
Schemaless
Cluster-Friendly
Elimination of
Join Operations
Weaker
Concurrency Model

22. ACID Transactions
● Atomicity: “alle or nothing”
○ No interruption during the transaction
● Consistency: “valid data”
○ Bring the DB from one consistent state
to another consistent state
● Isolation: “one after another”
○ Concurrent transactions would be serialized
● Durability: “no lost”
○ Ensures that no committed transaction
will be lost
22

23. Orders
Customers
Order Lines
Credit Cards
23

24. Browser Server
Database

26. Get Get
Post
Post

27. Get Get
Post
Post

28. Get Get
Post
Post

29. Get Get
Post
Post

30. Get Get
Post
Post
Offline Lock
V001
V001 V001
V001
V002 V001

31. 31
Consistency
Replication
Logical

32. CAP Theorem (Brewer, Lynch)
● Consistency:
○ Do all applications see the same data?
● Availability:
○ Can I interact with the system in case of a failure?
● Partitioning:
○ When two segments of the system cannot communicate with each other, could it still continue working?
■ If yes, consistency is sacrificed.
■ If no, availability is sacrificed.
32

33. CAP Theorem
33

34. 34

35. 35
Sam Kam

36. ACID vs. BASE
BASE:
● Basic Availability
● Soft-state
● Eventual Consistency
36

37. 37
Source: DTBD

38. Main Data Models

39. 39
Graph
Columnar
Document
Key-Value

40. 40
Columnar
Document
Key-Value
Graph
Aggregation-Oriented
DBs
ACID

41. Source References
[xinuos] http://osr507doc.xinuos.com/en/PERFORM/disk_IO_mech.html
[dzone] https://dzone.com/articles/vertical-scaling-and-horizontal-scaling-in-aws
[DTBD] Big Data Principles and Paradigms/Chapter 6: Database Techniques for Big Data/
Parinaz Ameri
41