Shawn Bice, VP of Non-Relational Databases at AWS, discusses a purpose-built strategy for databases, where you choose the right tool for the job. Shawn explains why your application should drive the requirements of a database, not the other way around. We introduce AWS databases that are purpose-built for your application use cases. Learn why you should select different database services to solve different aspects of an application, and watch a demonstration in which application use cases lend themselves well to specific data services. If you're a developer building modern applications that require high performance, scale, and functional databases, and you're trying to determine which relational and non-relational data services to use, this session is for you.

1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Databases on AWS: The Right Tool
for the Right Job
Shawn Bice
VP, Non relational Databases
AWS
D A T 2 0 5
Joseph Idziorek
Product Manager
AWS

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Two fundamental areas of focus
“Lift and shift” existing
apps to the cloud
Quickly build new
apps in the cloud

4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
“Lift and shift” existing apps to the cloud
“Lift and shift” existing
apps to the cloud
Quickly build new
apps in the cloud

5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Aurora
MySQL and PostgreSQL-compatible relational database built for the cloud
Performance and availability of commercial-grade databases at 1/10th the cost
Performance
and scalability
Availability
and durability
Highly secure Fully managed
5x throughput of standard
MySQL and 3x of standard
PostgreSQL; scale-out up to
15 read replicas
Fault-tolerant, self-healing
storage; six copies of data
across three Availability Zones;
continuous backup to Amazon S3
Network isolation,
encryption at
rest/transit
Managed by RDS:
No hardware provisioning,
software patching, setup,
configuration, or backups

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Relational Database Service (RDS)
Managed relational database service with a choice of six popular database engines
Easy to administer Available and durable Highly scalable Fast and secure
No need for infrastructure
provisioning, installing, and
maintaining DB software
Automatic Multi-AZ data
replication; automated backup,
snapshots, failover
Scale database compute
and storage with a few
clicks with no app
downtime
SSD storage and guaranteed
provisioned I/O; data
encryption at rest and in
transit

7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS Database Migration Service (AWS DMS)
M I G R A T I N G
D A T A B A S E S
T O A W S
Migrate between on-premises and AWS
Migrate between databases
Automated schema conversion
Data replication for
zero-downtime migration
100,000+
databases migrated

8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Quickly build new apps in the cloud
“Lift and shift” existing
apps to the cloud
Quickly build new
apps in the cloud

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Modern apps create new requirements
Users: 1 million+
Data volume: TB–PB–EB
Locality: Global
Performance: Milliseconds–microseconds
Request rate: Millions
Access: Web, mobile, IoT, devices
Scale: Up-down, Out-in
Economics: Pay for what you use
Developer access: No assembly requiredSocial mediaRide hailing Media streaming Dating

10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Common data categories and use cases
Relational
Referential
integrity, ACID
transactions,
schema-
on-write
Lift and shift, ERP,
CRM, finance
Key-value
High
throughput, low-
latency reads
and writes,
endless scale
Real-time bidding,
shopping cart,
social, product
catalog, customer
preferences
Document
Store
documents and
quickly access
querying on
any attribute
Content
management,
personalization,
mobile
In-memory
Query by key
with
microsecond
latency
Leaderboards,
real-time analytics,
caching
Graph
Quickly and
easily create
and navigate
relationships
between
data
Fraud detection,
social networking,
recommendation
engine
Time-series
Collect, store,
and process
data sequenced
by time
IoT applications,
event tracking
Ledger
Complete,
immutable, and
verifiable history
of all changes to
application data
Systems
of record, supply
chain, health care,
registrations,
financial

11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Let’s take a closer look at…
Relational Key-value Graph

12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational data
• Divide data among tables
• Highly structured
• Relationships established via
keys enforced by the system
• Data accuracy and consistency
Patient
* Patient ID
First Name
Last Name
Gender
DOB
* Doctor ID
Visit
* Visit ID
* Patient ID
* Hospital ID
Date
* Treatment ID
Medical Treatment
* Treatment ID
Procedure
How Performed
Adverse Outcome
Contraindication
Doctor
* Doctor ID
First Name
Last Name
Medical Specialty
* Hospital Affiliation
Hospital
* Hospital ID
Name
Address
Rating

13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Relational use case
// Doctors affiliated with Mercy
hospital
Patient
* Patient ID
First Name
Last Name
Gender
DOB
* Doctor ID
Visit
* Visit ID
* Patient ID
* Hospital ID
Date
* Treatment ID
Medical Treatment
* Treatment ID
Procedure
How Performed
Adverse Outcome
Contraindication
Doctor
* Doctor ID
First Name
Last Name
Medical Specialty
* Hospital Affiliation
Hospital
* Hospital ID
Name
Address
Rating
SELECT
d.first_name, d.last_name
FROM
doctor as d,
hospital as h
WHERE
d.hospital = h.hospital_id
AND h.name = ‘Mercy';
// Number of patient visits each doctor
completed last week
SELECT
d.first_name, d.last_name, count(*)
FROM
visit as v,
hospital as h,
doctor as d
WHERE
v.hospital_id = h.hospital_id
AND h.hospital_id = d.hospital
AND v.t_date > date_trunc('week’,
CURRENT_TIMESTAMP - interval '1 week')
GROUP BY
d.first_name, d.last_name;

14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Key-value data
• Simple key value
pairs
• Partitioned by keys
• Resilient to failure
• High throughput,
low-latency reads
and writes
• Consistent
performance at
scale
Gamers
Primary Key Attributes
GamerTag Level Points High Score Plays
Hammer57 21 4050 483610 1722
FluffyDuffy 5 1123 10863 43
Lol777313 14 3075 380500 1307
Jam22Jam 20 3986 478658 1694
ButterZZ_55 7 1530 12547 66
… … … … …
Gamers
Hammer57
21
4050,
483610,
1722
GET {
TableName:"Gamers",
Key: {
"GamerTag":"Hammer57“,
“ProjectionExpression“:”Points”
} }

15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Gamers
Primary Key
Attributes
Gamer Tag Type
Hammer57
Rank
Level Points Tier
87 4050 Elite
Status
Health Progress
90 30
Weapon
Class Damage Range
Taser 87% 50
FluffyDuffy
Rank
Level Points Tier
5 1072 Trainee
Status
Health Progress
37 8
Key-value use case
// Status of Hammer57
GET {
TableName:"Gamers",
Key: {
"GamerTag":"Hammer57",
"Type":"Status” } }
// Return all Hammer57
Gamers
GamerTag = :a
:a Hammer57

16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Graph data
• Relationships are first-class
objects
• Vertices connected by Edges
PURCHASED PURCHASED
FOLLOWS
PURCHASED
KNOWS
PRODUCT
SPORT
FOLLOWS

17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Graph use case
// Product recommendation to a user
gremlin> V().has(‘name’,’sara’).as(‘customer’).out(‘follows’).in(‘follows’).out(‘purchased’)
( (‘customer’)).dedup() (‘name’) ('name')
PURCHASED PURCHASED
FOLLOWS
PURCHASED
KNOWS
PRODUCT
SPORT
FOLLOWS
FOLLOWS
// Identify a friend in common and
make a recommendation
gremlin> g.V().has('name','mary').as(‘start’).
both('knows').both('knows’).
where(neq(‘start’)).
dedup().by('name').properties('name')

18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Airbnb uses different databases based
on the purpose
User search history: Amazon DynamoDB
• Massive data volume
• Need quick lookups for personalized search
Session state: Amazon ElastiCache
• In-memory store for submillisecond site rendering
Relational data: Amazon RDS
• Referential integrity
• Primary transactional database

19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
CHALLENGE
Wanted to enable anyone to learn a
language for free.
SOLUTION
Purpose-built databases from AWS:
• DynamoDB: 31B items tracking
which language exercises completed
• Aurora: primary transactional
database for user data
• ElastiCache: instant access to
common words and phrases
Result:
More people learning a language on
Duolingo than entire US school system
300M total users
7B exercises per month

20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Retail demo application
Demo application:
1. Available today
2. On GitHub:
/aws-samples/aws-
bookstore-demo-app
3. One click
CloudFormation
deployment
Search
Indexing and
searching
semistructured
logs and data
Product
search
Amazon
Neptune
Amazon Elasticsearch
Service
Key-value
High
throughput,
Low-latency
reads
and writes,
endless scale
Shopping cart, user
profile
Graph
Quickly and
easily create
and navigate
relationships
between
data
Product
recommendation
In-memory
Query by key
with
microsecond
latency
Product
leaderboard
DynamoDB ElastiCache

22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Common customer use cases
Ledgers with centralized control
Healthcare
Verify and track hospital
equipment inventory
Manufacturers
Track distribution of a
recalled product
HR & Payroll
Track changes to an
individual’s profile
Government
Track vehicle title
history

24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Challenges with building ledgers
Adds unnecessary
complexity
BlockchainRDBMS - audit tables
Difficult to
maintain
Hard to use
and slow
Hard to build
Custom audit functionality using
triggers or stored procedures
Impossible to verify
No way to verify changes made
to data by sys admins

25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Ledger database concepts
C | H
J Journal
C | H Current | History
Current | History
Journal
Ledger comprises
J
L
Ledger databaseL
Journal determines Current | History

26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works

27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
ID Manufacturer Model Year VIN Owner
ID Version Start Manufacturer Model Year VIN Owner
J
history.cars
H
current.cars
C

28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
ID Manufacturer Model Year VIN Owner
ID Version Start Manufacturer Model Year VIN Owner
J
history.cars
H
INSERT INTO cars <<
{ 'Manufacturer':'Tesla',
'Model':'Model S',
'Year':'2012',
'VIN':'123456789',
'Owner':'Traci Russell' }
>>current.cars
C

29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
ID Manufacturer Model Year VIN Owner
ID Version Start Manufacturer Model Year VIN Owner
J
history.cars
H
INSERT INTO cars <<
{ 'Manufacturer':'Tesla',
'Model':'Model S',
'Year':'2012',
'VIN':'123456789',
'Owner':'Traci Russell' }
>>
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C

30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
ID Manufacturer Model Year VIN Owner
ID Version Start Manufacturer Model Year VIN Owner
J
history.cars
H
INSERT INTO cars <<
{ 'Manufacturer':'Tesla',
'Model':'Model S',
'Year':'2012',
'VIN':'123456789',
'Owner':'Traci Russell' }
>>
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1)

31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
ID Version Start Manufacturer Model Year VIN Owner
J
history.cars
H
INSERT INTO cars <<
{ 'Manufacturer':'Tesla',
'Model':'Model S',
'Year':'2012',
'VIN':'123456789',
'Owner':'Traci Russell' }
>>
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Traci Russell

32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT INTO cars <<
{ 'Manufacturer':'Tesla',
'Model':'Model S',
'Year':'2012',
'VIN':'123456789',
'Owner':'Traci Russell' }
>>
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Traci Russell
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell

33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Traci Russell
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell

34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Traci Russell
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Ronnie Nash'

35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Traci Russell
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (T2)
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Ronnie Nash'

36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Traci Russell
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (T2)
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Ronnie Nash'

37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1)
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (T2)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Ronnie Nash
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Ronnie Nash'

38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1) UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (T2)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Ronnie Nash
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
1 2 08/03/2013 Tesla Model S 2012 123456789 Ronnie Nash
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Ronnie Nash'

39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1) UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (T2)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Ronnie Nash
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
1 2 08/03/2013 Tesla Model S 2012 123456789 Ronnie Nash

40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1) UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (T2)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Ronnie Nash
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
1 2 08/03/2013 Tesla Model S 2012 123456789 Ronnie Nash
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Elmer Hubbard'

41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1) UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (T2)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Ronnie Nash
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
1 2 08/03/2013 Tesla Model S 2012 123456789 Ronnie Nash
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Elmer Hubbard'
UPDATE cars
ID:1
Owner: Elmer Hubbard
Metadata: {
Date: 09/02/2016
}
H (T3)

42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How it works
J
history.cars
H
INSERT cars
ID:1
Manufacturer: Tesla
Model: Model S
Year: 2012
VIN: 123456789
Owner: Traci Russell
Metadata: {
Date:07/16/2012
}
current.cars
C
H (T1) UPDATE cars
ID:1
Owner: Ronnie Nash
Metadata: {
Date:08/03/2013
}
H (T2)
ID Manufacturer Model Year VIN Owner
1 Tesla Model S 2012 123456789 Elmer Hubbard
FROM cars WHERE VIN = '123456789' UPDATE owner = 'Elmer Hubbard'
UPDATE cars
ID:1
Owner: Elmer Hubbard
Metadata: {
Date: 09/02/2016
}
H (T3)
ID Version Start Manufacturer Model Year VIN Owner
1 1 07/16/2012 Tesla Model S 2012 123456789 Traci Russell
1 2 08/03/2013 Tesla Model S 2012 123456789 Ronnie Nash
1 3 09/02/2016 Tesla Model S 2012 123456789 Elmer Hubbard

43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Quantum Ledger Database (QLDB) (Preview)
Fully managed ledger database
Track and verify history of all changes made to your application’s data
Immutable
Maintains a sequenced record of
all changes to your data, which
cannot be deleted or modified;
you have the ability to query and
analyze the full history
Cryptographically
verifiable
Uses cryptography to
generate a secure output
file of your data’s history
Easy to use
Easy to use, letting you
use familiar database
capabilities like SQL APIs
for querying the data
Highly scalable
Executes 2–3X as many
transactions than ledgers
in common blockchain
frameworks

44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Time-series data
What is time-series
data?
What is special about a
time-series database? A sequence of data points
recorded over a time interval
Time is the
single primary axis
of the data model
t

46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Time-series use case
Application events
IoT Sensor
Readings
DevOps data
Humidity
% WATER VAPOR
91.094.086.093.0

47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Existing time-series databasesRelational databases
Difficult to
maintain high
availability
Difficult to scale Limited data
lifecycle
management
Inefficient
time-series data
processing
Unnatural for
time-series
data
Rigid schema
inflexible for fast
moving time-series
data
Building with time-series data is challenging

48. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Timestream (sign up for the preview)
Fast, scalable, fully managed time-series database
1,000x faster and 1/10th the
cost of relational databases
Collect data at the rate of
millions of inserts per
second (10M/second)
Trillions of
daily events
Adaptive query processing
engine maintains steady,
predictable performance
Time-series analytics
Built-in functions for
interpolation, smoothing,
and approximation
Serverless
Automated setup,
configuration, server
provisioning, software patching

49. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

50. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Purpose-built databases
Relational Key-value Document In-memory Graph Time-series Ledger
DynamoDB NeptuneAmazon RDS
Aurora CommercialCommunity
Timestream QLDBElastiCache

51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Breakout repeats
Thursday, Nov 29
DAT205 - Databases on AWS: The Right Tool for the Right Job
11:30 AM - 12:30 PM | Aria West, Level 3, Ironwood 5

52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Related breakouts
Tuesday, Nov 27
DAT202 - Reliable & Scalable Redis in the Cloud with Amazon ElastiCache
1:00 PM - 2:00 PM | Aria East, Level 2, Mariposa 5
Tuesday, Nov 27
ANT334-R - [REPEAT] Get the Most out of Your Amazon Elasticsearch Service Domain
11:30 AM - 12:30 PM | Venetian, Level 2, Venetian E
Wednesday, Nov 28
DAT403 - Deep Dive on Amazon Neptune
11:30 AM - 12:30 PM | Aria East, Level 1, Joshua 6

53. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Related breakouts
Wednesday, Nov 28
DAT301 - Building with AWS Databases: Match Your Workload to the Right Database
1:00 PM - 2:00 PM | Aria West, Level 3, Ironwood 5
Wednesday, Nov 28
DAT201 - A Deep Dive into What's New for Amazon DynamoDB
2:30 PM - 3:30 PM | Bellagio, Level 1, Grand Ballroom 2
Friday, Nov 30
An Introduction to Amazon Quantum Ledger Database (QLDB)
10:00AM – 11:00AM | Mirage, Montego D

54. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Purpose-built
databases
The right tool for the right job

55. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Shawn Bice
@shawnbice
Joseph Idziorek
@josephidziorek

56. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.