Speaker: Joseph Idziorek - Sr. Product Manager

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The Non-Relational Revolution
Joseph Idziorek
idziorek@amazon.com
Sr Product Manager

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purpose
noun | pur·pose | ˈpər-pəs
The reason for which something is done or created
or for which something exists

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Things with
purpose

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Non-Relational Powers New Architectures

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Traditional application characteristics
HR Payroll
CRM ERP
…
Users 100s-1000s
Data volume GB-TB
Locality HQ
Performance Seconds
Request Rate Tens of thousands
Access Internal servers, PCs
Scale Up
Economics Pay up front
Developer Access days/weeks/months

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Cloud application characteristics
Key-valueRelational Document Graph
;
Users 1M+
Data volume TB-PB-EB
Locality Global
Performance Milliseconds-Microseconds
Request Rate Millions
Access Mobile, IoT, Devices
Scale Up-Out-In
Economics Pay as you go
Developer access Instant API access

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Database characteristics
• Referential integrity
with strong
consistency,
transactions, and
hardened scale
GraphKey-value Document
;
Relational
Low-latency, key-based
queries with high
throughput and fast
ingestion of data
Indexing and storing
documents with support
for query on any
property
Creating and navigating
relations between data
easily and quickly
Complex query support
via SQL
Simple query methods
with filters
Simple query with filters,
projections, and
aggregates
Easily express queries in
terms of relations

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Polyglot persistence
• Different data technologies for different data needs
– Within an application
– Across an organization
– Spanning multiple organizations
• Loose coupling sets you free
– Services communicate through queues
– Non-relational databases make excellent queues

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The New De-Normal

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Different approaches for highly connected data
Purpose-built for a business process
Purpose-built to answer questions about
relationships

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Amazon DynamoDB
F a s t a n d f l e x i b l e N o S Q L d a t a b a s e s e r v i c e f o r a n y s c a l e
Fast, consistent
performance
Highly scalable Fully managed Business critical
reliability
Consistent single-digit millisecond
latency; DAX in-memory
performance reduces response
times to microseconds
Auto-scaling to hundreds
of terabytes of data that
serve millions of requests
per second
Automatic provisioning,
infrastructure
management, scaling,
and configuration with
zero downtime
Data is replicated across
fault tolerant availability
zones, with fine-grained
access control

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VPC
Endpoints
April 2017
Auto
Scaling
June 2017
DynamoDB
Accelerator (DAX)
April 2017
Time to
Live (TTL)
February 2017
Global Tables
(GA)
N E W !
On-demand
Backup (GA)
N E W !
Amazon DynamoDB
D e l i v e r i n g o n c u s t o m e r n e e d s
Encryption at rest
(Coming Soon)
N E W !

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DynamoDB Global Tables
F i r s t f u l l y m a n a g e d , m u l t i - m a s t e r , m u l t i - r e g i o n d a t a b a s e
Build high performance, globally distributed applications
Low latency reads and writes to locally available tables
Disaster proof with multi-region redundancy
Easy to set up and no application re-writes required
Globally dispersed users
Replica (N. America)
Replica (Europe)
Replica (Asia)
Global App
Global Table

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Amazon Elasticsearch Service
L o g a n a l y t i c s , c l i c k s t r e a m a n a l y t i c s , f u l l - t e x t s e a r c h
Easy to Use
Fully managed.
Single click topologies.
Open & Integrated
Elasticsearch APIs &
Kibana. AWS Integrated.
Secure
VPC. IAM.
Scalable & Available
100 Nodes. 150 TB.
Multi-AZ.

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Amazon ElastiCache
Extreme
Performance
Secure & hardened Easily scalable
Highly available &
reliable
In-memory data store and
cache using optimized stack
to deliver sub-millisecond
response times
VPC for cluster isolation,
encryption at rest/transit,
and HIPAA compliance
Read scaling with replicas.
Write and memory scaling
with sharding. Non
disruptive scaling
Multi-AZ with automatic
failover
Managed, in-memory data store service
Redis or Memcached to power real-time apps with sub-millisecond latency

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Highly connected data
Social News Feed Restaurant Recommendations Retail Fraud Detection

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Too expensive
Difficult to maintain
high availability
Difficult to scale
Limited support for
open standards
$
Existing Graph DatabasesRelational Databases
Inefficient graph
processing
Unnatural for
querying graph
Rigid schema inflexible
for changing graphs
Challenges building apps with highly connected data

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Amazon Neptune
F u l l y m a n a g e d g r a p h d a t a b a s e
(public preview today)
Fast & Scalable Reliable Open
Store billions of
relationships and
query with
milliseconds latency
Six replicas of your
data across three AZs
with full backup and
restore
Build powerful
queries easily with
Gremlin and SPARQL
Supports Apache
TinkerPop & W3C
RDF graph models
Gremlin
SPARQL
Easy

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