In this session, Shawn Bice, VP of NoSQL and QuickSight, will cover what's new in AWS non-relational data services, such as Amazon DynamoDB, Amazon ElastiCache, and Amazon Elastisearch. We will discuss how developers might select different data services to solve different aspects of an application and demo scenarios on which application use cases lend themselves well to which data services. If you’re a developer building massively scaled applications, requiring flexibility, consistent millisecond performance, and trying to understand what non-relational data service you might use, this is a great introductory session.

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AWS re:INVENT
What's New for AWS Purpose Built,
Non-Relational Databases
S h a w n B i c e , V P , A W S
@ s h a w n b i c e
D A T 2 0 4
November 29, 2017

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

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DW | Big Data Processing | Interactive
AWS databases and analytics
B r o a d a n d d e e p p o r t f o l i o , p u r p o s e - b u i l t f o r b u i l d e r s
Business Intelligence & Machine Learning
Data Movement
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Amazon QuickSight
Relational Databases
Amaozn RDS
Amazon Aurora
Data Lake
Amazon S3/Amazon Glacier AWS Glue
(ETL & Data Catalog)
SageMaker
Non-Relational Databases Analytics
Amazon DynamoDB
Amazon ElastiCache
(Redis, Memcached)
Neptune
(Graph)
Redshift EMR Athena
Kinesis
Analytics
Elasticsearch
Service
Real-time
Amazon Clair

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Demo architecture
Amazon
DynamoDB
Amazon ES Amazon Neptune
Lambda
function
Amazon
DynamoDB
us-west-2 eu-west-1
web app

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Demo re:cap
✓ Purpose-built data stores
✓ Architected for a global customer base
✓ Able to scale to >1M users
✓ Great search experience
✓ Visualize log analytics
✓ Powerful recommendation engine

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What's new for AWS purpose built,
Non-relational databases

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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
De live ring on cu stome r ne e ds
Encryption at rest
(Coming Soon)
N E W !

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DynamoDB Global Tables ( G A )
Fi 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 DynamoDB – Backup and Restore
Fi r s t N o S Q L d a t a b a s e t o a u t o m a t e o n - d e m a n d a n d c o n t i n u o u s b a c k u p s
Point in time restore
for short-term
retention and data
corruption protection
(coming soon)
Back up hundreds of
TB instantaneously
with NO performance
impact
On-demand
backups for long-
term data archival
and compliance
(GA)

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Amazon DynamoDB
E n c r y p t i o n @ r e s t ( c o m i n g s o o n )
Server-side
encryption
Supports
compliance
certifications
No application
code rewrites

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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 Elasticsearch Service: What’s new
100 nodes
150 TB
February 2017
VPC
Slow Logs
October 2017April 2017
Elasticsearch 5
January 2017
1 Petabyte
I3 Instances
(coming soon)
N E W !
Encryption at rest
(coming soon)
N E W !
Elasticsearch 6
(coming soon)
N E W !
C4, M4, R4
1.5 TB Storage

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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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Memcached
1.4.34
April 2017
Online cluster
resizing
Redis Cluster aware
backup/restore
March 2017
HIPAA
Eligibility
N E W !
Amazon ElastiCache
De live ring on cu stome r ne e ds
Encryption In
Transit, At Rest,
and Redis AUTH
N E W ! N E W !
Redis test
failover
April 2017

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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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Select NoSQL database talks
Amazon DynamoDB:
• DAT304: DynamoDB - What's New
• DAT405: Workshop on Advanced Design Patterns for Amazon DynamoDB
• DAT320: Moving a Galaxy into the Cloud: Best Practices from Samsung on Migrating to
Amazon DynamoDB
• DAT326: How DynamoDB Powered Amazon's Prime Day 2017
Amazon ElastiCache:
• DAT305: ElastiCache Deep Dive: Best Practices and Usage Patterns
• DAT321: From Minutes to Milliseconds: How Careem Used Amazon ElastiCache for
Redis to Accelerate Their Ride Share Application
• DAT341: Working with Amazon ElastiCache for Redis

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Select NoSQL database talks
General:
• DAT310: Which Database to Use When?
Amazon Elasticsearch Service:
• ABD326: Easy and Scalable Log Analytics with Amazon Elasticsearch Service
• ABD331: Log Analytics at Expedia Using Amazon Elasticsearch Service
• ABD333: Scaling Amazon Elasticsearch Service
Amazon Neptune
• DAT342: How to Build Graph Applications with SPARQL and Gremlin Using Neptune
• DAT318: Amazon Neptune Deep Dive and Customer Use Case (with Siemens)
• MCL342: Graph-Based Approaches for Cyber Investigative Analytics Using GPU
• DAT319: Amazon Neptune Overview and Customer Use Case

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Thank you!