As you look to modernizing your applications, you will need to consider your database options to meet the new application requirements. AWS offers a series of purpose-built databases that include relational, key value, document, graph and cache use cases to help you deliver new and enhanced functionalities. In this webinar session, we share the different modern application architectures, and how to combine different database services to meet your requirements. Understand how to modernize your relational databases through easy upgrades with Amazon Relational Database Service and learn how to migrate from one database to another with AWS Database Migration Service and AWS Schema Conversion Tool. Speaker: Blair Layton, Business Development Manager, Amazon Web Services

1. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Using AWS Purpose-Built Databases
to Modernize your Applications
Blair Layton
Business Development - Database Services

2. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Agenda
1. The Database Market is Changing
2. Application Architecture is Changing
3. AWS Database Services Overview
4. Modernization Paths
5. Relational Database Modernization
6. RDS Database Upgrade Demo!
7. Migrating Databases to AWS (DMS and SCT)
8. What Data Store to use for Refactoring/Rearchitecting
9. Modernizing an Application Data Tier – Step by Step
10. Summary

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The Database Market is Changing

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Traditional Database Architecture
App/Web Tier
Client Tier
RDBMS
one database
for all
workloads

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Traditional Database Architecture
key-value access
complex queries
transactions
analytics
App/Web Tier
Client Tier
RDBMS

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Data Tier
Cache
Data
Warehouse
Blob Store
RDBMSNoSQL Search
Cloud Data Tier Architecture
App/Web Tier
Client Tier
best database
for each
workload

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Data Tier
Cache
Data
Warehouse
Blob Store
RDBMSNoSQL Search
Workload Driven Data Store Selection
logging
rich search
key/value
simple query
hot reads
analytics
complex queries
& transactions

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Database Characteristics
Referential
integrity with
strong
consistency,
transactions, and
hardened scale
GraphKey-value DocumentRelational
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
{;}
In-memory Search
Microsecond
latency, key-
based queries,
specialized data
structures
Indexing and
searching
semistructured
logs and data
Amazon Aurora,
Amazon RDS
Amazon
DynamoDB
Amazon
DynamoDB
Amazon
Neptune
Amazon
ElastiCache for
Redis &
Memcached
Amazon
Elasticsearch
Service

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Application Architecture is
Changing

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Microservices at Amazon
Service-Oriented Architecture (SOA)
Single-purpose
Connect only through APIs
Connect over HTTPS
“Microservices”

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Monolithic vs. SOA vs. Microservices
SOA
Coarse-grained
Microservices
Fine-grained
Monolithic
Single Unit

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Microservices…
Eliminates any long-term commitment to a technology stack
Polyglot ecosystem
Polyglot persistence
• Decompose Databases
• Database per microservice pattern
Allows easy use of Canary and Blue-Green deployments

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AWS Database Services

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DW | Big Data Processing | Ad hoc
AWS Databases and Analytics
Broadest and deepest portfolio purpose-built for builders
Business Intelligence & Machine Learning
Data Movement
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Amazon QuickSight
Relational Databases
RDS
Aurora
Data lake (Batch/ETL)
S3/Glacier
(Storage)
Glue
(ETL & Data Catalog)
Machine Learning
Macie
(Data Protection)
Non-Relational Databases
Analytics (OLAP/DSS)
DynamoDB
(Wide Column/Document)
ElastiCache
(Indexed Key Value)
Amazon
Redshift
EMR Athena
Kinesis Data
Analytics
Elasticsearch
Service
Real-time
Operational (OLTP)
Neptune
(Graph)

15. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
DW | Big Data Processing | Ad hoc
AWS Databases and Analytics
Broadest and deepest portfolio purpose-built for builders
Business Intelligence & Machine Learning
Data Movement
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Amazon QuickSight
Relational Databases
RDS
Aurora
Data lake (Batch/ETL)
S3/Glacier
(Storage)
Glue
(ETL & Data Catalog)
Machine Learning
Macie
(Data Protection)
Non-Relational Databases
Analytics (OLAP/DSS)
DynamoDB
(Wide Column/Document)
ElastiCache
(Indexed Key Value)
Amazon
Redshift
EMR Athena
Kinesis Data
Analytics
Elasticsearch
Service
Real-time
Operational (OLTP)
Neptune
(Graph)

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Amazon RDS
Managed relational database service with a choice of six popular database engines
Easy to administer Highly flexible Available &
durable
Fast
No need for infrastructure
provisioning, installing and
maintaining database
software.
Scale database compute
and storage with a few
mouse clicks and zero
downtime.
Multi-AZ:
Automatically
replicates data.
Automated backup,
snapshots, failover.
Choose between dual SSD-
backed storage for high-
performance OLTP.

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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 &
scalability
5x throughput of standard
MySQL and 3x of standard
PostgreSQL; scale-out up to 15
read replicas
Highly secure
Network isolation,
encryption at rest and in transit
Availability & durability
Fault-tolerant, self-healing
storage. Six copies of data across
three AZs. Continuous backup to
Amazon S3
Fully managed
Managed by Amazon RDS: no
hardware provisioning,
software patching, setup,
configuration, or backups

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Amazon DynamoDB
Fast and flexible NoSQL database service for any scale
Key-value NoSQL database that supports both document and wide column structures
Fast, consistent
performance
Highly scalable Fully managed
Business-critical
reliability
Consistent single-digit
millisecond latencies at any
scale. DAX speeds up times
to microseconds.
Auto-scaling tables serving
millions of requests per
second, storing hundreds of
terabytes of data.
Automatic
provisioning and
infrastructure
management.
Data replicated across
multiple AZs and
accessed with regionally
available APIs.

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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
N E W !
Backup and
Restore
N E W !
Amazon DynamoDB
Delivering on customer needs
Encryption at rest
N E W !
February 2018November
2017
November
2017

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Amazon Neptune
Fully managed graph database
Fast Reliable Open
Query billions of
relationships with
millisecond 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
Easy

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Highly Connected Data Best Represented in a Graph
Social news feed Restaurant recommendations Retail fraud detection

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Amazon ElastiCache
Extreme performance Secure & hardened Easily scalable Highly available & reliable
In-memory data store and
cache for sub-millisecond
response times
VPC for cluster isolation,
encryption at rest and
transit, and HIPAA
compliance
Read scaling with
replicas, write and
memory scaling with
sharding, undisruptive
scaling
Multi-AZ with automatic
failover
Fully managed, Redis or Memcached compatible, in-memory data store andcache

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Internet scale applications need low latency and high throughput
In-memory data stores and caches:
• Deliver sub-millisecond performance
• Deliver high throughput to support millions of users
• Significantly reduce cost-at-scale
Gaming
leaderboards
Social
media
Ride
hailing
Session
stores
Media
streaming
Dating
Why Use In-Memory Data Stores and Caches

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Modernization Paths

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The 6Rs of Migration Planning
Discover, Assess &
PrioritizeApplications
Use Migration Tools
Transition Production
Redesign Application/
InfrastructureArchitecture
App Code
Development
PurchaseCOTS/
SaaS & licensing
Validation
Modify underlying
Infrastructure
Full ALM/ SDLC
Manual Config
Manual Deploy
Manual Install
Determine
Migration Path
Automate
Manual Install & Setup
Integration
Determine
new platform

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Relational Database Modernization

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Relational Database Modernization
• Oracle 11gR2 requires additional extended support contract and
additional fees from Jan 1, 2019 with support ending on Dec 31,
2020
• Microsoft SQL Server 2008 and 2008 R2 are in extended support
with support ending on Jul 9, 2019
• MySQL 5.5 support ends on Dec 2018
• PostgreSQL 9.3 support ended in Sep 2018

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Relational Database Modernization - Options
• Upgrade on-prem
• Upgrade on-prem with Amazon RDS on VMware
• Migrate to AWS on VMware and then upgrade
• Migrate to EC2/RDS and then upgrade
• Migrate directly to a later version in AWS
• Migrate to another database, e.g. Oracle to MySQL
• If on RDS – use the single click upgrade option

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RDS Upgrade Demo!

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Migrating Databases to AWS

31. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Database Migration Service (AWS DMS)
DMS migrates databases to AWS easily and securely with
minimal downtime. It can migrate your data to and from
most widely used commercial and open-source databases.
Amazon Aurora
DynamoDBS3 Bucket

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AWS Schema Conversion Tool (AWS SCT)
SCT helps automate many database schema and code
conversion tasks when migrating between database engines
or data warehouse engines
Amazon Aurora

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Database Migration the Easier Way
Step 1: Convert or copy your schema
Source DB or DW
AWS SCT
Native tool
Destination DB or DW
Step 2: Move your data
Source DB or DW
AWS SCT
Destination DB or DW
AWS DMS

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New NoSQL support
Migrate to AWS
• Move from MongoDB to Amazon DynamoDB
• Move from MongoDB to relational DB’s
• Move from Apache Cassandra to DynamoDB
Move between NoSQL and SQL
• Change technologies
Amazon Aurora
DynamoDB
DynamoDB
RDS

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Support for S3 as a Source and Target
Extract Data from any supported DMS source to S3 and to any DMS target
S3 Bucket
Amazon Aurora
S3 Bucket Amazon Aurora
DynamoDB

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SCT can tell you how hard the migration will be
1. Connect SCT to
Source and Target
databases.
2. Run Assessment
Report.
3. Read Executive
Summary.
4. Follow detailed
instructions.

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>90,000 Databases Migrated with DMS

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What Data Store to use for
Refactoring/Rearchitecting?

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Challenge: What Data Store to Use?
• Many problems can be solved with NoSQL, RDBMS or even in-
memory cache technologies
• Non-functional requirements can help identify appropriate services
• Solution: Classify your organizations non-functional requirements
and map them to service capabilities

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Determine Your Non-Functional Requirements
Requirement
Latency > 1s 200 ms -1s 20 ms – 200 ms < 20 ms
Durability 99.99 99.999 99.9999 > 99.9999
Storage Scale < 256 GB 256 GB – 1 TB 1 TB – 16 TB > 16 TB
Availability 99 99.9 99.95 > 99.95
Data Class Public Important Secret Top Secret
Recoverability 12 – 24 hours 1 – 12 hours 5 mins – 1 hour < 5 mins
Skills None Average Good Expert
This is only an example. Your company’s classifications will be different
There will be other requirements such as regulatory compliance too.

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Map Non-Functional Requirements to Services
Service Latency Durability Storage Availability Recoverability from AZ Failure
(RPO, RTO)
RDS < 100 ms > 99.8 (EBS) 16 TB 99.95 0s and 90s (MAZ)
Aurora < 100 ms > 99.9 64 TB > 99.95 0s and < 30s (MAZ)
Aurora + ElastiCache < 1 ms > 99.9 64 TB > 99.95 0s and < 30s (MAZ)
DynamoDB < 10 ms > 99.9 No Limit > 99.99 0s and 0s
DynamoDB / DAX < 1 ms > 99.9 No Limit > 99.99 0s and 0s
ElastiCache Redis < 1 ms N/A 6.1 TiB 99.95 0s and < 30s (MAZ)
Elasticsearch < 200 ms > 99.9 1.5 PB 99.95 0s and < 30s (Zone Aware)
S3 < 500 ms 99.999999999 No Limit 99.99 0s and 0s
The information below is not exact and does not represent SLAs

42. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Finalizing Your Data Store Choices
• After mapping your non-functional requirements to services you should
have a short list to choose from
• Functional requirements such as geospatial data and query support will
refine the list further
• You may institute standards to make data store selection simpler and also
make it easier for people to move between teams, e.g Redis over
Memcached and PostgreSQL over MySQL. These can still be overridden,
but require justification to senior management

43. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Modernizing an Application Data
Tier – Step by Step

44. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon
Route 53
CloudFront
Availability Zone #1
Amazon S3
Availability Zone #2
Amazon EC2Amazon EC2
Auto Scaling
Geo Routing
US East
User
Amazon
CloudWatch
RDS DB Instance
Active (Multi-AZ)
RDS DB Instance
Standby (Multi-AZ)

45. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon
Route 53
CloudFront
Availability Zone #1
Amazon S3
Availability Zone #2
Amazon EC2Amazon EC2
Auto Scaling
Geo Routing
US East
User
Amazon
CloudWatch
RDS DB Instance
Active (Multi-AZ)
RDS DB Instance
Standby (Multi-AZ)
RDS DB
instance read
replica

46. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon
Route 53
CloudFront
Availability Zone #1
Amazon S3
DynamoDB
Availability Zone #2
Amazon EC2Amazon EC2
Auto Scaling
Geo Routing
US East
User
Amazon
CloudWatch
RDS DB Instance
Active (Multi-AZ)
RDS DB Instance
Standby (Multi-AZ)
RDS DB
instance read
replica

47. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon
Route 53
CloudFront
Availability Zone #1
Amazon S3
DynamoDB
Availability Zone #2
Amazon EC2
ElastiCache
Memcached
Amazon EC2
Auto Scaling
Geo Routing
US East
User
Amazon
CloudWatch
RDS DB Instance
Active (Multi-AZ)
RDS DB Instance
Standby (Multi-AZ)
RDS DB
instance read
replica

48. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon
Route 53
CloudFront
Availability Zone #1
Amazon S3
DynamoDB
Availability Zone #2
Amazon EC2
ElastiCache
(Redis Master)
ElastiCache
Memcached
Amazon EC2
Redis Slave
Auto Scaling
Geo Routing
US East
User
Amazon
CloudWatch
Aurora DB Instance
Active (Multi-AZ)
Aurora DB
instance read
replica
Amazon Redshift

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Taking it Further…
• Microservices
• Containers with Amazon Elastic Container Service, Amazon Elastic
Container Service for Kubernetes, AWS Fargate
• Serverless with AWS Lambda, AWS Batch, AWS Step Functions,
Amazon Aurora MySQL Serverless
• Integrate new functionality for IoT, AI/ML, Mobile, AR/VR, and media
– all through managed AWS services

50. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Summary

51. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Summary
• The database market is changing
• Application architecture is changing
• Relational databases are not the best solutions for all workloads
• Choose the best database or data store for the job at hand
• AWS has a comprehensive data management platform that enables
you to modernize your applications

52. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
DW | Big Data Processing | Ad hoc
AWS Databases and Analytics
Broadest and deepest portfolio purpose-built for builders
Business Intelligence & Machine Learning
Data Movement
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Amazon QuickSight
Relational Databases
RDS
Aurora
Data lake (Batch/ETL)
S3/Glacier
(Storage)
Glue
(ETL & Data Catalog)
Machine Learning
Macie
(Data Protection)
Non-Relational Databases
Analytics (OLAP/DSS)
DynamoDB
(Wide Column/Document)
ElastiCache
(Indexed Key Value)
Amazon
Redshift
EMR Athena
Kinesis Data
Analytics
Elasticsearch
Service
Real-time
Operational (OLTP)
Neptune
(Graph)

53. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Digital Training
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• AWS Database Services Overview »
• AWS Development Tools Services Overview »
• AWS Analytics Services Overview »
• Introduction to AWS Application Discovery Service »
• Migrating and Tiering Storage on AWS »
• VMware Cloud on AWS (Technical) »
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