In this session, we dive deep into applying the "AWS Purpose-Built Database Strategy" to determine which databases to use for which components of your application. Learn how to evaluate a new workload for the best managed database option based on specific application needs related to data shape, data size at limit, computational requirements, programmability, throughput and latency needs, etc. This session explains the ideal use cases for relational and non-relational database services, including Amazon Aurora, Amazon DynamoDB, Amazon ElastiCache for Redis, Amazon Neptune, and Amazon Redshift.

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Rick Houlihan
Senior Practice Manager, DBS NoSQL
SRV307
Matching the Database to the Workload

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Agenda
• Database workload classifications
• Traditional approaches to scaling RDBMS
• How NoSQL databases compare
• The flavors of NoSQL on AWS
• What database to use when

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Why did you choose this database for your app?
“Because we heard X is the best new thing.”
“Because we have a site license for X.”
“Because X is what we know how to use.”
“Because this database is purpose built to support
what my application is designed to do.”

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Types of Database Workloads
• Online Transaction Processing (OLTP)
• Most common type of app
• Online Analytics Processing (OLAP)
• BI and ad-hoc data projections
• Decision Support Systems (DSS)
• Long running query aggregations and projections
Operations
Analytics

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Sizing the Workload
Unbounded problems are harder to solve
“I need a root cause analysis engine to correlate transaction level events
to trading patterns across global markets.”
Problems with limited scope are easier to solve
“I need a system to manage inventory in my store.”

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Sizing the Database

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Scaling Relational DBs

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Sharded Relational DBs?
A B C D
?

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NoSQL Databases
• Denormalize and
shard to provide
horizontal scale
• Near unbounded
throughput and
storage
Collection 1
1 TB
Shard A
500 GB
Shard B
500 GB

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00 55 A954 FFAA00 FF
Partition Keys in NoSQL
Id = 1
Name = Jim
Hash (1) = 7B
Id = 2
Name = Andy
Dept = Eng
Hash (2) = 48
Id = 3
Name = Kim
Dept = Ops
Hash (3) = CD
Key Space
Partition Key uniquely identifies an item
Partition Key is used for building an unordered hash index
Allows table to be partitioned for scale

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The Iron Triangle of Data - All About CAP
C
A
PConsistency:
all clients always have
the same view of data
Partition tolerance:
the system works well despite physical
network partitions
Availability:
all clients can always
read and write
CA
MSSQL
Oracle
DB2
MySQL
Aster Data
Greenplum
Postgres
CP
Big Table
Hypertable
HBase
MongoDB
Terastore
Couchbase
Scalaris
DynamoDB
BerkeleyDB
Memcached
Redis
Pick Two
AP
Voldemort
Tokyo Cabinet
KAI
Amazon
DynamoDB
Cassandra
SimpleDB
CouchDB
Riak
Data Models:
Relational
Wide Column
Document
Key/Value

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Technology adoption and the hype curve

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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
AWS Database Migration Service | AWS Snowball | AWS Snowmobile | Amazon Kinesis Data Firehose | Amazon Kinesis Data Streams
Amazon QuickSight
Relational Databases
Amazon
RDS
Amazon
Aurora
Data lake (Batch/ETL)
Amazon S3 / Amazon
Glacier
(Storage)
AWS Glue
(ETL & Data Catalog)
Machine Learning
Amazon Macie
(Data Protection)
Denormalized Databases
Analytics (OLAP/DSS)
DynamoDB
(Wide Column/Document)
Amazon ElastiCache
(Indexed Key Value)
Amazon
Redshift
Amazon
EMR
Amazon
Athena
Amazon
Kinesis Data
Analytics
Amazon
Elasticsearch
Service
Real-time
Opertional (OLTP)
Amazon
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 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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DynamoDB Schema
Table
Items
Attributes
Partition
Key
Sort
Key
Mandatory
Key-value access pattern
Determines data distribution
Optional
Model 1:N relationships
Enables rich query capabilities
All items for key
==, <, >, >=, <=
“begins with”
“between”
“contains”
“in”
sorted results
counts
top/bottom N values

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SQL vs. NoSQL Design Pattern

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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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Amazon Neptune High-Level Architecture
Bulk load
from S3
Database
Mgmt.

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Graph Workloads

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Normalized Graph Design Pattern

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De-normalized Graph Design Pattern
Node Relationship Endpoint
Bill
is a Person
visited Eiffel Tower
Alice
is a Person
visited Eiffel Tower
friend of Bob
Bob
is a Person
born on 7/14/90
friend of Alice
interested in Mona Lisa
Leonardo daVinci is a Person
La Jaconde a Washington
is a Video
is about Mona Lisa
Eiffel Tower
is a Place
located in Paris
7/14/90 is a Date
Paris is a City
The Louvre
is a Museum
located in Paris
Mona Lisa
is a Painting
created by Leonardo daVinci
exhibited at The Louvre
Nodes are the
vertices of a graph
Relationships are the
edges of a graph
Select nodes to get
edges for an entity
Index Relationship and
Endpoint for edge type and
target aggregations
Follow the edges to
traverse the graph
Bob wants to see the Mona
Lisa. While he in Paris he
would like to see other things
his friends have enjoyed.

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Node Query (Primary)
• What entities are in the graph?
Edge Query (Index)
• What relationships do graph entities have?
Hybrid Query (Traversal)
• How are entities related through each other?
Graph Query Types
RDBMS, NoSQL,
GraphDB
GraphDB

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Amazon Redshift – Data Warehousing
Fast, powerful, and simple data warehousing at 1/10 the cost
Massively parallel, petabyte scale
Fast Inexpensive Scalable Secure
Columnar storage technology
to improve I/O efficiency and
parallelize queries. Data load
scales linearly.
As low as $1,000 per
terabyte per year, 1/10th
the cost of traditional data
warehouse solutions.
Resize your cluster up and
down as your performance
and capacity needs
change.
Data encrypted at rest and
transit. Isolate clusters with
VPC. Manage your own keys
with AWS KMS.

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Amazon Athena – Interactive Analysis
Interactive query service to analyze data in Amazon S3 using standard SQL
No infrastructure to set up or manage and no data to load
Ability to run SQL queries on data archived in Amazon Glacier (Coming soon)
Serverless
Zero setup cost. Just
point to Amazon S3,
and start querying.
Pay per query
Pay only for queries run.
Save 30–90% on per-
query costs through
compression.
Open
ANSI SQL interface,
JDBC/ODBC drivers, multiple
formats, compression types,
and complex Joins and data
types.
Easy
Serverless. Zero
infrastructure. Zero
administration.

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Categories of Database
Optimized for Storage Optimized for Compute
Optimized for
Relationships
Normalized relational or
dimensional DW
Denormalized document,
wide column or key value
Denormalized entity
relationship
Ad hoc queries and
aggregations
Instantiated views and
computed aggregations
Ad hoc entity/relationship
aggregations
Scale vertically Scale horizontally Hybrid
Great for OLAP and DSS Built for OLTP or DSS at scale
Designed for graph
traversals
SQL NoSQL Graph

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Infinite Scale:
The database can gracefully increase size
and throughput without practical limits
The Iron Triangle of Purpose (The PIE Theorem)
I
P
E Efficiency:
The database will deliver required query
latency for the workload at all times
Pattern Flexibility:
The database supports random access
patterns and ad hoc queries
PI
Amazon RDS
Elasticsearch
Aurora Serverless
Neptune
IE
Pick Two
PE
Data Models:
Relational
Wide Column
Document
Graph
Columnar
Unstructured
Amazon DynamoDB
Amazon Redshift
Athena

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Hundreds of thousands of customers use DynamoDB

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Hundreds of thousands more use Amazon RDS

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Purpose Built Database Solutions from AWS
Provisioning
Capacity planning
Monitoring
OS patching
Hardware upgrades
Database upgrades
Security patches
Scaling
Monitoring
Performance tuning
Replication across data centers
Re-replicate on server failureProvision new regions
Infrastructure Software
With Zero Unplanned Downtime

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