The document discusses choosing the right database for different types of workloads. It covers operational databases like Amazon DynamoDB, Amazon RDS, Amazon ElastiCache and Amazon Neptune that are well-suited for transactional workloads. It also discusses analytic databases like Amazon Redshift, Amazon Athena, Amazon Kinesis Analytics and Amazon Elasticsearch Service that are well-suited for large-scale analytics and business intelligence workloads. The document emphasizes that AWS offers a variety of purpose-built databases and there is no need to pick just one, as different databases can be combined to solve different aspects of a problem.

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Ozkan Can, Senior Solutions Architect, AWS
Choosing the Right Database for My
Workload: Purpose-Built Databases

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Looking at the Specifics
Purpose of a database Your application needs

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A Short Break from Generalities
Relational Non-Relational
NoSQL SQL
Schema Schema-free
Unstructured Structured

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Database Workloads
Data Considerations
Shape Size Compute

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Shape
Purpose-Built For Optimized for When you need to Example Workload
Row Store Operate on a record or group of records Payroll
Column Store Aggregations, scans and joins Analytics
Key-Value Store Query by key with high throughput & fast ingestion Tracking devices
Document Store Index & store documents for query on any property Patient data
Graph Store Persist and retrieve relationships Recommendations
Time-Series Store Store and process data sequence Process Engine telemetry
Unstructured Store Get and put of objects Store user reviews

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Size
Considerations Example Workload
Size at limit – bounded or unbounded
Number of employees – bounded
Number of sensors – unbounded
Working set size & caching
10-years of sales data but only the last 12-months is queried
Session data for users of a streaming service
Retrieval size
Get one row
Get one thousand rows
Partitionable or monolithic
Storage and processing of car location data is partitionable
Company payroll data has no natural partition boundary

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Compute
Considerations Example Workload
Compute functions
Sum of sales for the last 12-months
Get & Put data
Throughput
Million users browsing a product catalogue every second
50 doctors looking at 300 patient records per day
Latency
Get the location of a car in 5 milliseconds
Get the min, max & average deal size for the last 12-months in 5 seconds
Change rate
Inventory counts are frequently updated
Sales records are never updated
Rate of ingestion
Location telemetry from cars added to the database every minute
New employees records being added to the database

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My [insert your favorite DB] works for everything
General purpose Special purpose
One size fits all Efficiency at scale

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But Which Database to Use When?
Decision points and considerations

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Managed database services
DevOps
Build Deploy Operate
code, integrate, test provision, configure, rollout secure, monitor, scale, HA
All conveniently located at the end of an API call

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But Which Databases to Use When?
Why pick just one?

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Our Strategy

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Operational Analytics
transactional retrospective
system of record streaming
content management predictive
Back to Generalities

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Operational Analytics
transactional retrospective
system of record predictive
content management streaming

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General characteristics
• Usually a good fit for caching
• Small compute size – few rows,
items, documents per request
• Low-latency
• High-throughput
• High-concurrency
• Mission critical HA, DR and data
protection
Primary dimensions to consider
• Size at limit – bounded or
unbounded
• Rows, key-values or documents
• Need relational capabilities or not
• partitioned or monolithic
• Push-down compute requirements
• Change velocity
• Ingestion requirements
Operational workloads

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Relational or Not
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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Amazon Relational Database Service (RDS)
Managed relational database service with a choice of six popular database engines
Easy to administer Highly scalable Available &
Durable
Fast
No need for infrastructure
provisioning, installing and
maintaining database
software
Scale database compute
and storage with a few
mouse clicks with no
downtime
Multi-AZ:
Automatically
replicates data to in a
different AZ.
Automated backup,
snapshots, failover
Chose between 2 SSD-
backed storage for high
performance OLTP

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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
NoSQL database that supports both document and key-value structures
Fast, consistent
performance
Highly scalable Fully managed
Business Critical
Reliability
Consistently 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 is replicated across
fault tolerant availability
zones, with fine-grained
access control.

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Fully managed, in-
memory cache for
DynamoDB.
Reduces DynamoDB
response times from
milliseconds to
microseconds.
Amazon DynamoDB Accelerator (DAX)
Fully managed write-through cache for DynamoDB

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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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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
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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Operational database dimensions
Size at limit – bounded
Size at limit – unbounded
key-values or documents
Rows
Graph Data
Need relational capabilities
Partitioned
Push-down compute requirements
Change Velocity
Amazon
DynamoDB
Amazon
RDS
Amazon
Neptune

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Operational Analytics
transactional retrospective
system of record predictive
content management streaming

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General characteristics
• Almost always a columnar
• Large and usually partitioned
• large compute size – millions of
items involved in query
• Heavy compute push down
• Batch writes or trickle inserts
• Little to no updates
• Needs a lot of memory and often
in-memory compute capabilities
Primary dimensions to consider
• Streaming or not
• Latency requirements
• ETL or no ETL
• Serverless or dedicated compute
• Always active or occasionally active
• Data formats
Analytic workloads

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Amazon Athena – Interactive analysis
Interactive query service to analyze data in Amazon S3 using standard SQL
No infrastructure to setup or manage and no data to load
$
SQL
Query Instantly
Zero setup cost. Just
point to 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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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 Kinesis Analytics
Process and Analyze Streaming Data in Real-time with SQL
Capture streaming data with
Kinesis Streams or Kinesis Firehose
Run standard SQL queries
against data streams
Kinesis Analytics can send processed data
to analytics tools so you can create alerts
and respond in real-time
KINESIS
FIREHOSE
KINESIS
STREAMS
KINESIS ANALYTICS

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Amazon Elasticsearch Service
Easy to Use
Fully-managed.
Deploy production-ready
clusters in minutes.
Open
Direct access to
Elasticsearch open-source
APIs. Supports Logstash
and Kibana.
Secure
Secure access with VPC to
keep all traffic within AWS
network.
Available
Zone awareness replicates
data between two AZs.
Automatically monitors &
replaces failed nodes.
Easy to deploy, secure, operate, and scale Elasticsearch
Customers use Elasticsearch for log analytics, full text search, & application monitoring

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Analytics database dimensions
Streaming analytics
Serverless ad-hoc query
Process, prepare and index in-place
Low-latency for reporting and BI dashboards
Pay per query
Data warehouse with multiple enterprise data sources
Query data directly in S3 without format conversions
Directly query CSV, JSON, TSV or text files
Amazon
Redshift
AthenaKinesis Analytics

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Well Modelled DataData Exploration
Non-SQL Analytics
Real-Time analytics
Managed Storage Delivery
Amazon
Elasticsearch Service
Kinesis Analytics
Athena Amazon
Redshift
Amazon S3
Why pick one when you can use all three?

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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
QuickSight
Relational
Databases
RDS
Aurora
Data Lake
S3/Glacier Glue
(ETL & Data Catalog)
SageMaker
Non-Relational
Databases
Analytics
DynamoDB
ElastiCache
(Redis, Memcached)
Neptune
(Graph)
Redshift EMR Athena
Kinesis
Analytics
Elasticsearch
Service
Real-time
Operational Databases

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In Closing
AWS offers a myriad of services designed to help you solve your toughest problems at scale – no need to just
pick one service
When selecting a data service, consider the dimensions and pick the best match for each component of your
application

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Go Build Something!
Q&A