AWS Databases and Analytics Leadership Session

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Leadership Session:
AWS Databases and Analytics
Raju Gulabani
Vice President
Databases, Analytics, Machine Learning, & Blockchain
AWS
D A T 2 0 6 - L

What to expect
Hear
about the new
announcements,
with an emphasis on
news not covered in
Andy’s keynote
1
Understand
our database and
analytics strategy;
our portfolio of
various services &
how they work
together
2
Plan
how you would use
these services by
appreciating how
others use them
3

Our strategy & our beliefs
1. There is going to be an explosion in data.
2. Cloud will enable a different architecture.
3. One size does not fit all—databases should be
purpose-built.
2010

Analytics
Our portfolio
Broad and deep portfolio, purpose-built for builders
Redshift
Data warehousing
EMR
Hadoop + Spark
Athena
Interactive analytics
Kinesis Data Analytics
Real time
Elasticsearch Service
Operational Analytics
QuickSight SageMaker
S3/Glacier
Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams
Data Movement
Business Intelligence & Machine Learning
Data Lake

Analytics
Our portfolio
S3/Glacier
Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Data Movement
Data Lake
Redshift
Data warehousing
EMR
Hadoop + Spark
Real time
Athena
RDS
MySQL, PostgreSQL, MariaDB,
Oracle, SQL Server
Aurora
MySQL, PostgreSQL
DynamoDB
Key value, Document
ElastiCache
Redis, Memcached
Neptune
Graph
Timestream
Time Series
QLDB
Ledger Database
RDS on VMware
Databases

Our portfolio
Redshift
Data warehousing
EMR
Hadoop + Spark
Athena
Real time
RDS
MySQL, PostgreSQL, MariaDB,
Oracle, SQL Server
Aurora
MySQL, PostgreSQL
DynamoDB
Key value, Document
ElastiCache
Redis, Memcached
Neptune
Graph
Timestream
Time Series
QLDB
Ledger Database
S3/Glacier
Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Data Movement
Analytics Databases
Data Lake
Managed
Blockchain
Blockchain
Templates
Blockchain
RDS on VMware

Three type of projects
Quickly build new
apps in the cloud
Gain new
insights
“Lift and shift” existing
apps to the cloud

Traditionally, analytics looked like this
Relational data
GBs-TBs scale [not designed for PB/EBs]
Expensive: Large initial capex + $10K-$50K/TB/year
90% of data was thrown away because of cost
OLTP ERP CRM LOB
Data Warehouse
Business Intelligence

Our beliefs
1. All data has value. No data should be thrown away.
2. All employees should have access to all data (subject
to company access rules).
2010

Snowball
Snowmobile Kinesis
Data Firehose
Kinesis
Data Streams
S3
Redshift
EMR
Athena Kinesis
Data lakes on AWS
Kinesis
Video Streams
AI Services
QuickSight
Exabyte scale
Store and analyze relational and non-relational data
Purpose-built analytics tools
Cost effective
• Store at 2.3 cents per GB-month in Amazon S3
• Query with Amazon Athena at ½ cent per GB scanned
• DW with Amazon Redshift for $1,000/TB/year
Give access to everyone
• Amazon QuickSight: $0.30 for 30 minutes of use

CHALLENGE
Need to create constant feedback
loop for designers.
Gain up-to-the-minute
understanding of gamer
satisfaction to guarantee gamers
are engaged, resulting in the most
popular game played in the world.
Fortnite | 125+ million players

Epic Games uses data lakes and analytics
Entire analytics platform running on AWS
Amazon S3 leveraged as a data lake
All telemetry data is collected with Amazon Kinesis
Real-time analytics done through Spark on Amazon EMR,
DynamoDB to create scoreboards and real-time queries
Use Amazon EMR for large batch data processing
Game designers use data to inform their decisions
Game
clients
Game
servers
Launcher
Game
services
N E A R R E A L T I M E P I P E L I N E
N E A R R E A L T I M E P I P E L I N E
Grafana
Scoreboards API
Limited raw data
(real time ad-hoc SQL)
User ETL
(metric definition)
Spark on EMR DynamoDB
NEAR REAL-TIME PIPELINES
BATCH PIPELINES
ETL using
EMR
Tableau/BI
Ad-hoc SQLS3
(Data lake)
Kinesis
APIs
Databases
S3
Other
sources

Amazon Lake Formation (sign up for the preview)
Build a secure data lake in days
Move, store, catalog, and
clean your data faster
Move, store, catalog,
and clean your data faster
with machine learning
Enforce security policies
across multiple services
Enforce security policies across
multiple services
Gain and manage new
insights
Empower analyst and data
scientist to gain and manage
new insights

How it works
Data lakes and analytics on AWS
S3
IAM KMS
OLTP
ERP
CRM
LOB
Devices
Web
Sensors
Social Kinesis
Build data lakes quickly
• Identify, crawl, and catalog sources
• Ingest and clean data
• Transform into optimal formats
Simplify security management
• Enforce encryption
• Define access policies
• Implement audit login
Enable self-service and combined analytics
• Analysts discover all data available for analysis
from a single data catalog
• Use multiple analytics tools over the same data
Athena
Redshift
AI Services
EMR
QuickSight
Data
catalog

Amazon Redshift
Highly scalable cloud data warehouse at 10x the performance and 1/10th the cost
of traditional data warehouses
Virtually unlimited
concurrency
Extends your
data lake
Dynamically scales to
support virtually unlimited
number of concurrent users
and growing data volumes
Analyze exabytes of data in the
Amazon S3 data lake together
with petabytes of data loaded
into Amazon Redshift’s high
performance SSDs
10x performance 1/10th the cost
Get faster time-to-insight for all
types of analytics workloads;
powered by machine learning,
columnar storage and MPP
Start at $0.25 per hour,
scale out as low as $1,000
per terabyte per year

Automatically spins up additional clusters on-demand
Handles virtually unlimited number of concurrent users
Accrued minutes make it free for most customers
Amazon Redshift Concurrency Scaling (Preview)
Consistently fast performance at virtually unlimited concurrency
Redshift Managed
S3
Cluster Leader
Node
Data Data
Caching Layer
Cluster Leader
Node

Amazon Redshift Spectrum
Extend the data warehouse to exabytes of data in S3 data lake
S3 data lakeRedshift data
Amazon Redshift Spectrum
query engine
Exabyte Redshift SQL queries against Amazon S3
Join data across Amazon Redshift and Amazon S3

Amazon Redshift Elastic Resize (GA)
Quickly scale up or down to increase performance on-demand
Cluster Leader
Node
Data Data Data Data
Redshift Managed
S3
Add/remove additional nodes to cluster in minutes
Available today

Amazon Redshift is >3x faster than 6 months ago
100%
181%
237%
284%
350%
Redshift 6 months ago Redshift July 2018 Redshift Aug 2018 Redshift Sep 2018 Redshift Oct 2018
Queriesperhour
asa%ofRedshift6monthsago
Normalized Queries Per Hour (QPH)
(assuming Redshift’s QPH 6 months ago = 100%; higher is better)

“20 percent of our queries
now complete in less than one
second. Best of all, we didn’t
have to change anything to
get this speed-up with
Redshift, which supports our
mission-critical workloads.”
-Greg Rokita, Executive Director of
Technology, Edmunds

Fastest: up to 16x faster
100%
34%
6%
As a % of Amazon Redshift’s queries per hour
Based on the cloud DW benchmark derived from TPC-DS 3 TB dataset, 4-node cluster
Queries per hour

Most cost effective: 1/10 the cost
$ per hour
(As a % of Amazon Redshift’s $ per hour with a 3yr RI)
Based on the cloud DW benchmark derived from TPC-DS 3 TB dataset, 4-node cluster
2683%
500%
320%
211%
100%

CHALLENGE
Needed to analyze data to find
insights, identify opportunities, and
evaluate business performance.
The Oracle DW did not scale, was
difficult to maintain, and costly.
SOLUTION
Deployed a data lake with Amazon S3,
and run analytics with Amazon
Redshift, Amazon Redshift Spectrum,
and Amazon EMR.
Result: They doubled the data stored
(100PB), lowered costs, and was able
to gain insights faster.
50 PB of data
600,000 analytics jobs/day

Equinox Fitness migrated from Teradata to Redshift
Maximilian
(ELT scripts)
Spark
on EMR
Redshift
S3
Clickstream
Cycling logs
Club
management
software
Applications
Social
Redshift
Spectrum
EMR
Athena
Equinox
apps
3rd party
apps
Migrated from Teradata data warehouse
Built a DW with Redshift and data lake with S3
Analytics on data lake with Amazon Athena,
Amazon Redshift Spectrum, and Amazon EMR
Increased user productivity to move faster
Amazon Redshift costs ~20% of its original
Teradata maintenance & support

Amazon QuickSight
First BI service with pay-per-session pricing for everyone in your organization
Serverless, cloud-powered BI service (no servers to manage)
Scale from 10s of users to 100s of thousands of users
Pay only for what you use
• Readers: $0.30/30 min session with a $5/user/month max
• Authors: $18/month/Author
Integrates with S3, Athena, Redshift, RDS, Aurora, & EMR

AWS Directory Service
Microsoft AD
Custom Date Format Dashboard Save As Aggregate Calculations Readers Groups
Private VPC
25 GB SPICE
tables
Spark and Presto Connector Scheduled refresh Just In Time Provisioning One-click upgrade
Search Totals Excel Custom Range
100+
new features released since
launch
Federated SSO
Athena connector Export to CSV S3 Analytics
Week Aggregation Aurora PostgreSQL Calculations in SPICE
Cross Account
S3 Access
Aggregate Filters Hourly refresh
Row level security Hourly refresh
10K Filter Values On-screen controls
Redshift Spectrum
Support
KPI Chart
Spark Connector
AWS Directory Service
AD Connector
Tabular Reports Data labels
URL Actions
Combo Charts
Audit logging
with CloudTrail
Geospatial maps Count Distinct Parameters Relative Date Filters Filter Groups
Table calculations Snowflake Connector SaaS Connectors Teradata Connector HIPAA PCI compliance
Amazon QuickSight has been innovating quickly

Amazon QuickSight—embedded dashboards
Supercharge your applications with embedded dashboards
Fully interactive with drill down, filtering, & external links
No servers to manage, no long-term commitments
Pay for usage with pay-per-session reader pricing
Easy embedding with JavaScript SDK

Embedded NFL Next Gen Stats Dashboards
“With the Amazon QuickSight Readers and
pay-per-session pricing, we are able to
extend these secure, customized and easy
to use dashboards for each club without
having to provision servers or manage
infrastructure – all while only paying for
actual usage.”
Matt Swensson
Vice President, Emerging Products and Technology
Real-time stats for NFL games
Embedded in NFL Next Gen Stats Portal
Shared with 100s of users across NFL,
32 clubs and broadcast partners

Amazon QuickSight is used by customers at the largest scale
One of the world’s largest
metals and mining companies
deployed Amazon QuickSight
with its critical risk
management (CRM) solution
to ensure employee safety.
Thousands of employees
use its CRM globally.
Uses Amazon QuickSight
embedded in its Converge
Platform, a governance, risk,
and compliance healthcare
solution. Tens of thousands
of users across 900
healthcare organizations
use this platform.
Amazon.com is using
Amazon QuickSight
company-wide

Amazon QuickSight—ML Insights (Preview)
Automated business insights powered by ML and natural language
ML-powered anomaly detection
ML-powered forecasting
Auto-narratives

Discover all the hidden trends and
anomalies on millions of metrics
Amazon QuickSight—ML Insights
Example: anomaly detection

“Sales for office supplies in APAC
was 15% above expected.”

“SMB Segment was the top
contributor.”

“It’s significant because SMB
typically only accounts for 30% of
sales.”

QuickSight ML-powered forecasting Traditional BI forecasting
Captures seasonality and upward trends
Automatically excludes bad data
High confidence band
Captures only seasonality
Missing upward trend
Confidence band influenced by bad data
QuickSight ML Insights vs. traditional BI forecasting
VS.

Insights in plain language narrative
Embedded within your dashboard
No more staring at dashboards for hours!
Fully customizable to meet every need
No coding needed. Easy-to-use UI templates.
Auto-narratives

AWS analytics
Any analytic workload, any scale, at the lowest possible cost
Redshift
Data warehousing
EMR
Hadoop + Spark
Athena
Real time
Analytics
QuickSight
Business Intelligence
S3/Glacier Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Data Lake

More places to learn about analytics services
Amazon Athena
Amazon
Redshift
Amazon
Elasticsearch
Service
AWS Lake
Formation
ANT401-R2: Deep Dive and Best Practices for
Amazon Redshift | Fri 11:30
ANT 401-R1: Deep Dive and Best Practices for
Amazon Redshift | Thu 4:00
ANT202-R1: Modern Cloud Data Warehousing ft.
Intuit | Thu 2:30
ANT350-R1: What's New with Amazon Redshift ft.
McDonald's | Thu 3:15
Sessions that already occured: ANT202-R,
ANT350-R
ANT323-R1: Build Your Own Log Analytics
Solutions on AWS | Thur 11:30
Sessions that already occured: ANT334-R,
ANT334-R1, ANT323-RANT203
Introduction to AWS Lake Formation - Build a
Secure Data Lake in Days | Wed 7:00pm | Venetian,
Level 4, Delfino 4005.
Sessions that already occurred: ANT205
ANT340-R1: A Deep Dive into What's New with
Amazon EMR | Fri 3:00
Sessions that already occurred: ANT204, ANT312,
ANT340-R
Amazon Kinesis
Amazon
QuickSight
AWS Glue
Introducing Amazon Kinesis Data Analytics for
Java applications | Thu 12:15pm | MGM, Level 1,
South Concourse 105
ANT322-R1: High Performance Data Streaming
with Amazon Kinesis: Best Practices | Thu 1:00
ANT 310: Architecting for Real-Time Insights
with Amazon Kinesis | Thu 3:15
Sessions that already occurred: ANT 208,
ANT322-R
Introducing ML-powered insights with Amazon
QuickSight | Wed 1:00pm | Aria East, Level 1,
Joshua 9
ANT311: NFL and Forwood Safety Deploy
Business Analytics at Scale with Amazon
QuickSight | Fri 11:30
Amazon EMR
Sessions that already occured: ANT309, ANT308Sessions from prior days: ANT324

Customers tell us: they have three type of projects
Quickly build new
apps in the cloud
Gain new
insights
“Lift and shift” existing
apps to the cloud

Characteristics of modern applications
Internet-scale and transactional
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 accessSocial mediaRide hailing Media streaming Dating

AWS databases services
Purpose-built for all your app needs
DynamoDB NeptuneRDS
Aurora CommercialCommunity
Timestream QLDBElastiCache
Relational Key-value Document In-memory Graph Time series Ledger

Amazon DynamoDB
Fast and flexible key value database service for any scale
Comprehensive
security
Encrypts all data by
default and fully integrates
with AWS Identity and
Access Management for
robust security
Performance at scale
Consistent, single-digit
millisecond response times at
any scale; build applications with
virtually unlimited throughput
Global database for
global users and apps
Build global applications with
fast access to local data by easily
replicating tables across multiple
AWS Regions
Serverless
No hardware provisioning,
software patching, or
upgrades; scales up or down
automatically; continuously
backs up your data

DynamoDB powers the world’s largest applications

The need for transactions support
Scenario: Customer purchases an item
“PUT”:
“TableName”: “Orders”,
“OrderStatus”: “Sold”,
“Item”: “Bike”,
“Quantity”: “1”
“PUT”:
“TableName”: “Inventory”,
“Quantity”: “- 1”
Transact-write-items
{
“PUT”:
“TableName”: “Orders”,
“OrderStatus”: “Sold”,
“Quantity”: “1”
“PUT”:
“TableName”: “Inventory”,
“Quantity”: “- 1”
}
If code fails here, we inserted the
order but did not update inventory
If code fails here, we updated some
of our inventory, but not all
Developer needs to write code to
undo a partial operation
Transaction support: The system
ensures everything happens (or not)
VS.

Amazon DynamoDB Transactions (GA)
Build internet-scale apps with ACID transactions
Simplify application
code with ACID
guarantees
Run transactions
for large scale
workloads
Accelerate legacy
migrations

DynamoDB—read/write capacity on-demand (GA)
No more capacity planning—pay only for what you use
No capacity planning
No need to specify how much
read/write throughput you expect to
use
Ideal for unpredictable workloads
Ramp from zero to tens of thousands of requests
per second on demand
Pay only for what you use
Pay-per-request pricing

Amazon Timestream (sign up for the preview)
Fast, scalable, fully managed time series database
1,000x faster and 1/10th the
cost of relational databases
Collect data at the rate of
millions of inserts per
second (10M/second)
Trillions of
daily events
Adaptive query processing
engine maintains steady,
predictable performance
Analytics optimized
for time series data
Built-in functions for
interpolation, smoothing,
and approximation
Serverless
Automated setup,
configuration, server
provisioning, software patching

Amazon Quantum Ledger Database (QLDB) (Preview)
Fully managed ledger database
Track and verify history of all changes made to your application’s data
Immutable
Maintains a sequenced record of
all changes to your data, which
cannot be deleted or modified;
you have the ability to query and
analyze the full history
Cryptographically
verifiable
Uses cryptography to
generate a secure output
file of your data’s history
Easy to use
Easy to use, letting you
use familiar database
capabilities like SQL APIs
for querying the data
Highly scalable
Executes 2–3x as many
transactions than ledgers
in common blockchain
frameworks

Amazon Managed Blockchain (preview)
Create and manage scalable blockchain networks
Choice of Hyperledger
Fabric or Ethereum
Hyperledger Fabric available
today; Ethereum coming soon
Fully managed
Create blockchain networks
with a few clicks; Manage
them with simple API calls
Easily analyze
blockchain activity
Easy to move data into
QLDB for further analysis
Scalable and
secure
Support thousands of client
applications running
millions of transaction;
integrates with AWS KMS

Amazon QLBD vs. Amazon Managed Blockchain
Amazon QLDB Amazon Managed Blockchain
Central trusted authority
Track and verify transactions with centralized
ownership
Ledger is immutable and cryptographically
verifiable
Ledger is owned by a trusted and centralized
authority
No central trusted authority
Execute transactions and contracts with
decentralized ownership
Ledger is immutable and cryptographically
verifiable
Each party maintains their own copy of the
ledger
Use Ethereum or Hyperledger Fabric to build
blockchain networks

More places to learn about purpose-built databases
Building modern applications using
DynamoDB Transactions | Wed 7:00pm |
MGM, Level 3, Premier Ballroom 310
Sessions that already occurred: DAT321,
DAT401, DAT201, DAT303, DAT314,
DAT325, DAT320, DAT332
Amazon
DynamoDB
Amazon
Neptune
Amazon
ElastiCache
Amazon
QLDB
Amazon
Managed
Blockchain
Sessions that already
occurred: DAT202, DAT319,
DAT302-R1, DAT302-R
occurred: DAT403, DAT315,
DAT316
Use Cases for Amazon QLDB | Fri 9:15 |
Mirage, Montego D
Deep Dive on Amazon Blockchain |
Wed 3:15 | Venetian, Level 2,
Titian 2205 - T2

AWS Database Migration Service
M I G R A T I N G
D A T A B A S E S
T O A W S
Migrate between on-premises and AWS
Migrate between databases
Automated schema conversion
Data replication for zero
downtime migration

Customers want to lift and shift to the cloud
Relational
databases
Non-relational
databases
Data
warehouses
Hadoop
and Spark
Redshift EMR
Operational
analytics
Elasticsearch
ServiceAurora DynamoDB
Business
Intelligence
QuickSightRDS

>100,000 databases migrated with DMS

Customers are migrating their workloads to AWS
Verizon is migrating over 1,000 business-critical applications and database backend systems to AWS,
several of which also include the migration of production databases to Amazon Aurora.
Wappa migrated from their Oracle database to Amazon Aurora and improved their
reporting time per user by 75 percent.
Trimble migrated their Oracle databases to Amazon RDS and project they will pay about 1/4th
of what they paid when managing their private infrastructure.
Intuit migrated from Microsoft SQL Server to Amazon Redshift to reduce data-processing timelines
and get insights to decision makers faster and more frequently.
Equinox Fitness migrated its Teradata on-premises data warehouse to Amazon Redshift. They went
from static reports to a modern data lake that delivers dynamic reports.
Eventbrite moved from Cloudera to Amazon EMR and were able to cut costs dramatically, spinning
clusters up/down on-demand and using Spot (saving > 80%) and Reserved Instances.
By December 2018, Amazon.com will have migrated 88% of their Oracle DBs (and 97% of
critical system DBs) moved to Amazon Aurora and Amazon DynamoDB. They also migrated their
50 PB Oracle Data Warehouse to AWS (Amazon S3, Amazon Redshift, and Amazon EMR).
Samsung Electronics migrated their Cassandra clusters to Amazon DynamoDB for their Samsung
Cloud workload with 70% cost savings.

Amazon Aurora
MySQL and PostgreSQL compatible relational database built for the cloud
Performance and availability of commercial-grade databases at 1/10th the cost
Availability
and durability
Fault-tolerant, self-healing
storage; six copies of data
across three AZs; continuous
backup to S3
Fully managed
Managed by RDS:
no hardware provisioning,
software patching, setup,
configuration, or backups
Highly secure
Network isolation,
encryption at
rest/transit
Performance
and scalability
5x throughput of standard
MySQL and 3x of standard
PostgreSQL; scale-out up to
15 read replicas

Aurora Global Database (GA)
High-performance database for globally-distributed applications
Single Global Database with cross region replication
Replication typically completes in less than a second
No impact on database performance
Write master in one region and read replicas in other regions
Cross-region disaster recovery
Local read latency for applications with global users
Primary Region Secondary Region
Application
Storage Storage
Replication <1s

Amazon RDS
Managed relational database service with a choice of six popular database engines
Available & durable
Automatic Multi-AZ data
replication; automated backup,
snapshots, failover
Easy to administer
No need for infrastructure
provisioning, installing and
maintaining DB software
Highly scalable
Scale database compute
and storage with a few
clicks with no
application downtime
Fast & secure
SSD storage and guaranteed
provisioned I/O; data
encryption at rest and
in transit

Databases in private data centers are still difficult
and expensive to set up and manage
Difficult to set up and
manage databases for
high availability across
multiple nodes
Personnel needed to create
the database image, install
operating system,
packages, and setup
Burdensome to support
multiple versions and
applying patching
?

Amazon RDS on VMware (Preview)
Managed service for on-premises databases
RDS deployed as a service in on-premises VMware private data centers (vSphere)
Automates management of on-premises databases and hybrid backup and scaling
Available and
durable
Enable hybrid features
and tap into AWS for
high availability, backup,
and restore
Secure and
compliant
Automate management of
databases for workloads that
must remain on-premises to
adhere to strict data policies
Fully managed
Easy to provision, monitor, and
operate relational databases in
your private data center
Scalability and
performance
Scale storage, compute, and
memory of on-premises
databases from a single,
simple interface

How it works
Amazon RDS on VMware
RDS on VMware
Deploy RDS on VMware
in your private data
center and manage on-
premises databases using
a single RDS interface
RDS interface
Use the Amazon RDS
console, APIs, or CLI to
provision databases
Log in or create
AWS account
Find RDS on VMware in
the console and choose
AWS region
Download and install
the connector
Download & install the RDS
connector in your VMware
vSphere environment to
establish secure VPN
connection between AWS and
your private data center
Automate
database
management
RDS on VMware
automates database
management tasks
including provisioning,
patching, backups and
failover

More places to learn about Aurora, RDS, and DMS
Sessions that already occurred:
DAT204-R1, DAT313, DAT318,
DAT204-R, DAT304-R, DAT304-R1,
DAT305-R, DAT305-R1, DAT336
Amazon Aurora
DMS
Amazon RDS Deep Dive on Amazon RDS on VMware
| Thu 1:45 | Aria East, Plaza Level,
Orovada 2
Chalk Talk on Amazon RDS on
VMware | Fri 10:45 | Mirage,
Martinique A
DAT323: Best Practices for Running
SQL Server on Amazon RDS | Thu 1:00
DAT402: Using Performance Insights
to Optimize Database Performance |
Thu 12:15
Sessions that already occurred:
DAT203, DAT322, DAT324, DAT317
occurred: DAT207

When to Use Which Services
Situation Solution
Existing application Use your existing engine on RDS
• MySQL Amazon Aurora, RDS for MySQL
• PostgreSQL Amazon Aurora, RDS for PostgreSQL
• MariaDB Amazon Aurora, RDS for MariaDB
• Oracle Use SCT to determine complexity Amazon Aurora, RDS for Oracle
• SQL Server Use SCT to determine complexity Amazon Aurora, RDS for SQL Server
New application • If you can avoid relational features DynamoDB
• If you need relational features Amazon Aurora
In-memory store/cache • Amazon ElastiCache
Time series data • Amazon Timestream
Track every application change, crypto verifiable.
Have a central trust authority
• Amazon Quantum Ledger Database (QLDB)
Don’t have a trusted central authority • Amazon Managed Blockchain
Data Warehouse & BI • Amazon Redshift, Amazon Redshift Spectrum, and Amazon QuickSight
Adhoc analysis of data in S3 • Amazon Athena and Amazon QuickSight
Apache Spark, Hadoop, HBase (needle in a
haystack type queries)
• Amazon EMR
Log analytics, operational monitoring, & search • Amazon Elasticsearch Service and Amazon Kinesis

Architect services ground-up for the cloud
and for the explosion of data
Offer a portfolio of purpose-built
services, optimized for your workloads
Help you innovate faster through
managed services
Our approach
Provide services that help you migrate
existing apps and databases to the cloud

EA is supporting their most
mission critical workloads
with Amazon Redshift.
They were able to increase
query throughput by 2x
over the last 12 months.

Amazon Kinesis—real time
Easily collect, process, and analyze video and data streams in real time
Capture, process,
and store video
streams for
analytics
Load data streams
into AWS
data stores
Analyze data
streams with SQL
Build custom
applications that
analyze data
streams
Kinesis Video
Streams
Kinesis
Data Streams
Kinesis Data
Firehose
Kinesis Data
Analytics

Build real-time streaming analytics in your Java apps
Use Java Integrated Development Environment of choice
Libraries include pre-built stream processing operators
Real-time application can be built in hours
Kinesis Data Analytics—Java support (GA)
Devices
Sensors
IoT
Kinesis Data
Streams
Kinesis Data
Analytics
Java IDE

Tailored recommendations to increase performance & reduce cost
Redshift’s machine learning engine uncovers optimizations
Operations such as vacuum and analyze run behind the scenes
Available today
Redshift ML based auto-tuning (GA)
Clusters always optimized for best performance and lowest cost
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AUTO
ADVISE
ADVISE

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AWS Databases and Analytics Leadership Session

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