Databases, Analytics & Artificial Intelligence/ Machine Learning, originally presented at AWS re:Invent Comes to London on January 24, 2019.

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Databases, Analytics, & AI/ML
Matt Johnson
Manager, Solutions Architecture
WWPS UK
T e c h n i c a l T r a c k – S e s s i o n 3
Andrew Langhorn
Solutions Architect
WWPS UK

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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

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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

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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

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Amazon Timestream (preview)
Fast, scalable, fully managed time series database
Collect data at the rate of
millions of inserts per
second (10M/second)
1,000x faster and 1/10th the
cost of relational databases
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

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Amazon Timestream: How it works
Time-series application

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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

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Amazon QLDB: How it works
Centralized ledgers

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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

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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

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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

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Why data lakes?
Data Lakes provide:
Relational and non-relational data
Scale-out to EBs
Diverse set of analytics and machine learning tools
Work on data without any data movement
Designed for low cost storage and analytics
OLTP ERP CRM LOB
Data Warehouse
Business
Intelligence
Data Lake
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101010111001010100001
011111011010
0011110010110010110
0100011000010
Devices Web Sensors Social
Catalog
Machine
Learning
DW Queries Big data
processing
Interactive Real-time

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Amazon Lake Formation (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

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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

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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

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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

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

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

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“Sales for office supplies in APAC
was 15% above expected.”
Amazon QuickSight—ML Insights
Example: anomaly detection

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“SMB Segment was the top
contributor.”
Amazon QuickSight—ML Insights
Example: anomaly detection

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“It’s significant because SMB
typically only accounts for 30% of
sales.”
Amazon QuickSight—ML Insights
Example: anomaly detection

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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.

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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.
Amazon QuickSight—ML Insights
Auto-narratives

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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

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Amazon Managed Streaming for Kafka (preview)
Secure, highly available, fully managed Apache Kafka service
Provisions Apache Kafka and Zookeeper nodes, and manages upgrades for you
Apache Kafka
compatible
Fully managed Highly available Highly secure
Migrate and run existing
Apache Kafka applications to
AWS with no code changes
Manages the provisioning,
configuration, scaling, and
maintenance of Apache Kafka
clusters and Apache
Zookeeper nodes
Replicates data across
three AZs within
an AWS region;
automatically replaces
unhealthy nodes
Multiple levels of security:
network isolation using
Amazon VPC, data
encryption at rest

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Finding missing children with machine learning
• Thorn is a global nonprofit organization fighting child
sexual exploitation and child traffickers.
• Thorn and AWS-partner, MemSQL, built an age
progressed facial recognition service using Deep
Learning and C5 instances.
• Thorn can apply 5,000 data points to a single face
and make a positive match in 200 milliseconds,
compared to 20 minutes previously.
• This tool is used by 5,300 officers in over 18 countries
and identifies an average of 5 kids per day.

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Amazon EC2 C5n instance

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Amazon Elastic Inference

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Amazon Elastic Inference

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Amazon Forecast (Preview)
• Automate the creation of highly-accurate time series forecasts using the
same machine learning technology as Amazon.com
• Visualise forecasts in the console, export as a CSV or access via the API
• Encrypt your data at rest using AWS Key Management Service, and
control access using AWS Identity and Access Management
• No machine learning experience required, and no servers or models to
build, manage, train or deploy. Pay only for what you use

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Amazon Personalize (Preview)

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AWS SageMaker Ground Truth

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Amazon Textract (Preview)
• Uses machine learning to identify contents of fields in forms and tables
• Automate document workflows – “read” millions of documents in hours
• Example use cases:
• Build smart search indexes for medical claims or loan processing
• Create automated approval workflows
• Maintain compliance by identifying information to redact

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Amazon DeepRacer (Preview)
HD video
camera
Dual-core Intel
processor
Four-wheel drive
Dual power for
compute and drive
Accelerometer
Gyroscope

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Amazon DeepRacer (Preview)

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AWS Inferentia (Coming Soon)

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Other announcements
• Amazon SageMaker RL
• quickly and easily develop reinforcement learning models at scale
• Amazon SageMaker Neo
• train models once, and run them anywhere with up to 2x improvement in performance
• Amazon Comprehend Medical
• extract information from unstructured medical texts, quickly and accurately
• Amazon Translate
• customise Amazon Translate output to use company- and domain-specific vocabulary

43. Thank you!
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