AWS reInvent 2023 recaps from Chicago AWS user group

© 2023, Amazon Web Services.
Scott Hewitt
AWS Solutions Architect
re:Cap – non-GenAI Highlights

Compute and Storage

Amazon EC2 r8g Instance (with Graviton4)
N E X T G E N E R A T I O N O F S T A N D A R D R A M F O C U S E D I N S T A N C E S W I T H N E W E S T G R A V I T O N P R O C E S S O R
Who should use? Anyone using r7g and
anyone running databases or caches on EC2
Preview
R8g
Powered by Graviton4 Processors
Up to 3x more vCPU and memory than R7g
Dedicated caches for every vCPU
Generational Performance Gains
30% faster for web apps
40% better performance for databases
45% faster for large Java applications
Security Capabilities
Always-on memory encryption
Support for pointer authentication
Support for encrypted Amazon Elastic Block Store

Amazon WorkSpaces Thin Client
M A N A G E D V D I H A R D W A R E O F F E R I N G A W S M A N A G E D S T O R A G E , H O S T , A N D C O N T R O L P L A N E A D M I N I S T R A T I O N
4.
Admin manages the registered
thin clients through AWS Console
3.
End user connects peripherals
and logs into virtual desktop
2.
Amazon ships directly to the
employee or office
1.
Purchase WorkSpaces Thin Client
through Amazon Business
Reduce end-user computing cost
Low-cost device starts at $195 USD
Ships directly from Amazon to users
Simplifies logistics
Built for AWS virtual desktop services
Works seamlessly with WorkSpaces,
WorkSpace Web, and AppStream 2.0
Set up in minutes
Pre-configured for simple end-user deployment
ü Buy: AWS Microsoft Office bundles
ü Bring: BYOL Microsoft 365 Apps for enterprise
Microsoft licensing flexibility
Available on www.amazon.com

Amazon EFS Archive Storage Class
A U T O M A T I C P E R - F I L E C O S T O P T I M I Z A T I O N I N A S I N G L E F I L E S Y S T E M
Infrequent Access (IA)
Standard
Fastest performance for
active data
Archive
Cost-optimized for inactive
data (a few times a quarter)
Cost-optimized for cold data
(a few times a year or less)
Tier back to Standard upon access
Tier
inactive data
Tier
cold data
30c/GB-mo
Sub-millisecond
latencies
0.8c/GB-mo
50% lower cost
than IA
1.6c/GB-mo
95% lower cost
than Standard

Amazon S3 Express One Zone
O B J E C T S T O R A G E F O R D A T A P R O C E S S I N G A N D C R I T I C A L A P P S U S I N G S 3 D A T A T H A T C A N B E R E C R E A T E D
Single-digit millisecond latency
One zone S3 storage class that delivers the
fastest data access speed and highest
performance of any cloud object storage for
customers’ most latency-sensitive applications
Most frequently accessed data
Designed for request intensive applications –
ML training and inference, interactive analytics,
media content creation
10x faster + 50% lower request costs
Data access speeds up to 10x faster, and request costs
up to 50% lower than S3 Standard. Fully elastic with no
storage provisioning and no prefix level limits (hundreds
of thousands of TPS)
root
b
a c
d
obj1
obj2
obj3
root/a/obj1.txt
root/b/d/obj2.txt
root/c/obj3.txt
Enables file system-like performance
on S3 using directory buckets
Amazon Athena
(SQL)
Up to 2.1x performance
Improvement
Amazon SageMaker
(File Mode)
Improvement
Improvement
Amazon EMR on EC2
(Spark)
For compute-intensive workloads
A faster authentication and access model to a
single zone creates a compute-optimized
consumptionn model
Ready for Partner
Integration

Database and Analytics

Amazon Aurora Limitless Database
M A N A G E D H O R I Z O N T A L S C A L E - O U T B E Y O N D T H E L I M I T S O F A S I N G L E I N S T A N C E
Preview
Aurora DB Shard Group
Transaction Routers
Shards
Aurora DB Cluster Shared Storage
Shard Group Endpoint
AZ1 AZ2 AZ3
Automatically scale to millions of transaction per second
Sharding of data across multiple writers with a single endpoint
Manage petabytes of data in a single database
Serverless and fast scaling
Automatically routes queries to the correct shard
Lightweight routing layer design enables quick scaling
Transactional consistency across all shards
Runs across multiple AZs to provide high availability
Orchestrate complex queries across multiple shards
Automatic repartitioning of data
Resharding transparent to applications

Amazon ElastiCache Serverless
Build highly responsive applications
Median latency of 500μs (p50)
Cache petabytes of data in a single system
Resilient and scalable
Tail latencies of 1.2ms (p50)
Lightweight routing layer design enables instant scaling
Runs across multiple AZs to provide high availability
No capacity management
Single endpoint abstracting underlying cluster topology
Single Cache
Endpoint
Amazon
ElastiCache
Serverless
Shards
Distributed,
Vertically Scaling
Cache Nodes
S E R V E R L E S S C A C H E T H A T I N S T A N T L Y S C A L E S T O S U P P O R T T H E M O S T D E M A N D I N G A P P L I C A T I O N S
Request Routing Layer
99.99% high availability for both Redis and Memcached engines
Create a highly available cache in < 1 minute
Supports up to 5TB of memory capacity

Vector Database Support on AWS
B U I L D I N G G E N A I A P P L I C A T I O N S O F T E N R E Q U I R E S V E C T O R S E A R C H A N D A W S I S E X T E N D I N G S U P P O R T
Redis
Enterprise
Cloud
Vector Engine
For OpenSearch
Serverless
C O M I N G S O O N
MongoDB
Pinecone Amazon
Aurora
C O M I N G S O O N
Cat
Kitten Old
Feline
Puppy Dog
Young
Canine
Vector Database Direct Integration for Amazon Bedrock
Vector Support for Amazon Databases
Embeddings encode all data types into vectors
that capture meaning and context of an asset.
Many Generative AI models depend on reading
embeddings data from a vector database
Vectors are critical for customizing generative
AI applications
DynamoDB DocumentDB MemoryDB
for Redis
Amazon RDS
PostgreSQL
Aurora
PostgreSQL
OpenSearch
Amazon
Neptune
NEW NEW
Preview

Amazon Redshift Serverless AI-Scaling
M L - P O W E R E D F O R E C A S T I N G A N D A N A L Y S I S T O O P T I M I Z E P E R F O R M A N C E , T H R O U G H P U T , A N D C O S T
Preview
SELECT v.venuename, v.venueid,
SUM ( s.qtysold * s.pricepaid ) AS total_sales
FROM sales s
JOIN event e ON s.eventid = e.eventid
JOIN venue v ON e.eventid = v.eventid
GROUP BY v.venuename, v.venueid
ORDER BY total_sales
DESC LIMIT 3
QUERY
Number of Rows in
Dataset/Column
Operation
Types
Selectivity of
Predicates
Only speedup
if it’s free
I’m willing to pay
slightly more for
more performance
I strongly favor
performance over cost
Analyzes data volume, concurrent users, and query complexity
AI learns from resource requirements of past queries
Price-performance slider to optimize for your workload
Improves consistency, throughput, and cost
Automatically adjusts capacity based on workload needs
10x better price performance for variable workloads

Zero-ETL Integration with Amazon Redshift
S T R E A M L I N E U S I N G T R A N S A C T I O N A L D A T A F O R A N A L Y T I C S A N D M A C H I N E L E A R N I N G W I T H O U T E T L
and
machine learning on transactional data
pipelines
from
multiple operational databases
Operational
databases
Amazon
Redshift
ML and
analytics
services
BI and
analytics
apps
RDS MySQL Preview
Preview
Aurora PostgreSQL Preview
DynamoDB
Aurora MySQL

AWS Glue Data Quality Anomaly Detection and Dynamic Rules
A D D I T I O N A L G L U E D Q R U L E C A P A B I L I T I E S F O R A U T O M A T E D D A T A Q U A L I T Y R U L E C R E A T I O N
Preview
Dynamic Rules with auto-adjusting
thresholds and Anomaly
Detection Rules that learn patterns
and spot deviations
1 2
3
1. Add Evaluate Data Quality node to
your Glue Studio Workflow.
2. Select which statistics and columns
you want to evaluate.
3. Review the patterns and
observations from the Quality tab.
4. Evaluate the generated rules code
to implement. Here are 2.
Sets adaptive thresholds that check
the row count is between the smallest
of the last 10 runs and the largest of
the last 20 runs.
Looks for unusual patters, for
example RowCount being abnormally
high on weekends:
4

Networking and Security

IAM Access Analyzer Simplifies Inspecting Unused Access
E A S I L Y S C A N Y O U R A C C O U N T S F O R U N U S E D A C C E S S A N D C R E A T E A R E P O R T T O H E L P M I N I M I Z E R I S K S
Use IAM Access Analyzer to
create an “Unused Access
Analysis” Analyzer
Group findings by account and
number of findings
Also available in the command
line and IAM Access Analyzer API

Application Load Balancer Security & Resilience
O P T I O N S F O R P E R F O R M I N G C L I E N T A U T H E N T I C A T I O N A N D O P T I M I Z I N G T A R G E T G R O U P W E I G H T I N G
x.509 Mutual TLS Automatic Target Weights

Cloud Operations

Amazon CloudWatch Logs Infrequent Access
T A I L O R E D S E T O F C A P A B I L I T I E S A T A L O W E R C O S T F O R I N F R E Q U E N T L Y A C C E S S E D L O G S
cost ingestion
cross-account log analytics
• Consolidate
operational overhead

CloudWatch Log Insights Query Assist
Q U I C K L Y A N D E A S I L Y G E N E R A T E Q U E R I E S O F Y O U R L O G S A N D M E T R I C S D A T A U S I N G P L A I N L A N G U A G E
Generate new queries from a
description or a question to
help you get started easily
Query explanation to help
you learn the language
including more advanced
features
Refine existing queries using
guided iterations

Cost Optimization Hub
C O N S O L I D A T E A N D P R I O R I T I Z E C O S T O P T I M I Z A T I O N R E C O M M E N D A T I O N S A C R O S S Y O U R A W S
O R G A N I Z A T I O N S M E M B E R A C C O U N T S A N D A W S R E G I O N S

Cost and Usage Dashboard
Q U I C K L Y D E P L O Y A C O S T A N D U S A G E D A S H B O A R D P O W E R E D B Y A M A Z O N Q U I C K S I G H T
Easy Set Up
No need to maintain underlying
infrastructure, such as Amazon
Athena views or AWS Glue crawlers
Over 100 Visuals
Get a high-level overview of your
AWS spend and dive deeper into
individual AWS services
Customizable
Create your own visuals and
dashboards, combine AWS data with
third party data sources, or provide
stakeholders access to cross sections
of data

Thank you

AWS reInvent 2023 recaps from Chicago AWS user group

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