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© 2023, Amazon Web Services.
Scott Hewitt
AWS Solutions Architect
re:Cap – non-GenAI Highlights
© 2023, Amazon Web Services.
© 2023, Amazon Web Services.
Compute and Storage
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
© 2023, Amazon Web Services.
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)
Up to 1.64x performance
Improvement
Up to 4.0x performance
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
© 2023, Amazon Web Services.
© 2023, Amazon Web Services.
Database and Analytics
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
© 2023, Amazon Web Services.
Networking and Security
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
© 2023, Amazon Web Services.
Cloud Operations
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
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
© 2023, Amazon Web Services.
Thank you
© 2023, Amazon Web Services.

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AWS reInvent 2023 recaps from Chicago AWS user group

  • 1. © 2023, Amazon Web Services. Scott Hewitt AWS Solutions Architect re:Cap – non-GenAI Highlights
  • 2. © 2023, Amazon Web Services. © 2023, Amazon Web Services. Compute and Storage
  • 3. © 2023, Amazon Web Services. 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
  • 4. © 2023, Amazon Web Services. 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
  • 5. © 2023, Amazon Web Services. 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
  • 6. © 2023, Amazon Web Services.
  • 7. © 2023, Amazon Web Services. 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) Up to 1.64x performance Improvement Up to 4.0x performance 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
  • 8. © 2023, Amazon Web Services. © 2023, Amazon Web Services. Database and Analytics
  • 9. © 2023, Amazon Web Services. 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
  • 10. © 2023, Amazon Web Services. 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
  • 11. © 2023, Amazon Web Services. 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
  • 12. © 2023, Amazon Web Services. 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
  • 13. © 2023, Amazon Web Services. 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
  • 14. © 2023, Amazon Web Services. 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
  • 15. © 2023, Amazon Web Services. © 2023, Amazon Web Services. Networking and Security
  • 16. © 2023, Amazon Web Services. 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
  • 17. © 2023, Amazon Web Services. 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
  • 18. © 2023, Amazon Web Services. © 2023, Amazon Web Services. Cloud Operations
  • 19. © 2023, Amazon Web Services. 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
  • 20. © 2023, Amazon Web Services. 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
  • 21. © 2023, Amazon Web Services. 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
  • 22. © 2023, Amazon Web Services. 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
  • 23. © 2023, Amazon Web Services. Thank you © 2023, Amazon Web Services.