The accelerated pace of innovation in cloud computing is one of its major benefits, but also one of its challenges. New features and services are released every other day, making new technology businesses possible and old ones more efficient. Understanding the innovations and the "state of the union" of the cloud helps builders to leverage new services, tools and ideas.

1. Innovations And Trends In Cloud
Javier Ramirez
Tech Evangelist at Amazon Web Services
@supercoco9
SEPTEMBER 5TH-6TH, 2019
PORTO

2. Who are Builders?I.T.
OPS
DATA
SCIENTISTS
DATA
PROFESSIONALS
CxO
BUSINESS
PEOPLE
DEVELOPERS

3. More than inputs / outputs
More than web content
Services and API-based
APPLICATION FOUNDATIONS APPLICATION CONTEXT APPLICATION INTEGRATION
All data is relevant
Connected everything
Programmable everything
Responsive design
Smart connectivity
Notifications / push for all apps
The “Application” is continuously evolving

4. APPLICATION CONTEXT
DEEP USER
EXPERIENCES
FULLY-AWARE
ALWAYS ON
APPLICATION FOUNDATIONS
CONTINUOUSLY
DELIVERED
FAST TTM
FAST FEEDBACK
APPLICATION INTEGRATION
CUSTOMER
SATISFACTION
CUSTOMER RETENTION
DIFFERENTIATION

5. RE-HOST RE-PROVISION RE-ARCHITECT
Businesses Run on Cloud
“Lift and Shift”
AWS Migration Hub
VMware on AWS
”Pick and Choose”
Fully-managed services
SaaS
“Rewrite and decouple”
Microservices
Serverless
QUICK WINS CLOUD BACK OFFICE MODERNIZE LEGACY
RE-PLATFORM
“Lift and Reshape”
AWS Database Migration Service
AWS Marketplace
EASY OPTIMIZATIONS

7. NOT JUST ARCHITECTED
Well-Architected
WA

8. AWS
Well-Architected
Framework
O P E R A T I O N A L
E X C E L L E N C E S E C U R I T Y R E L I A B I L I T Y
P E R F O R M A N C E
E F F I C I E N C Y
C O S T
O P T I M I Z A T I O N

9. AWS
Well - Arc hitec ted
Framework
P r i n c i p l e s
Stop guessing capacity needs
Test systems at production scale
Automate to ease architectural experimentation
Allow for evolutionary architectures
Drive your architecture using data
Improve through Game Days

10. Principles of Security and Compliance
WELL-
ARCHITECTED
SECURITY
IDENTITY AND
ACCESS
MANAGEMENT
DETECTIVE
CONTROLS
INFRASTRUCTURE
PROTECTION
DATA
PROTECTION
INCIDENT
RESPONSE

11. Well-Architected
Security Best Practices
Implement a strong identity foundation
Enable traceability
Automate security best practices
Protect data in transit and rest
Prepare for security events
Apply security at all layers

12. Dance like no one is watching.
Encrypt like everyone is.

13. Load
Balancer
ENCRYPTED IN
TRANSIT…
Amazon Redshift
Amazon Glacier
RDS
S3
EBS
…AND AT REST
Fully managed
keys in KMS
Yo u r K M I
Imported keys
Fully
auditable
E B S
A W S
C l o u d Tr a i l
Restricted
access
Ubiquitous Encryption
WELL-
ARCHITECTED
SECURITY
E F S

14. S e c u r i t y i s
e v e r y o n e ’ s j o b

15. T H E R E A L W O R L D O F
M a c h i n e L e a r n i n g

16. Smarter Apps

17. S m a r t e r a p p s

21. RETAIL
Demand Forecasting
Vendor Lead Time Prediction
Pricing
Packaging
Substitute Prediction
CUSTOMERS
Recommendation
Product Search
Product Ads
Shopping Advice
Customer Problem Detection
SELLER
Fraud Detection
Predictive Help
Seller Search & Crawling
CATALOGUE
Browse-Node Classification
Meta-data Validation
Review Analysis
Product Matching
TEXT
In-Book Search
Named-entity Extraction
Summarization/X-ray
Plagiarism Detection
IMAGES
Visual Search
Product Image Enhancement
Brand Tracking
THOUSANDS OF EMPLOYEES ACROSS THE COMPANY FOCUSED ON AI
Machine Learning at Amazon.com

22. COUNTERFEIT GOODS DETECTION
CUSTOMER ADOPTION MODELS
ITEM CLASSIFICATION
SALES LEAD RANKING
SEARCH INTENT
DEMAND ESTIMATION
CUSTOMER SUPPORT
DISPLAY ADS
The spark for hundreds of new smart
applications within Amazon.com

23. Tens Of Thousands Of Customers Running Machine
Learning On AWS

24. 81%
of all Deep Learning
workloads run
on AWS
250%
growth YoY
Tens of thousands
of active developers
running ML on AWS
TENSORFLOW ON AWS
Nucleus research
Nuclus Research. S 180 N O V E M B E R 2 0 1 8
85%
of all Tensorflow workloads
run
on AWS

25. Put machine learning in the
hands of every developer and
data scientist
M L @ A W S
O U R M I S S I O N

26. A P P L I C AT I O N S E R V I C E S
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X
P L AT F O R M S
A M A Z O N
S A G E M A K E R
F R A M E W O R K S
KERAS

27. F R A M E W O R K S
Complete control over the entire stack
KERAS

28. A P P L I C AT I O N S E R V I C E S
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X
P L AT F O R M S
A M A Z O N
S A G E M A K E R
F R A M E W O R K S
KERAS

29. Amazon SageMaker
Pre-built notebooks
for common
problems
Built-in, high
performance
algorithms
T R A I N D EPLOY
B U IL D

30. Amazon SageMaker
Pre-built notebooks
for common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
D EPLOY
B U IL D T R A I N

31. Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built notebooks
for common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
B U IL D T R A I N D EPLOY

32. Collect and
prepare
training data
Choose and
optimize
your ML
algorithm
Set up and
manage
environments for
training
Train and tune
model
(trial and error)
Deploy model
in production
Scale and
manage the
production
environment
Amazon SageMaker

33. Put machine learning in the
hands of every developer and
data scientist
M L @ A W S
O U R M I S S I O N

34. P L AT F O R M S
A M A Z O N
S A G E M A K E R
A P P L I C AT I O N S E R V I C E S
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X
F R A M E W O R K SF R A M E W O R K S
KERAS

35. F R A M E W O R K S
KERAS
P L AT F O R M S
A M A Z O N
S A G E M A K E R
A P P L I C AT I O N S E R V I C E S
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X

36. B R I N G I N G C L O U D S C A L E T O
D a t a b a s e s

37. Migrate between on -
prem and AWS
Migrate between
databases
Automated schema
conversion
Data replication for zero
downtime
AWS Database Migration Service
1 6 0 , 0 0 0 + u n i q u e d a t a b a s e s
m i g r a t e d u s i n g D M S

38. A m a z o n A u r o r a
F a s t e s t - g r o w i n g
s e r v i c e i n AW S
h i s t o r y
MySQL and PostgreSQL compatible
Several times faster than standard
MySQL and PostgreSQL
Highly available and durable
1/10th the cost of commercial
grade database

39. Amazon Aurora: The Fastest-Growing Service In AWS History

40. DATABASE
REQUESTS
TIME
Unpredictable workloads are challenging

41. No need to provision
instances
Automatically scales
capacity up and down
Starts up on demand
and shuts down when
not in use
Pay per second and only
for the database
capacity you use
Amazon Aurora Serverless
ON-DEMAND, AUTO-SCALING DATABASE FOR APPLICATIONS WITH UNPREDICTABLE OR CYCLICAL WORKLOADS

42. of what we consider
analytics is not analytics
80%
VALUE
WORK 20%
80%

43. INDEXING
DATA
ACQUISITION
DISCOVERY
SECURITY
STORAGE
GOVERNANCE
ACCESS
of what we consider
analytics is not analytics
80%

44. 10
9
8
7
6 5
4
3
2
1 1. Automated And Reliable Data Ingestion
2. Preservation Of Original Source Data
3. Lifecycle Management And Cold Storage
4. Metadata Capture
5. Managing Governance, Security, Privacy
6. Self-Service Discovery, Search, Access
7. Managing Data Quality
8. Preparing For Analytics
9. Orchestration And Job Scheduling
10. Capturing Data Change
T h e m o d e r n d a t a
a r c h i t e c t u r e i s c o m p l e x

45. W h a t d o e s t h e m o d e r n d a t a
a r c h i t e c t u r e l o o k l i k e o n AW S ?

46. A u t o m a t e d I n g e s t i o n
S3
Upload
AWS Storage
Gateway
Kinesis Snowball
Snowball Edge
Snowmobile
DynamoDB
Streams

47. P r e s e r v a t i o n o f
o r i g i n a l s o u r c e d a t a
S3 EFS EBS DynamoDB
RDS Aurora Redshift

48. Data Lake with multiple analytical engines
AWS GLUE
+
AWS LAKE
FORMATION
Amazon Redshift
+ R e d s h i f t S p e c t r u m
Amazon
QuickSight
Amazon EMR
H a d o o p , S p a r k ,
P r e s t o , P i g ,
H i v e … 1 9 t o t a l
Amazon
Athena
Amazon
Kinesis
Amazon
Elasticsearch
Service
S t o r a g e
N o n - r e l a t i o n a l D a t a b a s e s
A U R O R A C O M M E R C I A L C O M M U N I T Y
R e l a t i o n a l D a t a b a s e s
A m a z o n
D y n a m o D B
A m a z o n
E l a s t i C a c h e
A n a l yt i c s
Amazon
CloudSearch
AWS Data
Pipeline
M a c h i n e
L e a r n i n g
A m a z o n
S 3
A m a z o n
E F S
A m a z o n
E B S
A m a z o n
G l a c i e r
O n - P r e m i s e
D a t a b a s e s

49. Shifting analytics
80%
analytics
20%
prep

50. AWS Customer Success With Analytics

51. C o m p u t e

52. Compute - From server to serverless
ContainersVirtual
Machines
Serverless

53. Compute - From server to serverless
Virtual
Machines
EMULATES PHYSICAL
INFRASTRUCTURE
GREAT FOR RUNNING
EXISTING SOFTWARE
LARGE EXISTING
FOOTPRINT
VIRTUAL
MACHINES

54. Broadest Spectrum Of Compute Instances
EC2 Elastic GPUs
• Graphics acceleration for EC2
instances
EC2 Fleet
• Simplified provisioning
• Massive scale
• Flexible capacity allocation
T 3
Big Data
Optimized
H 1
Memory
Optimized
R 5
In-
memory
X 1
High
I/O
I 3
Compute
Intensive
C 5
Graphics
Intensive
G 3
General
Purpose
GPU
P 3
Memory
Intensive
X 1 e
General
Purpose
M 5
Bare Metal
High I/O
I 3 m
Dense
Storage
D 2 F 1
FPGAV i r t u a l
P r i v a t e
S e r v e r s
Amazon
Lightsail z 1 d
Compute and Memory
Intensive
R 5 m z 1 d m
Burstable

55. From server to serverless
Containers
EVEN MORE COMPUTE
EFFICIENCIES
FAST STARTUP AND
TEAR DOWN
DEVELOPER AGILITY
CONTAINERS

56. ECS
Highly scalable, high performance
container management system
A Managed Platform
Cluster Management Container Orchestration Deep AWS integration

57. Customers Are Doing Amazing Things In Production With Containers

58. “Run K ubernetes for me”

59. E K S
Platform for
enterprises to run
production-grade
Kubernetes
Managed and
consistent experience
Seamless, native
integration
with AWS services
Built with the
OSS community
Upstream and
Certified

60. B u i l d e r s w a n t t o b u i l d ,
n o t m a n a g e c l u s t e r s

61. No infrastructure
Manage everything at the container level
Launch easily, scale quickly
Resource-based pricing

62. From server to serverless
Serverless
EXTREME DEVELOPER
PRODUCTIVITY
COMPLETE
ELIMINATION OF
INFRA CONCERNS
FOCUS MORE ON
BUSINESS LOGIC
SERVERLESS

63. Builders only want to
build business logic.

64. COMMUNICATIONS
Serverless
application
architectures
on AWS
A m a z o n
K i n e s i s
A m a z o n
S N S
A m a z o n
S Q S
COMPUTE
COORDINATION
SYSTEMS
DEVELOPMENT
NETWORK
DATA
IDENTITY
MONITORING
& AUDITING
DEVELOPMENT
& DEPLOYMENT
A m a z o n
C l o u d F r o n t
A m a z o n
S 3
A m a z o n
D y n a m o D B
A m a z o n
A u r o r a
S e r v e r l e s s
A m a z o n
C o g n i t o
A m a z o n
C o g n i t o
U s e r p o o l s
A W S
C l o u d Tr a i l
A W S X - R a y
A m a z o n
C l o u d W a t c h
A W S
L a m b d a
A W S
S t e p F u n c t i o n s
M o b i l e
A W S I o T
A W S
A L B
A m a z o n
A P I
G a t e w a y
A W S
C o d e P i p e l i n e
A W S
C o d e B u i l d
A W S C o d e C o m m i t
A W S I A M
A W S C l o u d 9
A W S C o d e S t a r

65. No server management
Flexible scaling
High availability
No idle capacity
B e n e f i t s o f
S e r v e r l e s s
A p p l i c a t i o n s

66. Some Of The Customers Running Serverless On AWS

67. Compute - From server to serverless
ContainersVirtual
Machines
Serverless

68. Self-service
Well-architected, with a focus on security
Powered by Machine Learning
Choosing managed databases
Automated data ingestion to a data lake used by multiple analytic engines
Serverless
S u m m a r y : Tr e n d s a n d I n n o v a t i o n s

69. Obrigado
Javier Ramirez
Tech Evangelist at Amazon Web Services
@supercoco9

70. Innovations And Trends In Cloud
Javier Ramirez
Tech Evangelist at Amazon Web Services
@supercoco9
SEPTEMBER 5TH-6TH, 2019
PORTO