Bluesoft @ AWS re:Invent 2017 + AWS 101

AWS re:INVENT 2017
@andrefaria

6º Reinvent
A Bluesoft esteve presente em todos eles!

13.000
Sessions
43.000+
attendees

Cloud Computing is on-demand delivery
of compute power, database storage,
applications and other IT resources
through a cloud services platform via the
Internet with pay-as-you-go pricing.

no upfront investments
no hardware management
low cost
scalable
capex vs opex
no capacity guessing
increased speed and agility
focus on core activities

IaaS - Infrastructure
networking, computers, storage (EC2)
PaaS - Platform
management layer, patching (RDS)
SaaS - Software
end user apps (Amazon WorkDocs)

Global Infrastructure
more than 1M customers in 190 countries
low latency and higher throughput
42 AZs - 16 regions

AWS Management Console
Simple and Intuitive User Interface

AWS Command Line Interface (CLI) AWS SDKs

EC2 - Amazon Elastic Compute Cloud
On-Demand
pay by the hour
Reserved
up to 75% for upfront payment
Spot
bid on spare capacity

ECS - Ec2 Container Service
ECS
Container Management
Run Containers Clusters on EC2 instances
ECR
Container Registry
Store, Manage, and Deploy Containers

Compute Services
AWS Batch
plan, schedule, and run batch
computing jobs on AWS - it
automatically provision resources
(cpu, memory, spot, etc.) no need to
manage servers clusters to run your
jobs
Amazon Lightsail
laugh virtual private servers

Compute Services
AWS Lambda
run code without provisioning or
maintaining servers, pay only for
compute time you consume
Elastic Beanstalk
run Java, .NET, PHP, Node.js,
Python, Ruby, Go, and Docker
on familiar servers such as
Apache, Nginx, Tomcat,
Passenger, and Internet
Information Services (IIS).

Compute and Storage Services
Amazon S3 and Glacier
S3 is an object storage designed
to have 99.999999999% durability
Glacier is a extremely low cost
storage for archiving and long-
term backup.
AWS Autoscaling
Ensure that your are running
the desired number of EC2
Instances, and increases
instances if demand increases

Storage Services
Elastic File System
Simple scalable ﬁle storage to
use with EC2. Storage is
elastic and can grow or shrink
automatically as you add and
remove ﬁles.
AWS EBS
Persistent block storage
volumes to use with EC2,
automatically replicated within
the AZ. Best for low latency
storage.

Storage and Database Services
Aurora
MySQL and Postgres compatible
relational database that
combines speed an availability
of high-end comercial databases
for 1/10 of the cost.
AWS Storage Gateway
Hybrid Storage between on
premisses and cloud

Database Services
Amazon Dynamo DB
fast and ﬂexible NoSQL DB for
consistent, single-digit
millisecond latency at any scale,
support both document and key-
value storage.
Amazon RDS
Managed Aurora, PostgreSQL,
MySQL, MariaDB, Oracle,
Microsoft SQL Server.

Database and Networking Services
Amazon VPC
provision a logically isolated
section for the cloud to launch
resources (ip ranges, subnets,
routes, route tables).
Amazon ElastiCache
Managed InMemory Database.
Supports Redis and Memcached.

Networking Services
Route 53
Highly available and scalable
DNS Web Service - translates
domains in IPS addresses
AWS Cloud Front
Global Content Delivery Network
(CDN) that accelerates delivery for
websites, APIs, video, or other
assets, routing automatically to
the nearest edge location.

Networking and Developer Services
AWS Code Commit
Fully Managed Source Control
Service to host private Git
Repos.
Elastic Load Balancing
ELB automatically distributes
incoming application trafﬁc
across multiple EC2 instances
enabling fault-tolerance and
scaling.

Developer Services
AWS Code Deploy
Automates code deployments to
any instance
AWS Code Build
Fully Managed Build Services that
compile source code, run tests
and produces software packages
that ready to deploy

Developer Services
AWS X-Ray
analyse and debug apps with
end-to-end view of requests and
a map of components.
AWS Code Pipeline
Continuous Integration and
Continuous Delivery

Management Tools
AWS Systems Manager
collect inventory, apply patches,
create images, conﬁgure and
run commands.
Amazon CloudWatch
Monitoring Resources and Apps

Management Tools
AWS Cloud Trail
records API calls for your
account and delivers log ﬁles
AWS Cloud Formation
Create and manage a collection of
AWS resources, providing and
updating

Management Tools
AWS OpsWorks
Configuration Management
Service that uses Chef or
Puppet to automate how servers
are configured, deposed and
manger across EC2 instances.
AWS Config
Full Managed service that
provides an AWS resource
inventory, config history, config
change notifications and rules
evaluations

Management Tools
AWS Services Catalog
Create and manage catalogs of
IT services that are approved for
use on AWS
AWS Config
Full Managed service that
provides an AWS resource
inventory, config history, config
change notifications and rules
evaluations

Management Tools
Amazon Inspector
Amazon Inspector automatically
assesses applications for
vulnerabilities or deviations
from best practices.
AWS IAM
control access to AWS services
and resources for your users

Management and Analytics Tools
Amazon Athena
is an serverless interactive
query service that makes it easy
to analyze data in Amazon S3
using standard SQL. 94 Athena
is serverless. You pay only for
the queries that you run.
AWS IAM
control access to AWS services
and resources for your users

Analytics Tools
Amazon CloudSearch
managed search solution for
websites and applications
Amazon Elasticsearch
managed Elasticsearch
Amazon EMR
managed service to run Hadoop,
Spark, HBase, Presto, and Flink
workloads that in a easy, fast, and
cost-effective fashion to process
vast amounts of data across
dynamically scalable Amazon EC2
instances.

Kinesis
Kinesis Firehose
capture, transforms and load
streaming data into s3, redshift,
kinesis analytics for real time
analytics
Kinesis Analytics
process streaming data in real
time with standard SQL without
having to learn new languages
or processing frameworks - run
queries continuously
Kinesis
platform for collecting, storing
and analysing streaming data -
you can load terrabytes of data
per hour from IoT devices, mobile
apps, etc. Kinesis offers 3
services.
Kinesis Streams
continuously capture and store
treats of data per hour from
thousands for sources

Analytics
Amazon QuickSight
cloud business analytics service
that makes it easy to build
visualizations, perform ad-hoc
analysis, and quickly get
business insights.
Amazon Redshift
fast, fully managed, petabyte -
scale data warehouse that makes
it simple and cost - effective to
analyze all your data using your
existing business intelligence
tools.

ETL
AWS Glue
fully managed ELT service that
makes it easy to move data
between data stores.
Disponibiliza um serviço ETL
gerenciado, executado em um
ambiente Apache Spark sem
servidor.
Para não Spark, Hive, Pig, etc.
usar Data Pipeline
Amazon Data Pipeline
Move data between different AWS
compute and storage services.
Access, Transform and Process at
Scale and Store Results.
Serviço de orquestração com
ﬂexibilidade de ambiente de
execução, do acesso e do
controle sobre os recursos que
executam código, bem como
sobre o próprio código
responsável pelo processamento
dos dados.

IA
Amazon Polly
turns text into Speech
Amazon Rekognition
image analysis
Amazon LEX
building conversational interfaces
into any application using voice
and text

IA and Mobile
AWS Mobile Hub
quickly create and conﬁgure
mobile app backends features
and integrate them to the mobile
app
Amazon Cognito
serverless identity service
Amazon Pinpoint
target campaigns to drive user
engagement (e-mails, SMS, push
notiﬁcations)
Machine Learning
makes it easy for developers of all
skill levels to use machine
learning technology. Provides
visualisation tools and wizards
that guide you through the
process of creating machine learn
ing models
Amazon Device Farm
test apps on many devices at
once (Android, iOS and web)

Mobile and Application Services
Simple Workﬂow Service
developers build, run, and scale
background jobs that have parallel
or sequential steps (like Step
functions but no visual and more
control of your logic)
Amazon API Gateway
create, publish, maintain and
monitor secure APIs at scale
Mobile Analytics
measure app usage and revenue
AWS Step Functions
coordinate componentes of
distributed applications and micro
services using visual workﬂows

Messaging and App Streaming
Amazon Workspaces
fully managed desktop computing
service
Amazon AppStream 2.0
store your app from AWS to any
device running in a web browser
Amazon SQS
managed queuing service
Amazon SNS
push notiﬁcation service
Amazon SES
send e-mails

IoT
AWS IoT
connect devices to AWS
AWS Greengrass
run local compute, lambda,
messaging, caching, syncing for
connected devices
AWS ioT Button
programable button (Dash alike)

$18B+ revenue run rate
42% growth rate
AWS 44% of the public cloud marketshare
(more than all other competitors combined)
Millions of Active Customers
airbnb, slack, intercom, pinterest, sony, go pro, johson, pﬁzer, GE,
philps, siemens, netﬂix, disney, hbo, discovery, fox, kellogs, coca cola,
samsung, LG
3000 government agencies
8000 academic institutions

Pace of Innovation
2016 1000+ Features
2017 1300+ Features

What builders really want?
6 songs

Voice is next big disruption
it’s a natural interface
how you interact with people?

Security is everyone’s job
Encryption is not only faster and more efﬁcient now.
There’s really no excuse not to encrypt your data.

Dance like no one is watching
Encrypt like everyone is

GuardDuty (network level plus IA)

Product Launch
Amazon Elastic Container Service for Kubernetes (EKS)

Product Launch
AWS Serverless Application Repository
Publish and Find Lambdas to use

AI
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Vision Speech LanguageServices
Platforms
Frameworks
Infrastructure
Amazon ML Spark & EMR Kinesis
Mechanical
Turk
Amazon ECS
Amazon Batch
AWS Deep Learning AMI
Apache
MXNet
TensorFlow
Caffe2
& Caffe
Theano Keras
Cognitive
Toolkit
PyTorch
GPU CPU IoT
(Greengrass)
Mobile
Gluon

AWS DeepLens
A camera fully loaded with onboard compute power optimized for deep learning

Amazon Sagemaker
- prebuilt notebooks that solve common problems in machine learning
- 10 algorithms to address problems
- Import your own if you need a custom solution
- “one-click training” specify the location of your dataset in S3, choose
an instance type to run the computation, and Sagemakers does all
the heavy lifting, setting up the algorithms to run your training.
- “one-click-deploy“ set the instance type and minimum/maximum
numbers for your cluster and Sagemaker then gives you secure
endpoints to connect to your app.

Amazon Rekognition
Amazon Rekognition
Extract rich metadata from visual content
Object and Scene
Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
Celebrity
recognition
Image
moderation
Text in Image

Maple
Villa
Bushes
Grass
Tree
House
Window
Sky
Mountain Range
Forest
Clouds
Object and scene detection makes it easy for you to add features that
search, filter, and curate large image libraries.
Identify objects and scenes and provide confidence scores
DetectLabelsObject & Scene Detection

Demographic Data
Facial Landmarks
Sentiment Expressed
Image Quality
Brightness: 23.6
Sharpness: 99.9
General Attributes
Facial Analysis DetectFaces
Analyze facial characteristics in multiple dimensions
Smiling
99.1%
Female
100%
Mouth Closed
99.5%
Age Range
26 – 43 years old
Crowd Mode

Facial Analysis
Image Quality
Facial Landmarks
Demographic Data Emotion Expressed
General Attributes
Facial Pose
Brightness 23.6%
Sharpness 99.9%
EyeLeft,EyeRight,Nose
RightPupil,LeftPupil
MouthRight,LeftEyeBrowUp
Bounding Box...
Age Range 29-45
Gender:Male 96.5%
Happy 83.8%
Surprised 0.65%
Smile:True 23.6%
EyesOpen:True 99.8%
Beard:True 99.5%
Mustache:True 99.9%...
Pitch 1.446
Roll 5.725
Yaw 4.383
DetectFaces

AWS Rekognition Video
process real-time and batch video to detect objects, people, activities, and more
- detect inappropriate content
- check surveillance footage for missing people
- continually trained, gets “smarter” as more people use it

Amazon Kinesis Video Streams
its real time streaming capabilities for video
- integrates with Rekognition video (as an input source)
- SDK that manufacturers can use to integrate it directly into their devices

http://amzn.to/takeselfie

End-to-End Architecture
SELECT
STREAM COUNT(*) AS MUSTACH_COUNT,
STEP(ROWTIME BY INTERVAL '1' SECOND)
FROM SOURCE_STREAM
WHERE HAS_MUSTACH = TRUE;
Amazon
Kinesis
Stream
Amazon
Kinesis
Analytics
Amazon Cognito
Amazon
Kinesis
Stream
Amazon
DynamoDB
Amazon
Lambda
Amazon
S3
JavaScript
SDK
Amazon
Rekognition
Amazon
Kinesis
Firehose
Amazon
S3

Amazon Rekognition Case Study
W a s h i n g t o n C o u n t y S h e r i f f ’ s O f f i c e
Chris Adzima
Washington County Sheriff’s Office
Chris_adzima@co.washington.or.us

Real-world example
The solution
Real-world example

Unlimited
replays
Returns an MP3
or audio stream
Lightning-fast
response
Fully managed and
low cost
Amazon Polly
Turn text into lifelike speech using deep learning
technologies to synthesize speech that sounds like a human
voice
Potential use cases
Content creation Education and E-learning
Mobile and desktop applications Customer contact center
Internet of Things (IoT) Accessibility

Amazon Lex
Conversational interfaces for your applications, powered by
the same natural language understanding (NLU) and
automatic speech recognition (ASR) models as Alexa
Integrated
development in
AWS console
Trigger AWS
Lambda
functions
Multi-step
conversations
Continually
improving ASR and
NLU models
Enterprise
connectors
Fully managed
Potential use cases
Appointment booking Customer support (Contact Center bots)
Informational services Access enterprise data
Internet of Things (IoT)

Amazon Transcribe
converts speech to text

Amazon Translate
it translates text from one language to another
use batches of text from S3
it boasts real-time translation

Amazon Comprehend
fully managed natural language processing service
Provide data from your lake (S3) via an API
then Comprehend will provide four elements for analysis:
1 Entities – Things like people, dates, and speciﬁc places
2 Key phrases – Comprehend picks out the “most important” sets of words
3 Language – Automatic detection of the language used
4 Sentiment – Is the text saying something positive or negative?

The Alexa Family

“Book a hotel”
Book hotel
NYC
“Book a hotel in
NYC”
Automatic speech
recognition
Hotel booking
New York City
Natural language
understanding
Intent/slot
model
UtterancesHotel booking
City New York City
Check In April 19
Check Out April 21
“Your hotel is booked for
April 19”
Amazon Polly
Confirmation: “Your hotel
is booked for April 19”
“Can I go ahead
with the
booking?”
a
in

Amazon Lex
Utterances
Spoken or typed phrases that invoke
your intent
BookHotel
Intents
An intent performs an action in
response to natural language user
input
Slots
Slots are input data required to fulfill
the intent
Fulfillment
Fulfillment mechanism for your intent

Amazon Lex—create a bot
1
2
Define sample
utterances
Define slots
Create bot

2
3
Confirm
transaction
Fulfill transaction
1
Elicit
information
Interact with bot
Amazon Lex—interact with a bot

Alexa for Business
Alexa for Business is a fully managed service for
Alexa voice-controlled devices at work.
just say, “Alexa, start the meeting.”

MOBILE INDUSTRY TRENDS
Time spent in apps
and growing
1.6T
hours
Source: AppAnnie
New enterprise apps
built with web
technologies
50%+
Source: AWS
JavaScript – most
commonly used
programming
language
66.7%▲
Source: Stack Overflow

THREE SIMPLE STEPS
1. Pick a Platform
Mobile Hub
2. Set Up Cloud Services
Native SDKs
3. Connect Your App
Mobile CLI AWS Amplify

MOBILE CLI: SETTING UP CLOUD SERVICES
> awsmobile init
> awsmobile user-signin enable
Initialize your app
Enable User Sign-in
Supported services:
• user-signin (Amazon Cognito)
• analytics (Amazon Pinpoint)
• database (Amazon DynamoDB)
• user-files (Amazon S3)
• cloud-api (API GW & AWS
Lambda)
Web app deployment support:
• hosting (Amazon S3 and
Amazon CloudFront)

APP DATA: HARD PROBLEMS REMAIN
Data requirements
vary across devices
and become harder
when multiple
users share data
Users want instant
access to data
Building scalable
data-driven apps
without learning
distributed systems
concepts is hard
Users want to
continue using
their apps even
with low or no
connectivity

AWS AppSync

START BUILDING FAST

EASY ACCESS TO RICH DATA
{
"id": "1",
"name": "Get Milk",
"priority": "1"
},
"id": "2",
"name": "Go to gym",
"priority": "5"
},…
type Query {
getTodos: [Todo]
}
type Todo {
id: ID!
name: String
description: String
priority: Int
duedate: String
}
query {
getTodos {
id
name
priority
}
}
Model data with
application schema
Client requests what it
needs
Only that data is
returned

INTEGRATE WITH YOUR APPLICATION

AWS Amplify
AWS Amplify is a JavaScript library for frontend and mobile
developers building cloud-enabled applications.
The library is a declarative interface across different categories
of operations in order to make common tasks easier to add into
your application.
"I was able to use AWS Cognito to integrate a full-featured
authentication system, including email signup veriﬁcation and
MFA, into a React application with less than 10 lines of code, in
about 20 minutes. That time would have been drastically less if I
had already created a user pool beforehand."

Load balancer used to
route incoming requests
to multiple Amazon EC2
instances, containers,
or IP addresses in your
VPC.
ELB
EC2
Instance
EC2
Instance
EC2
Instance

Application Load Balancer Network Load Balancer Classic Load Balancer
Protocol HTTP, HTTPS, HTTP/2 TCP TCP, SSL, HTTP, HTTPS
SSL offloading ✔ ✔
IP as Target ✔ ✔
Path-based routing,
Host-based routing ✔
Static IP ✔
WebSockets ✔ ✔
Container Support ✔ ✔

For TCP in VPC, use Network Load Balancer.
For all other use cases in VPC , use
Application Load Balancer
For Classic networking, use Classic Load
Balancer

Aurora Serverless
Database starts up on demand, shuts down when it’s not in use…
when it is in use, you’re billed by the second.

DynamoDB Global Tables
The ﬁrst fully managed, multi-master, multi-region
database system in the world

DynamoDB On-Demand Backup
create backups (and restore them) with one click or API call

Amazon Neptune
Neptune is a fully managed graph database service
store billions of records will millisecond latency

S3 Select and Glacier Select
Good for Data Lakes
pull out only the data you need using an API to pull only speciﬁc parts of an object

Amazon S3 By The Numbers
44 Availability Zones
(16 more coming in 2018)
16 Regions
(5 more coming in 2018)
Trillions of
objects
Millions of requests
per second
One of first three
AWS Services
(2006)
99.999999999%
Durability

Amazon S3 Availability Zones
S3 stores data in at least 3
Availability Zones (AZ’s)
Each AZ can be up to 8
physical data centers
Unavailability of a data center
or an AZ does not impact
overall S3 availability
Low latency private
network connect data
centers and AZ’s
Physically separate – even
extremely uncommon disasters
would only affect a single AZ
Data is automatically distributed
across a minimum of 3 AZ’s GEO
separated within an AWS Region

Amazon S3 Storage Classes
& T ra n sitio n s
S3 Standard S3 Standard –
Infrequent Access
Amazon Glacier
Active data
Synchronous access
Milliseconds retrieval
2.1¢-GB/mo
Archive data
Asynchronous access
Minutes-to-hours retrieval
0.4¢-GB/mo
Infrequently accessed data
Synchronous access
Milliseconds retrieval
1.25¢-GB/mo

Amazon S3 Security, Encryption & Compliance
T he b roade st se t of tools in the indu stry
Security
• IAM and Bucket Policies
• Access Control Lists
• Audit logging with CloudTrail
& Alerts with CloudWatch
• Secure CloudFormation
templates
• Amazon Macie
• S3 Console Permission Checks
Encryption
• Encryption in transit with TLS
• SSE-S3 – Amazon S3 manages
data & keys
• SSE-C – Customer managed keys
• SSE-KMS – Master keys in KMS
• CSE – 100% Customer managed
• Default Bucket Encryption
• Encryption Status in Inventory
Compliance
• PCI-DSS
• HIPAA/HITECH
• FedRAMP
• FISMA
• EU Data Protection
Directive

Do More With Your In-place Data
• Athena
• Redshift Spectrum
• QuickSight
• EMR
Data Lake
Storage
IoT Storage
Machine Learning
& AI Storage
• AWS IoT
• Greengrass
• Other IoT sensors
• Rekognition
• LEX
• Polly
• MXNet & TensorFlow

Enter Data Lake Architectures
Data lake is a new and increasingly
popular architecture to store and analyze
massive volumes and heterogeneous
types of data

Benefits of a Data Lake—All Data is in One Place
Analyze all of your data,
from all of your sources, in one stored
location
“Why is the data distributed in
many locations? Where is the
single source of truth?”

Designed for 11 9s
of durability
Designed for
99.99% availability
Durable Available High performance
Multiple upload
Range GET
Scalable throughput
Store as much as you need
Scale storage and compute
independently
No minimum usage commitments
Scalable
Amazon EMR
Amazon Redshift Spectrum
Amazon DynamoDB
Amazon Athena
AWS Glue
Amazon Rekognition
Amazon Macie
Integrated
Simple REST API
AWS SDKs
Simple management tools
Event notification
Lifecycle policies
Easy to use
Why Amazon S3 for a Data Lake?

AWS Direct Connect AWS Snowball ISV Connectors
Amazon Kinesis
Firehose
Amazon S3 Transfer
Acceleration
AWS Storage
Gateway
Data Ingestion into Amazon S3

Building a Data Lake on AWS
Kinesis Firehose
Athena
Query Service

Processing and Analytics
Real-time Batch
AI and Predictive
BI and Data Visualization
Transactional and
RDBMS
AWS Lambda
Apache Storm
on EMR
Apache Flink
on EMR
Spark Streaming
on EMR
Elasticsearch
Service
Kinesis Analytics,
Kinesis Streams
DynamoDB
NoSQL DB Relational Database
Aurora
EMR
Hadoop, Spark,
Presto
Amazon Redshift
Data Warehouse
Amazon Athena
Query Service
Amazon Lex
Speech
recognition
Amazon
Rekognition
Amazon Polly
Text to speech
Machine Learning
Predictive analytics
Kinesis Streams
and Firehose

IoT is about…
“closing the gap between the physical and digital
world in self-reinforcing and self-improving systems.”

AWS IoT Device Management
IoT Device Management is similar to 1-Click, but at a larger scale.
Onboard, deploy, and manage your ﬂeet of devices all from a single location.
Organize inventory, query the ﬂeet for troubleshooting, and remotely deploy updates
take action on subsets of your devices, not just all of them at once

AWS IoT Device Defender
Many of the attacks we’ve seen in recent years have
utilized unsecured IoT devices.
Device Defender allows you to set device policies, audit
them, and monitor behaviours on an individual level to
identify anomalies and out-of-compliance behaviours
Send you automatic alerts when it detects a problem

AWS IoT Analytics
Traditionally, IoT devices pick up a lot of “noisy” data, like
temperature and humidity, resulting in raw, unstructured information
that’s very difﬁcult to process.

AWS FreeRTOS
While larger devices often come with a full onboard CPU, smaller ones tend
to use an MCU (micro controller unit) and they do still need an operating
system. Amazon has created their own version of FreeRTOS (a commonly
used OS in these devices), and it’s got some awesome features.
Amazon FreeRTOS comes with prepackaged libraries to connect to AWS
services, update, and secure the device. It also allows you to easily send
data to the cloud for further analysis.

AWS GreenGrass
Execute funções lambda nos dispositivos com segurança usando recursos
locais de computação, sistema de mensagens, armazenamento de dados
em cache e sincronização para dispositivos conectados.

AWS GreenGrass Exemplo
You have a collection of IoT sensors deployed in the ﬁeld, along with a
GreenGrass device. Rather than sending data straight to the cloud, the
sensors can connect to the GreenGrass device directly and have it perform
some operation for them. This is done locally – the sensors don’t need to be
connected to the public internet to communicate with GreenGrass.
This saves time by lowering the latency of connections, and money by
ﬁltering data from the sensors before sending it all to the cloud for
processing.

AWS GreenGrass
Machine Learning Inference
GreenGrass device still operates the same way – at the edge of your network.
But it can now apply machine learning models in the ﬁeld.
ex: an IoT sensor that takes an action in response to a voice command.
Before, you’d have to send that data to the cloud for processing, then back to
the sensor, which would then trigger the action (back to the cloud again, most
likely). With Machine Learning Inference on a GreenGrass device, you can do
all of that locally, resulting in much faster response times.

Obrigado!@andrefaria - andrefaria.com

Bluesoft @ AWS re:Invent 2017 + AWS 101

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