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AWS re:Invent 2016 re:Cap Julien Simon Principal Technical Evangelist, AWS [email protected] @julsimon
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AWS re:Invent 2016 recap (part 2)

Jan 05, 2017

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Page 1: AWS re:Invent 2016 recap (part 2)

AWS re:Invent 2016 re:Cap

Julien Simon Principal Technical Evangelist, AWS [email protected] @julsimon

Page 2: AWS re:Invent 2016 recap (part 2)

What Are The Best Practices Of Transformational Development?

Page 3: AWS re:Invent 2016 recap (part 2)

Security Performance efficiency

Cost optimization

Reliability

Well-Architected Framework

Operational excellence

NEW

Page 4: AWS re:Invent 2016 recap (part 2)

Introducing

The Well-Architected Framework Course With Broader And Deeper Content

Page 5: AWS re:Invent 2016 recap (part 2)

A Year In Amazon CloudFormation

20 additional services 20 services updated YAML support Role-based stack creation

Change sets Cross-stack references Failure recovery Resource schemas

Prepare

Page 6: AWS re:Invent 2016 recap (part 2)

AWS OpsWorks For Chef AutomateFully managed Chef server

NEW

Prepare

Generally Available Today

Page 7: AWS re:Invent 2016 recap (part 2)

Amazon EC2 Systems ManagerCollection of AWS tools for package installation, patching, resource configuration, and task automation

Prepare

NEW

Generally Available Today

Page 8: AWS re:Invent 2016 recap (part 2)

AWS CodeBuild

NEW

Operate

Generally Available Today

Build service for compiling source code and running unit tests

Page 9: AWS re:Invent 2016 recap (part 2)

AWS X-Ray

NEW

Operate

Analyze and debug distributed applications in production

Preview Available Today

Page 10: AWS re:Invent 2016 recap (part 2)

fanout-00002

AWS X-Ray NEW

hello-1.mbfzqxzcpe.us-east-..

hello-2.mbfzqxzcpe.us-east-..

fanout-00005 fanout-00003

throttle DynamoDB

index DynamoDB

fanout-00004

400 traces/min avg - 0.30 ms

600 traces / min avg - 0.19 ms

1000 traces /min avg - 0.13 ms

400 traces / min avg - 0.30 ms

1000 traces / min avg - 0.28 ms

400 traces / min avg - 0.30 ms

850 traces / min avg - 0.16 ms

850 traces / min avg - 0.17 ms

Client

Client

800 traces / min avg - 0.19 ms

fanout-00001

560 traces / min avg - 0.19 ms

fanout-00006

Page 11: AWS re:Invent 2016 recap (part 2)

AWS X-Ray NEW

Page 12: AWS re:Invent 2016 recap (part 2)

AWS X-Ray NEW

Page 13: AWS re:Invent 2016 recap (part 2)

AWS Personal Health Dashboard

NEW

Respond

Personalized view of AWS service health

Generally Available Today

Page 14: AWS re:Invent 2016 recap (part 2)

Web applications running on AWS are already protected by Shield Standard - no action is required

Protection from volumetric and state exhaustion attacks

NEW

Respond

Generally Available Today

AWS Shield For Everyone

Page 15: AWS re:Invent 2016 recap (part 2)

For additional protection against very large and sophisticated attacks

AWS Shield Advanced

Respond

NEW

Generally Available Today

Page 16: AWS re:Invent 2016 recap (part 2)

Respond

Advanced notifications via CloudWatch

Cost protection on ELB, CloudFront, Route 53

24/7 DDoS response team and support

WAF included at no additional cost

AWS Shield Advanced

Page 17: AWS re:Invent 2016 recap (part 2)

NEW

Targeting

Targeted push notifications for mobile apps

Amazon Pinpoint

Generally Available Today

Page 18: AWS re:Invent 2016 recap (part 2)

Understand user behavior

Define who to engage

Deliver notifications

Track campaign

results

Amazon Pinpoint

Page 19: AWS re:Invent 2016 recap (part 2)

S3 Storage Management

S3 Data Events in CloudTrail S3 Object Tagging S3 Analytics - Storage Class Analysis S3 CloudWatch Metrics S3 Inventory

Innovations In S3 NEW

Page 20: AWS re:Invent 2016 recap (part 2)

Integrated with:

Fully-managed data catalog and ETL service

S3, RDS, Redshift & any JDBC-compliant data store

AWS GlueNEW

Page 21: AWS re:Invent 2016 recap (part 2)

Build Your Data Catalog

Page 22: AWS re:Invent 2016 recap (part 2)

Generate And Edit Transformations

Page 23: AWS re:Invent 2016 recap (part 2)

Schedule And Run Your Jobs

Page 24: AWS re:Invent 2016 recap (part 2)

2. Source Data

S3 Upload

Kinesis Firehose

DynamoDB Streams

Snowball Snowball Edge

Snowmobile

3. Lifecycle management and cold storage

5. Data governance, security, privacy

8. Preparing for Analytics

9. Orchestration and job scheduling

Analytics

Database Migration Service

1. Ingestion

Datastore target

4. Metadata capture

10. Capturing data change

Athena

EMR

Elasticsearch

Redshift

AI

Machine Learning

Quicksight

RDS

DynamoDB

EFS

S3 EBS

6. Self-service discovery, search, access

Glacier

7. Managing data quality

EMR

Glue

The Modern Data Architecture On AWS

Page 25: AWS re:Invent 2016 recap (part 2)

Install batch software

Manage jobinterdependencies

Manage job queues

Schedulejobs

Provision server cluster

Scale servercluster

Challenges Of Large-Scale Batch Processing

Page 26: AWS re:Invent 2016 recap (part 2)

AWS BatchFully-managed batch processing at any scale

NEW

Preview Available Today

Page 27: AWS re:Invent 2016 recap (part 2)

Dynamic provisioning and scaling

Priority-based queues and scheduling

Cost optimization through EC2 Spot fleet

Fully managed

AWS Batch

Page 28: AWS re:Invent 2016 recap (part 2)

Containers

A Spectrum Of Compute

Page 29: AWS re:Invent 2016 recap (part 2)

High availability

Infrastructure management

Security Task scheduling

Pipeline integration

Container management

Service discovery

Resource access

The Challenges Of Container Based Operations

Page 30: AWS re:Invent 2016 recap (part 2)

Amazon ECS

Managed platform for:

Cluster Management Container Orchestration Deep AWS Integration

Page 31: AWS re:Invent 2016 recap (part 2)

ELBContainerAuto Scaling

CloudTrailVPCIAM CloudWatch Metrics

InstanceAuto Scaling

CloudWatch Logs

CloudWatchEvents

X-Ray

Deep Integration With AWS Platform

Page 32: AWS re:Invent 2016 recap (part 2)

You can now determine scheduling policies based on:

AMI ID Availability zone

Instance type

Distinct instances

Custom

Task Placement Engine (Coming Soon)

Page 33: AWS re:Invent 2016 recap (part 2)

Real-time notifications of current state

Container instance events

Task events

Event Stream

Page 34: AWS re:Invent 2016 recap (part 2)

They need the ability to:

Create custom schedulers Integrate 3rd party schedulers Develop and test locally

Developers Want More Control

Page 35: AWS re:Invent 2016 recap (part 2)

Introducing NEW

A collection of open source projects for:Container management and orchestration

Page 36: AWS re:Invent 2016 recap (part 2)

Today we are releasing:

cluster-state-service daemon-scheduler

BloxNEW

Page 37: AWS re:Invent 2016 recap (part 2)

Open Source

blox.github.io

Page 38: AWS re:Invent 2016 recap (part 2)

Serverless

AWS Lambda

A Spectrum Of Compute

Page 39: AWS re:Invent 2016 recap (part 2)

Languages Integrations

Node.js (JavaScript) Java (Java 8 compatible) Python

Amazon API Gateway AWS CodeCommit AWS CloudFormation AWS Config

Amazon Simple Notification Service Amazon Simple Email Service

State Of AWS Lambda

Amazon CloudWatch Amazon Kinesis Streams

Amazon DynamoDB Amazon RDS Amazon Aurora

Amazon Cognito Amazon S3

Page 40: AWS re:Invent 2016 recap (part 2)

VPC Support Node 4.3 Simple Proxy

API Gateway Binary Support Environment Variables Serverless Application Model

NEW

New Serverless Features In 2016

Page 41: AWS re:Invent 2016 recap (part 2)

NEW

C# In AWS Lambda

Page 42: AWS re:Invent 2016 recap (part 2)

AWS Lambda@Edge

NEW

Preview Available Today

Run AWS Lambda functions at CloudFront locations

Page 43: AWS re:Invent 2016 recap (part 2)

“I want to sequence functions”

“I want to run functions in parallel”

“I want to select functions based on data”

“I want to retry functions”

“I want to try/catch/finally”

“I have code that runs for hours”

Turning Functions Into Applications

Page 44: AWS re:Invent 2016 recap (part 2)

Method Call Function Chaining Database Queues Different Ways To Coordinate Functions

Page 45: AWS re:Invent 2016 recap (part 2)

NEW

AWS Step Functions

Generally Available Today

Coordinate the components of distributed applications using visual workflows

Page 46: AWS re:Invent 2016 recap (part 2)

AWS Step Functions

Page 47: AWS re:Invent 2016 recap (part 2)

Branching StepsSelect image

converter

RAW to JPEG RAW to PNG RAW to TIFF

Load in Database

Start

End

Unsupported image type

Upload RAW file

Delete RAW file

Start

End

Process photo

Resize image

Start

End

Extract metadata

Facial recognition

Load in Database

Parallel StepsSequential Steps

AWS Step Functions

Page 48: AWS re:Invent 2016 recap (part 2)

Virtual Machines Containers Serverless

First-Class Primitives With Deep AWS Integration

Page 49: AWS re:Invent 2016 recap (part 2)

Transformations

Page 50: AWS re:Invent 2016 recap (part 2)

AWS OpsWorks For Chef Automate

Amazon EC2 Systems Manager

AWS CodeBuild

AWS X-Ray

AWS Personal Health Dashboard

AWS Shield

Amazon Pinpoint

AWS Glue

AWS Batch

C# In AWS Lambda

AWS Lambda@Edge

AWS Step Functions

Elastic GPUs For EC2

Amazon Lightsail

F1 Instances

Amazon Athena

Amazon AI

Amazon Rekognition

Amazon Polly

Amazon Lex

PostgreSQL For Aurora

AWS Greengrass

AWS Snowball Edge

AWS Snowmobile

Day 1 Day 2

Page 51: AWS re:Invent 2016 recap (part 2)

https://aws.amazon.com/fr/blogs/aws/well-architected-working-backward-to-play-it-forward/ https://aws.amazon.com/fr/opsworks/chefautomate/ https://aws.amazon.com/fr/ec2/systems-manager/ https://aws.amazon.com/fr/blogs/aws/aws-codebuild-fully-managed-build-service/ https://aws.amazon.com/fr/blogs/aws/aws-x-ray-see-inside-of-your-distributed-application/ https://aws.amazon.com/fr/blogs/aws/new-aws-personal-health-dashboard-status-you-can-relate-to/ https://aws.amazon.com/fr/blogs/aws/aws-shield-protect-your-applications-from-ddos-attacks/ https://aws.amazon.com/fr/blogs/aws/amazon-pinpoint-hit-your-targets-with-aws/ https://aws.amazon.com/fr/blogs/aws/aws-batch-run-batch-computing-jobs-on-aws/ https://aws.amazon.com/fr/blogs/aws/blox-new-open-source-scheduler-for-amazon-ec2-container-service/ https://aws.amazon.com/fr/blogs/aws/coming-soon-lambda-at-the-edge/ https://aws.amazon.com/fr/blogs/compute/announcing-c-sharp-support-for-aws-lambda/ https://aws.amazon.com/fr/blogs/aws/new-aws-step-functions-build-distributed-applications-using-visual-workflows/

Page 52: AWS re:Invent 2016 recap (part 2)

AWS re : Invent 2016 : Ach iev ing Agi l i ty by Fo l lowing Wel l -Arch i tected Framework Pr inc ip les (ARC203) ht tps : / /www.youtube.com/watch?v=8e2zCHx-K3M AWS re : Invent 2016 : In f ras t ructure Cont inuous De l i very Us ing AWS CloudFormat ion (DEV313) h t tps : / /www.youtube.com/watch?v=TDalsML3QqY AWS re : Invent 2016 : NEW LAUNCH! Amazon EC2 Systems Manager for Hybr id C loud Management (WIN205) ht tps : / /www.youtube.com/watch?v=2ZqmRSIqQ6Q AWS re : Invent 2016 : NEW LAUNCH! In t roduct ion to AWS X-Ray (DEV316) h t tps : / /www.youtube.com/watch?v=s8tB3YhZd9U AWS re : Invent 2016 : NEW LAUNCH! Transparency and contro l w i th AWS Persona l Hea l th Dashboard (ARC207) ht tps : / /www.youtube.com/watch?v=0kW2rkfQgvM AWS re : Invent 2016 : NEW LAUNCH! AWS Sh ie ld—A Managed DDoS Protect ion Serv ice (SAC322) ht tps : / /www.youtube.com/watch?v=R06GDQBbtRU AWS re : Invent 2016 : NEW LAUNCH! Enab le Rea l -T ime Mobi le App Engagement w i th Amazon P inpo int (MBL304) h t tps : / /www.youtube.com/watch?v=eq3hKt0XxQE AWS re : Invent 2016 : NEW LAUNCH! In t roduct ion to AWS Glue : A Fu l l y Managed ETL Serv ice (BDA209) ht tps : / /www.youtube.com/watch?v=4N_ktE4NFIk AWS re : Invent 2016 : NEW LAUNCH! In t roduc ing AWS Batch : Easy and e f f i c ient batch comput ing (CMP323) ht tps : / /www.youtube.com/watch?v=ebwfhSS4ZkY AWS re : Invent 2016 : NEW LAUNCH! Advanced Task Schedu l ing w i th Amazon ECS and B lox (CON307) ht tps : / /www.youtube.com/watch?v=evYcLW3TLcQ AWS re : Invent 2016 : NEW LAUNCH! Br ing ing AWS Lambda to the Edge (CTD206) ht tps : / /www.youtube.com/watch?v=j26novaqF6M AWS re : Invent 2016 : NEW LAUNCH! Deve lop ing Server less C# Appl i cat ions (DEV207) h t tps : / /www.youtube.com/watch?v=Ymn6WGCSjE4 AWS re : Invent 2016 : NEW LAUNCH! Server less Apps w i th AWS Step Funct ions (SVR201) h t tps : / /www.youtube.com/watch?v=75MRve4nv8s

Page 53: AWS re:Invent 2016 recap (part 2)

Thank You Julien Simon Principal Technical Evangelist, AWS [email protected] @julsimon