What to Expect:
- AWS Lambda concepts
- Common Serverless use cases
- Monolithic vs. service oriented architecture
- Debugging applications with AWS Cloudwatch and AWS X-Ray
Build a Serverless Web Application in One Day Workshop - DevDay Los Angeles 2017Amazon Web Services
The document provides an overview of a serverless workshop that will teach attendees how to build a serverless web application. It outlines the scenario of building a website for the fictional company Wild Rydes. The workshop consists of four labs that will cover hosting a static website on Amazon S3, managing users with Amazon Cognito, creating a serverless backend with AWS Lambda and DynamoDB, and building a RESTful API with API Gateway. Details are provided on the services that will be used, including Lambda, DynamoDB, Cognito, S3, and API Gateway.
Getting Started with Docker on AWS - DevDay Los Angeles 2017Amazon Web Services
The document discusses getting started with Docker on AWS. It provides an overview of containers and Docker, and introduces Amazon ECS for managing Docker containers on AWS. Key points include: Docker provides portable application environments; Amazon ECS manages container scheduling across EC2 instances; and services in ECS allow deploying containers behind a load balancer for long-running applications.
Building and Scaling a Containerized Microservice - DevDay Los Angeles 2017Amazon Web Services
From monolith to microservices, you'll learn to build and scale your first containerized microservice on AWS. We'll cover, microservices architecture, Amazon ECS, Task Placement and twelve-factor app with Amazon ECS.
BDA402 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Everything generates logs. Applications, infrastructure, security ... everything. Keeping track of the flood of log data is a big challenge, yet critical to your ability to understand your systems and troubleshoot (or prevent) issues. In this session, we will use both Amazon CloudWatch and application logs to show you how to build an end-to-end log analytics solution. First, we cover how to configure an Amazon Elaticsearch Service domain and ingest data into it using Amazon Kinesis Firehose, demonstrating how easy it is to transform data with Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data and configure a secure analytics environment. We demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.
This document discusses containers and Amazon ECS. It provides an overview of containers and their benefits like portability and efficiency. It then describes Amazon ECS as a highly scalable and performant container management service that supports Docker containers. It discusses how ECS runs applications on a managed cluster of EC2 instances using tasks, services, and scheduling. It also outlines some key benefits of ECS like being fully managed, integration with other AWS services, and application load balancing. Finally, it provides examples of commands to create an ECS cluster, register a task definition, and create a service to run tasks.
WKS401 Deploy a Deep Learning Framework on Amazon ECS and EC2 Spot InstancesAmazon Web Services
Deep learning is an implementation of machine learning that uses neural networks to solve difficult and complex problems, such as computer vision, natural language processing, and recommendations. Due to the availability of deep learning libraries and frameworks, developers have the ability to enhance the capabilities of their applications and projects. In this workshop, you learn how to build and deploy a powerful deep learning framework called MXNet on containers. The portability and resource management benefit of containers means developers can focus less on infrastructure and more on building. The labs start by demonstrating the automation capabilities of AWS CloudFormation to stand up core infrastructure; as an added bonus, you use Spot Fleet to leverage the cost benefits of using Spot Instances, especially for developer environments. Then, you walk through creating an MXNet container in Docker and deploying it with Amazon ECS. Finally, you walk through an image classification demo of MXNet to validate that everything is working as expected.
Pre-reqs: Laptop and AWS account
Build a Serverless Web Application in One Day Workshop - DevDay Los Angeles 2017Amazon Web Services
The document provides an overview of a serverless workshop that will teach attendees how to build a serverless web application. It outlines the scenario of building a website for the fictional company Wild Rydes. The workshop consists of four labs that will cover hosting a static website on Amazon S3, managing users with Amazon Cognito, creating a serverless backend with AWS Lambda and DynamoDB, and building a RESTful API with API Gateway. Details are provided on the services that will be used, including Lambda, DynamoDB, Cognito, S3, and API Gateway.
Getting Started with Docker on AWS - DevDay Los Angeles 2017Amazon Web Services
The document discusses getting started with Docker on AWS. It provides an overview of containers and Docker, and introduces Amazon ECS for managing Docker containers on AWS. Key points include: Docker provides portable application environments; Amazon ECS manages container scheduling across EC2 instances; and services in ECS allow deploying containers behind a load balancer for long-running applications.
Building and Scaling a Containerized Microservice - DevDay Los Angeles 2017Amazon Web Services
From monolith to microservices, you'll learn to build and scale your first containerized microservice on AWS. We'll cover, microservices architecture, Amazon ECS, Task Placement and twelve-factor app with Amazon ECS.
BDA402 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Everything generates logs. Applications, infrastructure, security ... everything. Keeping track of the flood of log data is a big challenge, yet critical to your ability to understand your systems and troubleshoot (or prevent) issues. In this session, we will use both Amazon CloudWatch and application logs to show you how to build an end-to-end log analytics solution. First, we cover how to configure an Amazon Elaticsearch Service domain and ingest data into it using Amazon Kinesis Firehose, demonstrating how easy it is to transform data with Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data and configure a secure analytics environment. We demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.
This document discusses containers and Amazon ECS. It provides an overview of containers and their benefits like portability and efficiency. It then describes Amazon ECS as a highly scalable and performant container management service that supports Docker containers. It discusses how ECS runs applications on a managed cluster of EC2 instances using tasks, services, and scheduling. It also outlines some key benefits of ECS like being fully managed, integration with other AWS services, and application load balancing. Finally, it provides examples of commands to create an ECS cluster, register a task definition, and create a service to run tasks.
WKS401 Deploy a Deep Learning Framework on Amazon ECS and EC2 Spot InstancesAmazon Web Services
Deep learning is an implementation of machine learning that uses neural networks to solve difficult and complex problems, such as computer vision, natural language processing, and recommendations. Due to the availability of deep learning libraries and frameworks, developers have the ability to enhance the capabilities of their applications and projects. In this workshop, you learn how to build and deploy a powerful deep learning framework called MXNet on containers. The portability and resource management benefit of containers means developers can focus less on infrastructure and more on building. The labs start by demonstrating the automation capabilities of AWS CloudFormation to stand up core infrastructure; as an added bonus, you use Spot Fleet to leverage the cost benefits of using Spot Instances, especially for developer environments. Then, you walk through creating an MXNet container in Docker and deploying it with Amazon ECS. Finally, you walk through an image classification demo of MXNet to validate that everything is working as expected.
Pre-reqs: Laptop and AWS account
Intro to Batch Processing on AWS - DevDay Los Angeles 2017Amazon Web Services
What to expect:
• Batch processing – overview and challenges
• Why run batch workloads in the cloud
• Overview of AWS batch solutions
• Deep dive look at AWS Batch and Amazon ECS
• Best practices
Workshop; Deploy a Deep Learning Framework on Amazon ECS and Spot InstancesAmazon Web Services
This document provides an overview of a workshop on deploying a deep learning framework on Amazon ECS and Spot Instances. The workshop will introduce MXNet, containers, Amazon ECS, Amazon ECR, AWS CloudFormation, Amazon EC2 Spot Fleet and Spot Instances. It will include hands-on labs to build an MXNet Docker image, deploy an MXNet container with ECS, and run an image classification demo using a Spot Fleet on ECS. The overall goal is to learn how to cost-effectively run deep learning workloads on AWS.
Building and Scaling Your First Containerized MicroservicesAmazon Web Services
This document discusses moving from monolithic architectures to microservices architectures using containerized microservices on Amazon ECS. It begins with an overview of microservices architectures and their benefits. It then covers Amazon ECS and how it provides a fully managed container orchestration service. The rest of the document discusses various aspects of deploying and managing microservices on ECS, including task scheduling and placement, reference architectures, continuous delivery, secrets management, and event streaming.
The document provides an agenda for an AWS event taking place on November 3, 2016 in Bucharest, Romania. The agenda includes sessions on AWS services like storage, compute, databases and managed services as well as business topics like cloud computing benefits and security and compliance. There will also be technical and business tracks running concurrently throughout the day along with introductions to the AWS Partner Network.
WKS401 Deploy a Deep Learning Framework on Amazon ECS and EC2 Spot InstancesAmazon Web Services
This document provides an overview and agenda for a workshop on deploying a deep learning framework on Amazon ECS and EC2 Spot Instances. The workshop will:
- Introduce MXNet, an open-source deep learning framework.
- Provide an overview of containers, Amazon ECS, Amazon ECR, AWS CloudFormation, and EC2 Spot Instances.
- Guide participants through hands-on labs to build an MXNet Docker image, deploy an MXNet container with ECS, run an image classification demo, and wrap the demo in an ECS task.
Build a Serverless Web Application in One Day - DevDay Austin 2017Amazon Web Services
This document provides an overview of a workshop on building serverless web applications. It will cover using AWS services like Lambda, DynamoDB, API Gateway, Cognito, and S3. The workshop scenario involves building a website for the company Wild Rydes. Participants will complete labs on static website hosting with S3, user management with Cognito, creating a serverless backend with Lambda and DynamoDB, and exposing APIs with API Gateway. The goal is to introduce the basics of building web apps without having to manage servers.
AWS re:Invent 2016: Monitoring, Hold the Infrastructure: Getting the Most fro...Amazon Web Services
This document discusses monitoring AWS Lambda functions. It provides an overview of AWS Lambda, important concepts like triggers and statelessness. It also covers best practices, examples of AWS Lambda usage, and how to add monitoring. Specifically, it recommends adding a line to CloudWatch logs to report metrics to monitoring systems like Datadog. The speaker then demonstrates creating and monitoring a sample AWS Lambda function.
Getting Started with AWS Lambda and the Serverless Cloud - AWS Summit Cape T...Amazon Web Services
This document provides an overview and introduction to AWS Lambda and serverless computing. It discusses AWS compute offerings like EC2, ECS, and Lambda. It explains benefits of Lambda like no servers to provision, automatic scaling, and built-in availability. Common use cases for Lambda are also presented like web applications, backends, data processing, chatbots, Alexa skills, and IT automation. Best practices for Lambda like versioning, networking, externalizing configuration, and monitoring with X-Ray are covered. The document concludes that Lambda is well-suited for modern application architectures.
AWS January 2016 Webinar Series - Getting Started with Big Data on AWSAmazon Web Services
With hundreds of new and sometimes disparate tools, it’s hard to keep pace. Amazon Web Services provides a broad and fully integrated portfolio of cloud computing services to help you build, secure and deploy your big data applications.
Attend this webinar to get an overview of the different big data options available in the AWS Cloud – including popular big data frameworks such as Hadoop, Spark, NoSQL databases, and more. Learn about ideal use cases, cases to avoid, performance, interfaces, and more. Finally, learn how you can build valuable applications with a real-life example.
Learning Objectives:
Learn about big data tools available at AWS
Understand ideal use cases
Learn some of the key considerations such as performance, scalability, elasticity and availability, when selecting big data tools
Who Should Attend:
Data Architects, Data Scientists, Developers
Serverless Big Data Architectures: Serverless Data AnalyticsKristana Kane
Serverless architectures are evolving to support big data analytics workflows. The document outlines serverless services for ingesting, storing, processing, and visualizing data. It describes how AWS Lambda, DynamoDB, S3, Kinesis, Athena, Glue, and other serverless services can be used without provisioning or managing servers. Serverless design patterns are presented for real-time analytics, interactive queries, and ETL workflows. A demo is promised to illustrate serverless big data architectures.
SRV203 Getting Started with AWS Lambda and the Serverless CloudAmazon Web Services
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. With serverless computing, you can build web, mobile, and IoT backends; run stream processing or big data workloads; run chatbots, and more. In this session, you'll learn how to get started with serverless computing with AWS Lambda, which lets you run code without provisioning or managing servers. We'll introduce you to the basics of building with Lambda and how you can benefit from features such as continuous scaling, built-in high availability, integrations with AWS and third-party apps, and subsecond metering pricing. We'll also introduce you to the broader portfolio of AWS services that help you build serverless applications with Lambda, including Amazon API Gateway, Amazon DynamoDB, AWS Step Functions, and more.
Microservices are a software architecture style where applications are composed of small, independent services that communicate using language-agnostic APIs. Microservices are designed to be small, highly decoupled, and focus on doing a single task. This contrasts with monolithic architectures that use a single codebase. Microservices architectures enable independent development, deployment, and scaling of services. However, running microservices at scale introduces challenges around resource management, monitoring, service discovery, and deployment complexity.
AWS re:Invent 2016: Wild Rydes Takes Off – The Dawn of a New Unicorn (SVR309)Amazon Web Services
Wild Rydes (www.wildrydes.com) needs your help! With fresh funding from its seed investors, Wild Rydes is seeking to build the world’s greatest mobile/VR/AR unicorn transportation system. The scrappy startup needs a first-class webpage to begin marketing to new users and to begin its plans for global domination. Join us to help Wild Rydes build a website using a serverless architecture. You’ll build a scalable website using services like AWS Lambda, Amazon API Gateway, Amazon DynamoDB, and Amazon S3. Join this workshop to hop on the rocket ship!
To complete this workshop, you'll need:
Your laptop
AWS Account
AWS Command Line Interface
Google Chrome
git
Text Editor
This document provides an overview of a serverless workshop on building backend APIs using AWS Lambda, Amazon DynamoDB, and Amazon API Gateway. It discusses serverless computing and its benefits over traditional infrastructure management. The workshop consists of 4 modules that teach participants how to use the Serverless Application Model to define a serverless REST API, implement continuous delivery pipelines with AWS CodePipeline and CodeBuild, integrate AWS X-Ray for debugging, and set up multiple environment pipelines for integration testing.
AWS re:Invent 2016: Infrastructure Continuous Delivery Using AWS CloudFormati...Amazon Web Services
In this session, we will review ways to manage the lifecycle of your dev, test, and production infrastructure using CloudFormation. Learn how to architect your infrastructure through loosely coupled stacks using cross-stack references, tightly coupled nested stacks and other best practices. Learn how to use CloudFormation to provision and manage a continuous deployment pipeline for your infrastructure-as-code. Automate deployment of new development environments as your infrastructure evolves, promote your new architecture for testing, and deploy changes to production.
Amazon Aurora is a cloud-optimized relational database that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. The recently announced PostgreSQL-compatibility, together with the original MySQL compatibility, are perfect for new application development and for migrations from overpriced, restrictive commercial databases. In this session, we’ll do a deep dive into the new architectural model and distributed systems techniques behind Amazon Aurora, discuss best practices and configurations, look at migration options and share customer experience from the field.
This document provides an overview of Apache MXNet and deep learning on AWS. It begins with an introduction to deep learning applications and trends. The rest of the document discusses MXNet features like scalability, language support and frameworks comparisons. It also covers MXNet usage on AWS like integration with services and AI research. The document concludes with developer resources like notebooks, documentation and tools for building models with MXNet.
"In recent years, Docker containers have become a key component of modern application design. Increasingly, developers are breaking their applications apart into smaller components and distributing them across a pool of compute resources. Using Docker on your local development machine is simple, but running Docker applications at scale in production can be difficult.
In this session, we will discuss the difficulties of running Docker in production and how Amazon EC2 Container Service (ECS) can be used to reduce the operational burdens. We will give an overview of the core architectural principles underlying Amazon ECS, and we will walk through a number of patterns used by our customers to run their microservices platforms, to run batch jobs, and for deployments and continuous integration. We will also demonstrate how to define multi-container applications with Docker Compose and deploy and scale them seamlessly on a cluster with Amazon ECS."
Blox is a collection of open source projects for container management and orchestration on Amazon ECS. Blox gives you more control over how your containerized applications run on Amazon ECS, and it enables you to build schedulers and integrate third-party schedulers on top of ECS, while leveraging Amazon ECS to fully manage and scale your clusters.
Distributed Serverless Stack Tracing and Monitoring - DevDay Austin 2017Amazon Web Services
This document summarizes a presentation about distributed serverless architectures and monitoring them with AWS X-Ray. It discusses serverless concepts like AWS Lambda and common use cases. It then explains how X-Ray works, including its key concepts, how it helps with debugging, and use cases like identifying performance bottlenecks. Finally, it provides an overview of the X-Ray API and how to get started using the X-Ray SDK and daemon.
Raleigh DevDay 2017: Distributed serverless stack tracing and monitoringAmazon Web Services
Distributed Serverless Stack
Tracing and Monitoring
The document discusses AWS Lambda and AWS X-Ray. It provides an overview of serverless concepts with AWS Lambda, common use cases, and how X-Ray can help debug serverless applications. The speaker demonstrates how to instrument code with the X-Ray SDK and use the X-Ray APIs and console to visualize and troubleshoot distributed applications.
Intro to Batch Processing on AWS - DevDay Los Angeles 2017Amazon Web Services
What to expect:
• Batch processing – overview and challenges
• Why run batch workloads in the cloud
• Overview of AWS batch solutions
• Deep dive look at AWS Batch and Amazon ECS
• Best practices
Workshop; Deploy a Deep Learning Framework on Amazon ECS and Spot InstancesAmazon Web Services
This document provides an overview of a workshop on deploying a deep learning framework on Amazon ECS and Spot Instances. The workshop will introduce MXNet, containers, Amazon ECS, Amazon ECR, AWS CloudFormation, Amazon EC2 Spot Fleet and Spot Instances. It will include hands-on labs to build an MXNet Docker image, deploy an MXNet container with ECS, and run an image classification demo using a Spot Fleet on ECS. The overall goal is to learn how to cost-effectively run deep learning workloads on AWS.
Building and Scaling Your First Containerized MicroservicesAmazon Web Services
This document discusses moving from monolithic architectures to microservices architectures using containerized microservices on Amazon ECS. It begins with an overview of microservices architectures and their benefits. It then covers Amazon ECS and how it provides a fully managed container orchestration service. The rest of the document discusses various aspects of deploying and managing microservices on ECS, including task scheduling and placement, reference architectures, continuous delivery, secrets management, and event streaming.
The document provides an agenda for an AWS event taking place on November 3, 2016 in Bucharest, Romania. The agenda includes sessions on AWS services like storage, compute, databases and managed services as well as business topics like cloud computing benefits and security and compliance. There will also be technical and business tracks running concurrently throughout the day along with introductions to the AWS Partner Network.
WKS401 Deploy a Deep Learning Framework on Amazon ECS and EC2 Spot InstancesAmazon Web Services
This document provides an overview and agenda for a workshop on deploying a deep learning framework on Amazon ECS and EC2 Spot Instances. The workshop will:
- Introduce MXNet, an open-source deep learning framework.
- Provide an overview of containers, Amazon ECS, Amazon ECR, AWS CloudFormation, and EC2 Spot Instances.
- Guide participants through hands-on labs to build an MXNet Docker image, deploy an MXNet container with ECS, run an image classification demo, and wrap the demo in an ECS task.
Build a Serverless Web Application in One Day - DevDay Austin 2017Amazon Web Services
This document provides an overview of a workshop on building serverless web applications. It will cover using AWS services like Lambda, DynamoDB, API Gateway, Cognito, and S3. The workshop scenario involves building a website for the company Wild Rydes. Participants will complete labs on static website hosting with S3, user management with Cognito, creating a serverless backend with Lambda and DynamoDB, and exposing APIs with API Gateway. The goal is to introduce the basics of building web apps without having to manage servers.
AWS re:Invent 2016: Monitoring, Hold the Infrastructure: Getting the Most fro...Amazon Web Services
This document discusses monitoring AWS Lambda functions. It provides an overview of AWS Lambda, important concepts like triggers and statelessness. It also covers best practices, examples of AWS Lambda usage, and how to add monitoring. Specifically, it recommends adding a line to CloudWatch logs to report metrics to monitoring systems like Datadog. The speaker then demonstrates creating and monitoring a sample AWS Lambda function.
Getting Started with AWS Lambda and the Serverless Cloud - AWS Summit Cape T...Amazon Web Services
This document provides an overview and introduction to AWS Lambda and serverless computing. It discusses AWS compute offerings like EC2, ECS, and Lambda. It explains benefits of Lambda like no servers to provision, automatic scaling, and built-in availability. Common use cases for Lambda are also presented like web applications, backends, data processing, chatbots, Alexa skills, and IT automation. Best practices for Lambda like versioning, networking, externalizing configuration, and monitoring with X-Ray are covered. The document concludes that Lambda is well-suited for modern application architectures.
AWS January 2016 Webinar Series - Getting Started with Big Data on AWSAmazon Web Services
With hundreds of new and sometimes disparate tools, it’s hard to keep pace. Amazon Web Services provides a broad and fully integrated portfolio of cloud computing services to help you build, secure and deploy your big data applications.
Attend this webinar to get an overview of the different big data options available in the AWS Cloud – including popular big data frameworks such as Hadoop, Spark, NoSQL databases, and more. Learn about ideal use cases, cases to avoid, performance, interfaces, and more. Finally, learn how you can build valuable applications with a real-life example.
Learning Objectives:
Learn about big data tools available at AWS
Understand ideal use cases
Learn some of the key considerations such as performance, scalability, elasticity and availability, when selecting big data tools
Who Should Attend:
Data Architects, Data Scientists, Developers
Serverless Big Data Architectures: Serverless Data AnalyticsKristana Kane
Serverless architectures are evolving to support big data analytics workflows. The document outlines serverless services for ingesting, storing, processing, and visualizing data. It describes how AWS Lambda, DynamoDB, S3, Kinesis, Athena, Glue, and other serverless services can be used without provisioning or managing servers. Serverless design patterns are presented for real-time analytics, interactive queries, and ETL workflows. A demo is promised to illustrate serverless big data architectures.
SRV203 Getting Started with AWS Lambda and the Serverless CloudAmazon Web Services
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. With serverless computing, you can build web, mobile, and IoT backends; run stream processing or big data workloads; run chatbots, and more. In this session, you'll learn how to get started with serverless computing with AWS Lambda, which lets you run code without provisioning or managing servers. We'll introduce you to the basics of building with Lambda and how you can benefit from features such as continuous scaling, built-in high availability, integrations with AWS and third-party apps, and subsecond metering pricing. We'll also introduce you to the broader portfolio of AWS services that help you build serverless applications with Lambda, including Amazon API Gateway, Amazon DynamoDB, AWS Step Functions, and more.
Microservices are a software architecture style where applications are composed of small, independent services that communicate using language-agnostic APIs. Microservices are designed to be small, highly decoupled, and focus on doing a single task. This contrasts with monolithic architectures that use a single codebase. Microservices architectures enable independent development, deployment, and scaling of services. However, running microservices at scale introduces challenges around resource management, monitoring, service discovery, and deployment complexity.
AWS re:Invent 2016: Wild Rydes Takes Off – The Dawn of a New Unicorn (SVR309)Amazon Web Services
Wild Rydes (www.wildrydes.com) needs your help! With fresh funding from its seed investors, Wild Rydes is seeking to build the world’s greatest mobile/VR/AR unicorn transportation system. The scrappy startup needs a first-class webpage to begin marketing to new users and to begin its plans for global domination. Join us to help Wild Rydes build a website using a serverless architecture. You’ll build a scalable website using services like AWS Lambda, Amazon API Gateway, Amazon DynamoDB, and Amazon S3. Join this workshop to hop on the rocket ship!
To complete this workshop, you'll need:
Your laptop
AWS Account
AWS Command Line Interface
Google Chrome
git
Text Editor
This document provides an overview of a serverless workshop on building backend APIs using AWS Lambda, Amazon DynamoDB, and Amazon API Gateway. It discusses serverless computing and its benefits over traditional infrastructure management. The workshop consists of 4 modules that teach participants how to use the Serverless Application Model to define a serverless REST API, implement continuous delivery pipelines with AWS CodePipeline and CodeBuild, integrate AWS X-Ray for debugging, and set up multiple environment pipelines for integration testing.
AWS re:Invent 2016: Infrastructure Continuous Delivery Using AWS CloudFormati...Amazon Web Services
In this session, we will review ways to manage the lifecycle of your dev, test, and production infrastructure using CloudFormation. Learn how to architect your infrastructure through loosely coupled stacks using cross-stack references, tightly coupled nested stacks and other best practices. Learn how to use CloudFormation to provision and manage a continuous deployment pipeline for your infrastructure-as-code. Automate deployment of new development environments as your infrastructure evolves, promote your new architecture for testing, and deploy changes to production.
Amazon Aurora is a cloud-optimized relational database that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. The recently announced PostgreSQL-compatibility, together with the original MySQL compatibility, are perfect for new application development and for migrations from overpriced, restrictive commercial databases. In this session, we’ll do a deep dive into the new architectural model and distributed systems techniques behind Amazon Aurora, discuss best practices and configurations, look at migration options and share customer experience from the field.
This document provides an overview of Apache MXNet and deep learning on AWS. It begins with an introduction to deep learning applications and trends. The rest of the document discusses MXNet features like scalability, language support and frameworks comparisons. It also covers MXNet usage on AWS like integration with services and AI research. The document concludes with developer resources like notebooks, documentation and tools for building models with MXNet.
"In recent years, Docker containers have become a key component of modern application design. Increasingly, developers are breaking their applications apart into smaller components and distributing them across a pool of compute resources. Using Docker on your local development machine is simple, but running Docker applications at scale in production can be difficult.
In this session, we will discuss the difficulties of running Docker in production and how Amazon EC2 Container Service (ECS) can be used to reduce the operational burdens. We will give an overview of the core architectural principles underlying Amazon ECS, and we will walk through a number of patterns used by our customers to run their microservices platforms, to run batch jobs, and for deployments and continuous integration. We will also demonstrate how to define multi-container applications with Docker Compose and deploy and scale them seamlessly on a cluster with Amazon ECS."
Blox is a collection of open source projects for container management and orchestration on Amazon ECS. Blox gives you more control over how your containerized applications run on Amazon ECS, and it enables you to build schedulers and integrate third-party schedulers on top of ECS, while leveraging Amazon ECS to fully manage and scale your clusters.
Distributed Serverless Stack Tracing and Monitoring - DevDay Austin 2017Amazon Web Services
This document summarizes a presentation about distributed serverless architectures and monitoring them with AWS X-Ray. It discusses serverless concepts like AWS Lambda and common use cases. It then explains how X-Ray works, including its key concepts, how it helps with debugging, and use cases like identifying performance bottlenecks. Finally, it provides an overview of the X-Ray API and how to get started using the X-Ray SDK and daemon.
Raleigh DevDay 2017: Distributed serverless stack tracing and monitoringAmazon Web Services
Distributed Serverless Stack
Tracing and Monitoring
The document discusses AWS Lambda and AWS X-Ray. It provides an overview of serverless concepts with AWS Lambda, common use cases, and how X-Ray can help debug serverless applications. The speaker demonstrates how to instrument code with the X-Ray SDK and use the X-Ray APIs and console to visualize and troubleshoot distributed applications.
This document provides an overview of distributed serverless stack tracing and monitoring with AWS X-Ray. It discusses AWS Lambda concepts, common use cases for serverless applications, benefits of a service-oriented architecture, how X-Ray works to trace requests across distributed services, debugging applications with X-Ray, the X-Ray SDK and API, and getting started. It also includes a demo of X-Ray's tracing capabilities.
Learn how to use AWS X-Ray to analyze both applications in development and in production, from simple three-tier applications to complex microservices applications consisting of thousands of services.
This document provides an overview of Microservices Insights with AWS X-Ray. It discusses how X-Ray traces requests across distributed services to help identify performance bottlenecks and errors. It also describes how the AWS X-Ray SDKs and daemon work to capture metadata and send it to the backend. Finally, it mentions that Lambda automatically enables X-Ray tracing and provides APIs to send, filter and retrieve trace data.
Analyzing and debugging production distributed applications built using a service-oriented or microservices architecture is a challenging task. In this session, we will introduce AWS X-Ray, a new service that makes it easier to identify performance bottlenecks and errors, pinpoint issues to specific service(s) in your application, identify the impact of issues on users of your application, and visualize a request call graph and service call graph for your applications. We will show interactive demos, and code samples for the demo will be available to all session attendees.
AWS X-Ray is a service that collects data about requests that your application serves, and provides tools you can use to view, filter, and gain insights into that data to identify issues and opportunities for optimization.
AWS X-Ray helps developers analyze and debug production, distributed applications, such as those built using a microservices architecture.
How to build and deploy serverless apps - AWS Summit Cape Town 2018Amazon Web Services
This document provides an overview of serverless computing on AWS and how to build and deploy serverless applications. It discusses what serverless computing is, common use cases, serverless patterns using AWS Lambda and Amazon API Gateway, examples of what customers are building, and how to do safe deployments of serverless apps using the AWS Serverless Application Model and AWS CodeDeploy.
Raleigh DevDay 2017: Building serverless web applicationsAmazon Web Services
This document summarizes a presentation on building serverless web applications using AWS services like AWS Lambda and Amazon API Gateway. It discusses why serverless is useful by avoiding managing servers. It then covers design patterns like monolithic versus microservices architectures. Finally, it demonstrates how to define a serverless application using the AWS Serverless Application Model (SAM) and deploy it with AWS CloudFormation.
This document provides an overview of AWS X-Ray and how it can help debug applications at scale. It discusses challenges with traditional debugging approaches not scaling well to microservices and distributed architectures. AWS X-Ray is introduced as a solution to visualize service graphs, identify errors and performance bottlenecks, and pinpoint issues to specific services. The key concepts of X-Ray like traces, segments, and sampling are explained. Finally, it provides instructions on getting started with the X-Ray SDK and daemon as well as a demo.
Getting Started with Serverless Architectures - August 2016 Monthly Webinar S...Amazon Web Services
Serverless architectures allow you to build and run applications and services without having to manage infrastructure. With serverless architectures, your application still runs on servers, but all the server management is done by AWS .
In this webinar, you will learn how to build applications and services using a serverless architecture. We will discuss how you can use AWS Lambda to run code for any type of application or backend service; use Amazon DynamoDB to store application data with high scalability and redundancy; and use Amazon API Gateway to create and manage secure API endpoints. We will run through a demo setting up a web application using this architecture, and we will discuss best practices and patterns used by our customers to run serverless applications.
Learning Objectives:
• Understand the basics of serverless architectures
• Learn how to use Lambda, API Gateway, and DynamoDB to run web applications
AWS X-Ray helps debug and monitor applications in production by providing visibility into requests across various services. It captures tracing data from AWS services, HTTP requests, and database operations. The tracing data can be used to visualize service graphs, identify performance bottlenecks, and pinpoint issues to specific services. AWS X-Ray is available in preview and provides free tiers for tracing and retrieval with additional charges for higher volumes.
The document discusses serverless architectures using AWS Lambda and Amazon API Gateway. It provides background on moving from monolithic to microservices architectures. It then covers AWS Lambda functions, event sources, and networking environments. Amazon API Gateway is presented as a way to build multi-tier serverless applications. Common serverless architecture patterns and best practices for AWS Lambda, API Gateway, and general serverless development are outlined. The document concludes with a demonstration of a simple CRUD backend using Lambda and DynamoDB with API Gateway.
by Rahul Sareen, Sr. IoT Consultant, AWS Professional Services
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. With serverless computing, you can build web, mobile, and IoT backends; run stream processing or big data workloads; run chatbots, and more. In this session, you’ll learn how to get started with serverless computing with AWS Lambda, which lets you run code without provisioning or managing servers. We’ll introduce you to the basics of building with Lambda and how you can benefit from features such as continuous scaling, built-in high availability, integrations with AWS and third-party apps, and subsecond metering pricing. We’ll also introduce you to the broader portfolio of AWS services that help you build serverless applications with Lambda, including Amazon API Gateway, Amazon DynamoDB, AWS Step Functions, and more.
Serverless Generative AI on AWS, AWS User Groups of FloridaCloudHesive
This document provides an overview of a presentation on serverless generative AI. The presentation will discuss the architecture, applications, and potential business impact of serverless generative AI. It will also explore how this technology can broaden perspectives and spark new ideas for both experienced AWS users and those just starting with cloud computing. The presentation format will include questions throughout and a dedicated Q&A period at the end.
¿Qué es eso del desarrollo sin servidores? ¿Qué lenguajes puedo utilizar? ¿Cómo hago cosas como autenticación, o guardar en base de datos, o enviar notificaciones? ¿Esto escala? A todas estas preguntas, y a alguna más, intentaré dar respuesta en esta sesión, donde haré una pequeña demo de montar una app muy sencilla y desplegarla en la nube sin preocuparnos de gestionar infraestructura. Charla realizada por primera vez para AlcarriaConf 2021
Getting Started with AWS Lambda and Serverless ComputingKristana Kane
This document provides an overview of AWS Lambda and serverless computing. It discusses AWS compute offerings like EC2, ECS, and Lambda. Lambda allows running code in response to events without provisioning or managing servers. Benefits include automatic scaling, pay per use, and built-in availability. Common use cases for Lambda include web applications, backends, data processing, chatbots, and IT automation. Best practices for Lambda include limiting function size, parameterizing code, and using versions and aliases. The document also provides examples of serverless applications and architectures using Lambda along with other AWS services.
AWS March 2016 Webinar Series Getting Started with Serverless ArchitecturesAmazon Web Services
Serverless Architectures allow you to build and run applications and services without having to manage the infrastructure. With serverless architectures on AWS, your application still runs on servers, but all the server management is done by AWS.
In this webinar, you will learn how to build applications and services using a serverless architecture. We will discuss how you can use AWS Lambda to run code for any type of application or backend service; use Amazon DynamoDB to store application data with high scalability and redundancy; and use Amazon API Gateway to create and manage secure API endpoints. We will also run through a demo setting up a web application using this architecture, and discuss best practices and patterns used by our customers to run serverless applications.
Learning Objectives:
• Understand the basics of serverless architectures
• Learn how to use Lambda, API Gateway, and DynamoDB to run web applications
Who Should Attend:
• Developers, web developers
Is your company committed to Microsoft’s application platform and Visual Studio? Learn how you can use the tools you love with the broad set of global services you need to move faster, lower costs, and scale to meet your customer’s demands. If you’re familiar with Visual Studio and SQL server but new to AWS, you’ll learn how you can use AWS without having to learn a new IDE or programming language.
Similar to Distributed Serverless Stack Tracing and Monitoring - DevDay Los Angeles 2017 (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
16. The traditional process of debugging doesn’t scale well for
production applications or those built using a service-
oriented, microservice, or serverless architecture
It’s tedious, repetitive, and time consuming.
34. X-Ray Key Concepts
Trace End-to-end data related a single request across services
Segments Portions of the trace that correspond to a single service
Sub-segments Remote call or local compute sections within a service
Annotations Business data that can be used to filter traces
Metadata Business data that can be added to the trace but not used
for filtering traces
Errors Normalized error message and stack trace
Sampling Percentage of requests to your application to capture as
traces
35. X-Ray SDK
Available for Java, .NET, and Node.js
Adds filters to automatically captures metadata for calls to:
• AWS services using the AWS SDK
• Non-AWS services over HTTP and HTTPS
• Databases (MySQL, PostgreSQL, and Amazon DynamoDB)
• Queues (Amazon SQS)
Enables you to get started quickly without having to manually
instrument your application code to log metadata about requests
36. X-Ray Daemon
Receives data from the SDK over UDP and acts as a local buffer.
Data is flushed to the backend every second or when the local
buffer fills.
Available for Amazon Linux AMI, RHEL, Ubuntu, OS X, and
Windows.
Can be run anywhere as long as AWS credentials are provided
(e.g.: EC2, ECS, on premise, developer machine, etc.)
37. Sampling configuration
{
"rules": {
"move": {
"id": 1,
"service_name": "*",
"http_method": "*",
"url_path": "/api/move/*",
"fixed_target": 0,
"rate": 0.05
},
"base": {
"id": 2,
"service_name": "*",
"http_method": "*",
"url_path": "*",
"fixed_target": 1,
"rate": 0.1
}
}
}
This example defines two rules.
The first rule applies a five-percent sampling rate
with no minimum number of requests to trace to
requests with paths under /api/move
The second overrides the default sampling rule
with a rule that traces the first request each
second and 10 percent of additional requests.
39. X-Ray API
Raw trace data is available using batch get APIs.
X-Ray provides a set of APIs to enable you to send, filter, and retrieve
trace data.
You can send trace data directly to the service without having to use
our SDKs. (e.g. you can write your own SDKs for languages/frameworks not currently supported)
You can build your own data analysis applications on top of the data
collected by X-Ray.
40. X-Ray API
PutTraceSegments Uploads segment documents to AWS X-Ray
BatchGetTraces Retrieves a list of traces specified by ID
GetServiceGraph Retrieves a document that describes services in your
application and their connections
GetTraceSummaries Retrieves IDs and metadata for traces available for a
specified time frame using an optional filter
50. X-Ray pricing
Free during the preview. After that:
Free tier
• The first 100,000 traces recorded per month are free
• The first 1,000,000 traces retrieved or scanned per month are free
Additional charges
• Beyond the free tier, traces recorded cost $5.00 per million per month
• Beyond the free tier, traces retrieved or scanned cost $0.50 per million per
month
51. Available Today!
The AWS X-Ray service is available today.
Go to https://aws.amazon.com/xray to get started.
Documentation: http://docs.aws.amazon.com/xray/latest/devguide/aws-xray.html
.NET Sample: https://github.com/awslabs/aws-xray-dotnet-webapp
Java Sample: https://github.com/awslabs/eb-java-scorekeep/tree/xray
Node.js Sample: https://github.com/awslabs/eb-node-express-sample/tree/xray