As serverless architectures become more popular, AWS customers need a framework of patterns to help them deploy their workloads without managing servers or operating systems.
Low Code Integration with Apache Camel.pdfClaus Ibsen
Design your integration flows using Camel and JBang for a better developer experience, and make it easily production grade using Quarkus.
Claus Ibsen, Apache Camel lead & Senior Principal Software Engineer, Red Hat
CQRS and Event Sourcing, An Alternative Architecture for DDDDennis Doomen
Most of us will be familiar with the standard 3- or 4-layer architecture you often see in larger enterprise systems. Some are already practicing Domain Driven Design and work together with the business to clarify the domain concepts. Perhaps you’ve noticed that is difficult to get the intention of the 'verbs' from that domain into this standard architecture. If performance is an important requirement as well, then you might have discovered that an Object-Relational Mapper and a relational database are not always the best solution.
One of the main reasons for this is the fact that the interests of a consistent domain that takes into account the many business rules, and those of data reporting and presentation are conflicting. That’s why Betrand Meyer introduced the Command Query Separation principle.
An architecture based on this principle combined with the Event Sourcing concept provides the ideal architecture for building high-performance systems designed using DDD. Well-known bloggers like Udi Dahan and Greg Young have already spent quite a lot of of posts on this, and this year’s Developer Days had some coverage as well.
But how do you build such a system with the. NET framework? Is it really as complex as some claim, or is just different work?
This presentation explains what serverless is all about, explaining the context from Devs & Ops points of view, and presenting the various ways to achieve serverless (Functions a as Service, BaaS....). It also presents the various competitors on the market and demo one of them, openfaas. Finally, it enlarges the pictures, positionning serverless, combined with Edge computing & IoT, as a valuable triptic cloud vendors are leveraging on top of, to create end-to-end offers.
Architecture patterns for distributed, hybrid, edge and global Apache Kafka d...Kai Wähner
Architecture patterns for distributed, hybrid, edge and global Apache Kafka deployments
Multi-cluster and cross-data center deployments of Apache Kafka have become the norm rather than an exception. This session gives an overview of several scenarios that may require multi-cluster solutions and discusses real-world examples with their specific requirements and trade-offs, including disaster recovery, aggregation for analytics, cloud migration, mission-critical stretched deployments and global Kafka.
Key takeaways:
In many scenarios, one Kafka cluster is not enough. Understand different architectures and alternatives for multi-cluster deployments.
Zero data loss and high availability are two key requirements. Understand how to realize this, including trade-offs.
Learn about features and limitations of Kafka for multi cluster deployments
Global Kafka and mission-critical multi-cluster deployments with zero data loss and high availability became the normal, not an exception.
2 hour session where I cover what is Apache Camel, latest news on the upcoming Camel v3, and then the main topic of the talk is the new Camel K sub-project for running integrations natively on the cloud with kubernetes. The last part of the talk is about running Camel with GraalVM / Quarkus to archive native compiled binaries that has impressive startup and footprint.
Building and deploying microservices with event sourcing, CQRS and Docker (Be...Chris Richardson
In this talk we share our experiences developing and deploying a microservices-based application. You will learn about the distributed data management challenges that arise in a microservices architecture. We will describe how we solved them using event sourcing to reliably publish events that drive eventually consistent workflows and pdate CQRS-based views. You will also learn how we build and deploy the application using a Jenkins-based deployment pipeline that creates Docker images that run on Amazon EC2.
This talk was given at the Berlin Microxchg conference and the Munich microservices meetup.
Low Code Integration with Apache Camel.pdfClaus Ibsen
Design your integration flows using Camel and JBang for a better developer experience, and make it easily production grade using Quarkus.
Claus Ibsen, Apache Camel lead & Senior Principal Software Engineer, Red Hat
CQRS and Event Sourcing, An Alternative Architecture for DDDDennis Doomen
Most of us will be familiar with the standard 3- or 4-layer architecture you often see in larger enterprise systems. Some are already practicing Domain Driven Design and work together with the business to clarify the domain concepts. Perhaps you’ve noticed that is difficult to get the intention of the 'verbs' from that domain into this standard architecture. If performance is an important requirement as well, then you might have discovered that an Object-Relational Mapper and a relational database are not always the best solution.
One of the main reasons for this is the fact that the interests of a consistent domain that takes into account the many business rules, and those of data reporting and presentation are conflicting. That’s why Betrand Meyer introduced the Command Query Separation principle.
An architecture based on this principle combined with the Event Sourcing concept provides the ideal architecture for building high-performance systems designed using DDD. Well-known bloggers like Udi Dahan and Greg Young have already spent quite a lot of of posts on this, and this year’s Developer Days had some coverage as well.
But how do you build such a system with the. NET framework? Is it really as complex as some claim, or is just different work?
This presentation explains what serverless is all about, explaining the context from Devs & Ops points of view, and presenting the various ways to achieve serverless (Functions a as Service, BaaS....). It also presents the various competitors on the market and demo one of them, openfaas. Finally, it enlarges the pictures, positionning serverless, combined with Edge computing & IoT, as a valuable triptic cloud vendors are leveraging on top of, to create end-to-end offers.
Architecture patterns for distributed, hybrid, edge and global Apache Kafka d...Kai Wähner
Architecture patterns for distributed, hybrid, edge and global Apache Kafka deployments
Multi-cluster and cross-data center deployments of Apache Kafka have become the norm rather than an exception. This session gives an overview of several scenarios that may require multi-cluster solutions and discusses real-world examples with their specific requirements and trade-offs, including disaster recovery, aggregation for analytics, cloud migration, mission-critical stretched deployments and global Kafka.
Key takeaways:
In many scenarios, one Kafka cluster is not enough. Understand different architectures and alternatives for multi-cluster deployments.
Zero data loss and high availability are two key requirements. Understand how to realize this, including trade-offs.
Learn about features and limitations of Kafka for multi cluster deployments
Global Kafka and mission-critical multi-cluster deployments with zero data loss and high availability became the normal, not an exception.
2 hour session where I cover what is Apache Camel, latest news on the upcoming Camel v3, and then the main topic of the talk is the new Camel K sub-project for running integrations natively on the cloud with kubernetes. The last part of the talk is about running Camel with GraalVM / Quarkus to archive native compiled binaries that has impressive startup and footprint.
Building and deploying microservices with event sourcing, CQRS and Docker (Be...Chris Richardson
In this talk we share our experiences developing and deploying a microservices-based application. You will learn about the distributed data management challenges that arise in a microservices architecture. We will describe how we solved them using event sourcing to reliably publish events that drive eventually consistent workflows and pdate CQRS-based views. You will also learn how we build and deploy the application using a Jenkins-based deployment pipeline that creates Docker images that run on Amazon EC2.
This talk was given at the Berlin Microxchg conference and the Munich microservices meetup.
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Best Practices for Middleware and Integration Architecture Modernization with...Claus Ibsen
What are important considerations when modernizing middleware and moving towards serverless and/or cloud native integration architectures? How can we make the most of flexible technologies such as Camel K, Kafka, Quarkus and OpenShift. Claus is working as project lead on Apache Camel and has extensive experience from open source product development.
The talk was recorded and runs for 30 minutes and published on youtube at: https://www.youtube.com/watch?v=d1Hr78a7Lww
Serverless integration with Knative and Apache Camel on KubernetesClaus Ibsen
This presentation will introduce Knative, an open source project that adds serverless capabilities on top of Kubernetes, and present Camel K, a lightweight platform that brings Apache Camel integrations in the serverless world. Camel K allows running Camel routes on top of any Kubernetes cluster, leveraging Knative serverless capabilities such as “scaling to zero”.
We will demo how Camel K can connect cloud services or enterprise applications using its 250+ components and how it can intelligently route events within the Knative environment via enterprise integration patterns (EIP).
Target Group: Developers, architects and other technical people - a basic understanding of Kubernetes is an advantage
In this session, we’ll discuss the benefits of moving from monolithic to micro-services application architectures, and examine where micro-services can be used. We’ll share common transition strategies and relate them to the specifics of e-commerce and retail workloads, using customer examples. You’ll learn how to build micro-services using AWS services, and get a better understanding of the role of data storage, API endpoints and service discovery. Plus, you can learn from the real-life experience of Digital Goodie, an online retailing platform for connected commerce.
Understanding MicroSERVICE Architecture with Java & Spring BootKashif Ali Siddiqui
This is a deep journey into the realm of "microservice architecture", and in that I will try to cover each inch of it, but with a fixed tech stack of Java with Spring Cloud. Hence in the end, you will be get know each and every aspect of this distributed design, and will develop an understanding of each and every concern regarding distributed system construct.
Learn about what a serverless architecture is, why they are growing in popularity, and who the key players are in a serverless API build on the AWS platform. Then get started building your own servless API!
While many organizations have started to automate their software development processes, many still engineer their infrastructure largely by hand. Treating your infrastructure just like any other piece of code creates a “programmable infrastructure” that allows you to take full advantage of the scalability and reliability of the AWS cloud. This session will walk through practical examples of how AWS customers have merged infrastructure configuration with application code to create application-specific infrastructure and a truly unified development lifecycle. You will learn how AWS customers have leveraged tools like CloudFormation, orchestration engines, and source control systems to enable their applications to take full advantage of the scalability and reliability of the AWS cloud, create self-reliant applications, and easily recover when things go seriously wrong with their infrastructure.
Benefits of Stream Processing and Apache Kafka Use Casesconfluent
Watch this talk here: https://www.confluent.io/online-talks/benefits-of-stream-processing-and-apache-kafka-use-cases-on-demand
This talk explains how companies are using event-driven architecture to transform their business and how Apache Kafka serves as the foundation for streaming data applications.
Learn how major players in the market are using Kafka in a wide range of use cases such as microservices, IoT and edge computing, core banking and fraud detection, cyber data collection and dissemination, ESB replacement, data pipelining, ecommerce, mainframe offloading and more.
Also discussed in this talk are the differences between Apache Kafka and Confluent Platform.
This session is part 1 of 4 in our Fundamentals for Apache Kafka series.
Any team that has made the jump from building monoliths to building microservices knows the complexities you must overcome to build a system that is functional and maintainable. Building a microservice architecture that is low latency and only communicates using REST APIs is even more tricky, with high latency for requests being a common concern. This talk explains how you can use events as the backbone of your microservice architecture and build an efficient, event-driven system. It covers how to get started with designing your microservice architecture and the key requirements any system needs to fulfil. It also introduces the different patterns you will encounter in event-driven architectures and the advantages and disadvantages of these choices. Finally it explains why Apache Kafka is a great choice for event-driven microservices.
by Kashif Imran, Sr. Solutions Architect, AWS
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.
Real-time Analytics with Upsert Using Apache Kafka and Apache Pinot | Yupeng ...HostedbyConfluent
Apache Kafka is used as the primary message bus for propagating events and logs across Uber. In particular, it pairs with Apache Pinot, a real-time distributed OLAP datastore, to deliver real-time insights seconds after the messages produced to Kafka.
One challenge we faced was to update existing data in Pinot with the changelog in Kafka, and deliver an accurate view in the real-time analytical results. For example, the financial dashboard can report gross booking with the corrected Ride fares. And restaurant owners can analyze the UberEats orders with their latest delivery status.
Implementing upserts in an immutable real-time OLAP store like Pinot is nontrivial. We need to make architectural changes in how data is distributed via Kafka amongst the server nodes, how it's indexed and queried in a distributed fashion. In this talk I will discuss how we leveraged Kafka's partition-by-key feature to this end and how we added this ability in Pinot without any performance degradation.
AWS re:Invent 2016: Building Complex Serverless Applications (GPST404)Amazon Web Services
Provisioning, scaling, and managing physical or virtual servers—and the applications that run on them—has long been a core activity for developers and system administrators. The expanding array of managed AWS cloud services, including AWS Lambda, Amazon DynamoDB, Amazon API Gateway and more, increasingly allows organizations to focus on delivering business value without worrying about managing the underlying infrastructure or paying for idle servers and other fixed costs of cloud services. In this session, we discuss the design, development, and operation of these next-generation solutions on AWS. Whether you're developing end-user web applications or back-end data processing systems, join us in this session to learn more about building your applications without servers.
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Best Practices for Middleware and Integration Architecture Modernization with...Claus Ibsen
What are important considerations when modernizing middleware and moving towards serverless and/or cloud native integration architectures? How can we make the most of flexible technologies such as Camel K, Kafka, Quarkus and OpenShift. Claus is working as project lead on Apache Camel and has extensive experience from open source product development.
The talk was recorded and runs for 30 minutes and published on youtube at: https://www.youtube.com/watch?v=d1Hr78a7Lww
Serverless integration with Knative and Apache Camel on KubernetesClaus Ibsen
This presentation will introduce Knative, an open source project that adds serverless capabilities on top of Kubernetes, and present Camel K, a lightweight platform that brings Apache Camel integrations in the serverless world. Camel K allows running Camel routes on top of any Kubernetes cluster, leveraging Knative serverless capabilities such as “scaling to zero”.
We will demo how Camel K can connect cloud services or enterprise applications using its 250+ components and how it can intelligently route events within the Knative environment via enterprise integration patterns (EIP).
Target Group: Developers, architects and other technical people - a basic understanding of Kubernetes is an advantage
In this session, we’ll discuss the benefits of moving from monolithic to micro-services application architectures, and examine where micro-services can be used. We’ll share common transition strategies and relate them to the specifics of e-commerce and retail workloads, using customer examples. You’ll learn how to build micro-services using AWS services, and get a better understanding of the role of data storage, API endpoints and service discovery. Plus, you can learn from the real-life experience of Digital Goodie, an online retailing platform for connected commerce.
Understanding MicroSERVICE Architecture with Java & Spring BootKashif Ali Siddiqui
This is a deep journey into the realm of "microservice architecture", and in that I will try to cover each inch of it, but with a fixed tech stack of Java with Spring Cloud. Hence in the end, you will be get know each and every aspect of this distributed design, and will develop an understanding of each and every concern regarding distributed system construct.
Learn about what a serverless architecture is, why they are growing in popularity, and who the key players are in a serverless API build on the AWS platform. Then get started building your own servless API!
While many organizations have started to automate their software development processes, many still engineer their infrastructure largely by hand. Treating your infrastructure just like any other piece of code creates a “programmable infrastructure” that allows you to take full advantage of the scalability and reliability of the AWS cloud. This session will walk through practical examples of how AWS customers have merged infrastructure configuration with application code to create application-specific infrastructure and a truly unified development lifecycle. You will learn how AWS customers have leveraged tools like CloudFormation, orchestration engines, and source control systems to enable their applications to take full advantage of the scalability and reliability of the AWS cloud, create self-reliant applications, and easily recover when things go seriously wrong with their infrastructure.
Benefits of Stream Processing and Apache Kafka Use Casesconfluent
Watch this talk here: https://www.confluent.io/online-talks/benefits-of-stream-processing-and-apache-kafka-use-cases-on-demand
This talk explains how companies are using event-driven architecture to transform their business and how Apache Kafka serves as the foundation for streaming data applications.
Learn how major players in the market are using Kafka in a wide range of use cases such as microservices, IoT and edge computing, core banking and fraud detection, cyber data collection and dissemination, ESB replacement, data pipelining, ecommerce, mainframe offloading and more.
Also discussed in this talk are the differences between Apache Kafka and Confluent Platform.
This session is part 1 of 4 in our Fundamentals for Apache Kafka series.
Any team that has made the jump from building monoliths to building microservices knows the complexities you must overcome to build a system that is functional and maintainable. Building a microservice architecture that is low latency and only communicates using REST APIs is even more tricky, with high latency for requests being a common concern. This talk explains how you can use events as the backbone of your microservice architecture and build an efficient, event-driven system. It covers how to get started with designing your microservice architecture and the key requirements any system needs to fulfil. It also introduces the different patterns you will encounter in event-driven architectures and the advantages and disadvantages of these choices. Finally it explains why Apache Kafka is a great choice for event-driven microservices.
by Kashif Imran, Sr. Solutions Architect, AWS
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.
Real-time Analytics with Upsert Using Apache Kafka and Apache Pinot | Yupeng ...HostedbyConfluent
Apache Kafka is used as the primary message bus for propagating events and logs across Uber. In particular, it pairs with Apache Pinot, a real-time distributed OLAP datastore, to deliver real-time insights seconds after the messages produced to Kafka.
One challenge we faced was to update existing data in Pinot with the changelog in Kafka, and deliver an accurate view in the real-time analytical results. For example, the financial dashboard can report gross booking with the corrected Ride fares. And restaurant owners can analyze the UberEats orders with their latest delivery status.
Implementing upserts in an immutable real-time OLAP store like Pinot is nontrivial. We need to make architectural changes in how data is distributed via Kafka amongst the server nodes, how it's indexed and queried in a distributed fashion. In this talk I will discuss how we leveraged Kafka's partition-by-key feature to this end and how we added this ability in Pinot without any performance degradation.
AWS re:Invent 2016: Building Complex Serverless Applications (GPST404)Amazon Web Services
Provisioning, scaling, and managing physical or virtual servers—and the applications that run on them—has long been a core activity for developers and system administrators. The expanding array of managed AWS cloud services, including AWS Lambda, Amazon DynamoDB, Amazon API Gateway and more, increasingly allows organizations to focus on delivering business value without worrying about managing the underlying infrastructure or paying for idle servers and other fixed costs of cloud services. In this session, we discuss the design, development, and operation of these next-generation solutions on AWS. Whether you're developing end-user web applications or back-end data processing systems, join us in this session to learn more about building your applications without servers.
AWS re:Invent 2016: [JK REPEAT] Serverless Architectural Patterns and Best Pr...Amazon Web Services
As serverless architectures become more popular, AWS customers need a framework of patterns to help them deploy their workloads without managing servers or operating systems. This session introduces and describes four re-usable serverless patterns for web apps, stream processing, batch processing, and automation. For each, we provide a TCO analysis and comparison with its server-based counterpart. We also discuss the considerations and nuances associated with each pattern and have customers share similar experiences. The target audience is architects, system operators, and anyone looking for a better understanding of how serverless architectures can help them save money and improve their agility.
AWS re:Invent 2016: Serverless Architectural Patterns and Best Practices (ARC...Amazon Web Services
As serverless architectures become more popular, AWS customers need a framework of patterns to help them deploy their workloads without managing servers or operating systems. This session introduces and describes four re-usable serverless patterns for web apps, stream processing, batch processing, and automation. For each, we provide a TCO analysis and comparison with its server-based counterpart. We also discuss the considerations and nuances associated with each pattern and have customers share similar experiences. The target audience is architects, system operators, and anyone looking for a better understanding of how serverless architectures can help them save money and improve their agility.
If you want to deploy your workloads without the burden of managing servers or operating systems, this webinar is for you. During the session, we will explore four re-usable serverless architectural patterns for supporting web apps, stream processing apps, batch processing apps, and automation apps. For each pattern, we provide a TCO analysis and comparison with the server-based equivalent. We also discuss the considerations and nuances associated with each pattern, with AWS customers sharing their experiences of deploying them. The information covered in the webinar is relevant for architects, system operators, and anyone looking for a better understanding of how serverless architectures can help them save money and improve agility.
Get the EDGE to scale: Using Cloudfront along with edge compute to scale your...Amazon Web Services
You could use Cloud Front to deliver pages faster, however, customized processing still required requests to be forwarded back to compute resources at centralized servers, which may slow down the end user experience. This session shows how a combination of Cloud Front, and edge compute can help you scale out your resources in a much more effective way than you think.
Speaker: Anil Nair
Solution Architect, Amazon India
With AWS Lambda, you can easily build scalable microservices for mobile, web, and IoT applications or respond to events from other AWS services without managing infrastructure. In this session, you’ll see demonstrations and hear more about newly launched features. We’ll show you how to use Lambda to build web, mobile, or IoT backends and voice-enabled apps, and we'll show you how to extend both AWS and third party services by triggering Lambda functions. We’ll also provide productivity and performance tips for getting the most out of your Lambda functions and show how cloud native architectures use Lambda to eliminate “cold servers” and excess capacity without sacrificing scalability or responsiveness.
Getting Started with AWS Lambda and the Serverless Cloud - AWS Summit Cape T...Amazon 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.
AWS Speaker : Danilo Poccia, Technical Evangelist - Amazon Web Services
Do you want to run your code without the cost and effort of provisioning and managing servers? Find out how in this deep dive session on AWS Lambda, which allows you to run code for virtually any type of application or back end service – all with zero administration. During the session, we’ll look at a number of key AWS Lambda features and benefits, including automated application scaling with high availability; pay-as-you-consume billing; and the ability to automatically trigger your code from other AWS services or from any web or mobile app.
Speaker: Raphael Londner, Developer Advocate, MongoDB
Speaker: Paul Sears, Partner Solutions Architect, Amazon Web Services
Level: 200 (Intermediate)
Track: Atlas
In this session, AWS Solutions Architect Paul Sears will provide an overview of AWS Lambda functions, including some key integration use cases with MongoDB Atlas. Developer Advocate Raphael Londner will walk you through how to code a Lambda function connected to MongoDB Atlas, with a specific focus on performance optimization. Raphael will then demonstrate how to orchestrate multiple Lambda functions inside a state machine built on top of AWS Step Functions.
What You Will Learn:
- Common use cases for which MongoDB Atlas + AWS Lambda help you boost developer productivity and minimize operational costs.
- How to write a performance-optimized Lambda function that re-uses MongoDB Atlas database connections across multiple calls in order to speed up queries.
- How AWS Step Functions can help you easily build application workflows to coordinate your Lambda functions.
How to build and deploy serverless apps - AWS Summit Cape Town 2018Amazon Web Services
Speaker: Alex Casalboni, AWS
Customer Speaker: Impression Signatures
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. It means that you can build web, mobile, and IoT backends, run stream processing or big data workloads, build chatbots, run code at the edge, and more. In this session, learn how to get started with serverless computing with AWS Lambda and managed services such as Amazon API Gateway, Amazon Kinesis, and Amazon DynamoDB. We introduce you to the basics of building with AWS Lambda, as well as how to properly perform CI/CD for your serverless application. We will discuss a method for automating the deployment of serverless applications using services such as AWS CodePipeline and AWS CodeBuild, and techniques such as canary deployments and automatic rollbacks.
This session introduces Lambda@Edge, a new AWS Lambda feature that allows developers to perform simple computations at AWS edge locations in response to CloudFront events. This will be of interest to developers who want to build low-latency, customized web experiences. We cover product functionality and details of the programming model, and we walk through potential use cases.
AWS Lambda is a new compute service that runs your code in response to events and automatically manages compute resources for you. In this session you’ll learn what you need to quickly begin building applications that use AWS Lambda as a serverless back-end. We’ll cover key Lambda features, its programming model, key scenarios, and tips on getting the most out of Lambda functions.
AWS Lambda is a compute service that runs your code in response to events and automatically manages the compute resources for you, making it easy to build applications that respond quickly to new information. AWS Lambda starts running your code within milliseconds of an event such as an image upload, in-app activity, website click, or output from a connected device.
The State of Serverless Computing | AWS Public Sector Summit 2017Amazon Web Services
oin us to learn about the state of serverless computing from Dougal Ballantyne, Principal Product Manager, Serverless. Dougal Ballantyne discusses the latest developments from AWS Lambda and the serverless computing ecosystem. He talks about how serverless computing is becoming a core component in how companies build and run their applications and services, and he also discusses how serverless computing will continue to evolve. Learn More: https://aws.amazon.com/government-education/
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.
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.
2. What does serverless mean?
No servers to provision or
manage
Scale with your usage
Built in availability and fault-
tolerance
Never pay for idle/unused
capacity
3. Serverless runs on functions
• Functions are the unit of deployment and scale
• This scales per request!
• Skip the boring parts, skip the hard parts
6. A few Lambda specific best practices
• Lambda is stateless architect accordingly!
• Assume no affinity with underlying compute infrastructure
• Local filesystem and child processes may not extend beyond the lifetime of
the Lambda request
7. Lambda considerations and best practices
• Can your Lambda functions survive the cold?
• Instantiate AWS clients and database
clients outside the scope of the handler to
take advantage of connection re-use.
• Schedule with CloudWatch Events for
warmth
• ENIs for VPC support are attached during
cold start
import sys
import logging
import rds_config
import pymysql
rds_host = "rds-instance"
db_name = rds_config.db_name
try:
conn = pymysql.connect(
except:
logger.error("ERROR:
def handler(event, context):
with conn.cursor() as cur:
Executes during
cold start
Executes with each
invocation
8. Lambda considerations and best practices
• How about a file system?
• Don’t forget about /tmp (512 MB
of scratch space)
exports.ffmpeg = function(event,context) {
new ffmpeg('./thumb.MP4', function (err,
video)
{
if (!err) { video.fnExtractFrameToJPG('/tmp’)
function (error, files) { … }
…
if (!error)
console.log(files);
context.done();
...
9. Lambda considerations and best practices
• Custom CloudWatch metrics
• 40 KB per POST
• Default Acct Limit of 150 TPS
• Consider aggregating with Kinesis
def put_cstate ( iid, state ):
response = cwclient.put_metric_data(
Namespace='AWSx/DirectConnect',
MetricData=[
{
'MetricName':'ConnectionState',
'Dimensions': [
{
'Name': 'ConnectionId',
'Value': iid
},
],
'Value': state,
'Unit': 'None’
16. Serverless web app lifecycle management
• AWS SAM (Serverless Application Model) - blog
AWS
Lambda
Amazon API
Gateway
AWS CloudFormationAmazon
S3
Amazon
DynamoDB
Package &
Deploy
Code/Packages/Swagger
Serverless
Template
Serverless
Template
w/ CodeUri
package deploy
CI/CD Tools
17. A couple words on Amazon API Gateway
• Use mock integrations
• Signed URL from API Gateway for large or binary file uploads to S3
• Use request/response mapping templates for legacy apps and HTTP
response codes
• Asynchronous calls for Lambda > 30s
18. Root/
/{proxy+} ANY Node.js Express
app
• Simple yet very powerful:
• Automatically scale to meet demand
• Only pay for the requests you receive
Greedy variable, ANY method, proxy integration
20. Characteristics of batch processing
• Large data sets
• Periodic or scheduled tasks
• Extract Transform Load (ETL) jobs
• Usually non-interactive and long running
• Many problems fit MapReduce programming model
22. Best practices and things to think about
• Cascade mapper functions
• Lambda languages vs. SQL
• Speed is directly proportional to the concurrent Lambda function limit
• Use DynamoDB/ElastiCache/S3 for intermediate state of mapper
functions
• Lambda MapReduce Reference Architecture
25. Characteristics of stream processing
• High ingest rate
• Near real-time processing (low latency from ingest to process)
• Spiky traffic (lots of devices with intermittent network connections)
• Message durability
• Message ordering
26. Sensors
Amazon Kinesis:
Stream
Lambda:
Stream Processor
S3:
Final Aggregated Output
Lambda:
Periodic Dump to S3
CloudWatch Events:
Trigger every 5 minutes
S3:
Intermediate Aggregated
Data
Lambda:
Scheduled Dispatcher
KPL:
Producer
Serverless stream processing architecture
28. More about fan-out pattern
• Keep up with peak shard capacity
• 1000 records / second, OR
• 1 MB / second
• Consider parallel synchronous Lambda invocations
• Rcoil for JS (https://github.com/sapessi/rcoil) can help
• Dead letter queue to retry failed Lambda invocations
29. Amazon Kinesis Analytics
Sensors
Amazon Kinesis:
Stream
Amazon Kinesis Analytics:
Window Aggregation
Amazon Kinesis Streams
Producer S3:
Aggregated Output
• CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"
• SELECT STREAM "device_id",
• FLOOR("SOURCE_SQL_STREAM_001".ROWTIME TO MINUTE) as "round_ts",
• SUM("measurement") as "sample_sum",
• COUNT(*) AS "sample_count"
• FROM "SOURCE_SQL_STREAM_001"
• GROUP BY "device_id", FLOOR("SOURCE_SQL_STREAM_001".ROWTIME TO MINUTE);
Aggregation Time Window
30. Some event services options
Amazon Kinesis Streams Amazon SQS Amazon SNS
Message Durability Up to retention period Up to retention period Retry delivery (depends on
destination type)
Maximum Retention Period 7 days 14 days Up to retry delivery limit
Message Ordering Strict within shard Standard - Best effort
FIFO – Strict within Message
Group
None
Delivery semantics Multiple consumers per shard Multiple readers per queue (but
one message is only handled by
one reader at a time)
Multiple subscribers per topic
Scaling By throughput using Shards Automatic Automatic
Iterate over messages Shard iterators No No
Delivery Destination Types Kinesis Consumers SQS Readers HTTP/S, Mobile Push, SMS,
Email, SQS, Lambda
31. Some serverless streaming best practices
• Tune batch size when Lambda is triggered by Amazon Kinesis Streams – reduce
number of Lambda invocations
• Tune memory setting for your Lambda function – shorten execution time
• Use KPL to batch messages and saturate Amazon Kinesis Stream capacity
33. Automation characteristics
• Respond to alarms or events
• Periodic jobs
• Auditing and Notification
• Extend AWS functionality
• Highly Available and scalable
34. AWS Lambda:
Update Route53
Amazon CloudWatch Events:
Rule Triggered
Amazon EC2 Instance
State Changes
Amazon DynamoDB:
EC2 Instance Properties
Amazon Route53:
Private Hosted Zone
Tag:
CNAME = ‘xyz.example.com’
xyz.example.com A 10.2.0.134
Automation: dynamic DNS for EC2 instances
39. But seriously, a few tips from someone who
knows what he’s talking about
• Serverless monolith: frameworks like Zappa or Serverless that just create a single
package and route all requests to the one package
• Easy to port existing applications
• Works well with traditional App level logging and monitoring
• Easy to keep all endpoints warm since everything is hooked up
• Start with a monolith then move out individual endpoints as things break
• If you’re building something greenfield you can do the managed endpoints
pattern with a framework like chalice - each APIGW endpoint gets created, but
it’s still a single app deployment
• Then there’s 1:1 every endpoint gets its own function- makes logging and
introspection a nightmare but gives extreme agility for parallel development
40. But seriously, a few tips from someone who
knows what he’s talking about
• On the non-web app side: Glue pattern
• Glue is what 99% of lambda deployments are about - taking events from one
service and doing something with them in another
• If services are bricks then lambda is mortar
41. Other resources
• Randall <3s Lambda!
• @jrhunt on Twitter
• Tons of examples and projects here: https://github.com/ranman
• AWS documentation:
http://docs.aws.amazon.com/lambda/latest/dg/welcome.html
• Tons of compute blog posts:
https://aws.amazon.com/blogs/compute/category/aws-lambda/
• Lambda reference architecture: https://github.com/awslabs/lambda-
refarch-webapp