Measure and Increase Developer Productivity with Help of Serverless at JCON 2021

With AWS, companies now have the ability to develop and run their applications with speed and flexibility like never before. Working with an infrastructure that can be 100 percent API driven enables businesses to use lean methodologies and realize these benefits. This in turn leads to greater success for those who make use of these practices. In this session, we talk about some key concepts and design patterns for continuous deployment and continuous integration, two elements of lean development of applications and infrastructures.

Agenda 1. The changing landscape of IT Infrastructure 2. Containers - An introduction 3. Container management systems 4. Kubernetes 5. Containers and DevOps 6. Future of Infrastructure Mgmt About the talk In this talk, you will get a review of the components & the benefits of Container technologies - Docker & Kubernetes. The talk focuses on making the solution platform-independent. It gives an insight into Docker and Kubernetes for consistent and reliable Deployment. We talk about how the containers fit and improve your DevOps ecosystem and how to get started with containerization. Learn new deployment approach to effectively use your infrastructure resources to minimize the overall cost.

A walkthrough of the recently released update to ShapeBlue’s CloudStack Container Service (CCS). This update brings CCS bang up-to-date by running the latest version of Kubernetes (v1.11.3) on the latest version of Container Linux. CCS also now makes use of CloudStack’s new CA framework to automatically secure the Kubernetes environments it creates.

This document discusses best practices for continuous integration and deployment on AWS. It recommends using AWS services like CodeCommit for source code repositories, CodeBuild for continuous integration, CodeDeploy for deployments, and CodePipeline for automated workflows. Continuous integration helps catch bugs early by frequently integrating code changes. Continuous deployment further automates releasing code to production multiple times a day through feature flags and A/B testing, allowing for rapid iteration and feedback from real users.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times and high memory footprint. For both you have to pay to the cloud providers of your choice. That's why most developers tried to avoid using Java for such use cases. But the times change: Community and cloud providers improve things steadily for Java developers. In this talk we look at the features and possibilities AWS cloud provider offers for the Java developers and look the most popular Java frameworks, like Micronaut, Quarkus and Spring (Boot) and look how (AOT compiler and GraalVM native images play a huge role) they address Serverless challenges and enable Java for broad usage in the Serverless world.

This document summarizes an event for the CloudStack European User Group that was held on December 13, 2018 in London. The agenda included welcome remarks from the group chairman, several technical presentations on CloudStack topics from various speakers, and discussions around collaborative opportunities for CloudStack users. Breaks were scheduled throughout the day for networking. The event was sponsored and aimed to provide a forum for sharing ideas, case studies, and addressing problems among the CloudStack user community.

The document previews ShapeBlue's CloudStack Backup and Recovery Framework, which aims to provide a vendor-agnostic API and UI in CloudStack for third-party backup and recovery solutions. The framework abstracts vendor specifics through plugins so solutions can deliver features like scheduled, ad-hoc, and policy-based backups as well as VM and volume restoration. An example plugin for Veeam Backup & Replication is provided. The framework and initial plugins are targeted for an open source release in Q4.

Cloud migration: it's practically a rite of passage for anyone who's built infrastructure on bare metal. When we migrated our 5-year-old Kafka deployment from the datacenter to GCP, we were faced with the task of making our highly mutable server infrastructure more cloud-friendly. This led to a surprising decision: we chose to run our Kafka cluster on Kubernetes. I'll share war stories from our Kafka migration journey, explain why we chose Kubernetes over arguably simpler options like GCP VMs, and present the lessons we learned while making our way toward a stable and self-healing Kubernetes deployment. I'll also go through some improvements in the more recent Kafka releases that make upgrades crucial for any Kafka deployment on immutable and ephemeral infrastructure. You'll learn what happens when you try to run one complex distributed system on top of another, and come away with some handy tricks for automating cloud cluster management, plus some migration pitfalls to avoid. And if you're not sure whether running Kafka on Kubernetes is right for you, our experiences should provide some extra data points that you can use as you make that decision.

Bootstrapping a Kubernetes cluster is easy, rolling it out to nearly 200 engineering teams and operating it at scale is a challenge. In this talk, we are presenting our approach to Kubernetes provisioning on AWS, operations and developer experience for our growing Zalando Technology department. We will highlight in the context of Kubernetes: AWS service integrations, our IAM/OAuth infrastructure, cluster autoscaling, continuous delivery and general developer experience. The talk will cover our most important learnings and we will openly share failure stories. Presented on 2017-09-28 at AWS Tech Community Days in Cologne.

Organizations around the globe are leveraging the cloud to accomplish world-changing missions. This session will address how AWS can help organizations put more money toward their mission and scale outreach and operations to achieve more with less. Hear some of AWS’s most advanced customers on how their organizations handle DevOps, continuous integration and deployment. Learn how these practices allow them to rapidly develop, iterate, test and deploy highly-scalable web applications and core operational systems on AWS. The discussion will focus on best practices, lessons learned, and the specific technologies and services they use.

Container Orchestration 之爭已經落幕，Kubernetes 成為主流，AWS, Azure 跟 GCP 都已提出相對應的解決方案， 但該選擇廠商所提供的服務或是自己架設呢？如何把 Stateless 甚至是 Stateful 應用服務運行於其上呢？部署應用程式到 Kubernetes 之中該如何做比較好？本分享談及多次在公司導入及維運 Kubernetes 的相關經���，讓有興趣或是剛使用的人可以減少摸索的時間

In this knolx, I'll be discussing about the components involved in setting up an EC2 instance and how we can achieve it via Terraform

This document describes a CI/CD pipeline for automating deployment of Python code and notebooks to Azure Databricks. The pipeline uses Pre-Commit hooks to run linters and tests on commits. If tests pass, a Python wheel is built and published to Azure DevOps artifacts. The pipeline then copies the version file to the development workspace and copies the full notebook folder to production, allowing installation of the specific library version in notebooks. The goal is continuous deployment with testing at each stage to reliably deploy small code changes.

Presented at AI NEXTCon Seattle 1/17-20, 2018 http://aisea18.xnextcon.com join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..

This document summarizes Scott Miao's presentation on Analytic Engine (AE), a common big data computation service on AWS. AE provides a RESTful API for users to create AWS EMR clusters, submit jobs to clusters, and delete clusters. It handles job scheduling and delivery to clusters to optimize usage of AWS resources. Using AE and AWS services like EMR and S3 allows Trend Micro to scale their data and computation needs elastically with reduced operational overhead compared to managing infrastructure on their own.

Minimizing customer impact is a key feature in successfully rolling out frequent code updates. Learn how to leverage the AWS cloud so you can minimize bug impacts, test your services in isolation with canary data, and easily roll back changes. Learn to love deployments, not fear them, with a blue/green architecture model. This talk walks you through the reasons it works for us and how we set up our AWS infrastructure, including package repositories, Elastic Load Balancing load balancers, Auto Scaling groups, internal tools, and more to help orchestrate the process. Learn to view thousands of servers as resources at your command to help improve your engineering environment, take bigger risks, and not spend weekends firefighting bad deployments.

The document discusses introducing modern monitoring techniques using Prometheus. It covers defining metrics and alerts, implementing Prometheus and exporters, designing dashboards and alerts, and configuring alert routing and templates. The goal is to improve on traditional monitoring approaches by implementing application-level metrics collection and monitoring multiple dimensions of metrics for better visibility.

The goal of Serverless is to focus on writing the code that delivers business value and offload everything else to your trusted partners (like Cloud providers or SaaS vendors). You want to iterate quickly and today’s code quickly becomes tomorrow’s technical debt. In this talk we will show why Serverless adoption increases the developer productivity and how to measure it. We will also go through AWS Serverless architectures where you only glue together different Serverless managed services relying solely on configuration, minimizing the amount of the code written.

TCO of Serverless application. How Serverless helps us to be productive, write less code and implement evolutionary architectures. How to measure productivity to see you're on track with Serverless

This document discusses increasing developer productivity through serverless computing. It begins by outlining various types of cognitive load on developers and how serverless can help minimize extraneous load. It then discusses how technical debt and inability to evolve can reduce productivity. Serverless is presented as helping reduce technical debt through writing less code and fewer dependencies. The total cost of ownership advantages of serverless are covered, including no infrastructure maintenance, built-in auto-scaling, ability to do more with fewer resources, lower technical debt, and faster time to market. Best practices like evolutionary architecture, DevOps, and chaos engineering are discussed for effectively leveraging serverless. Recent improvements to serverless offerings from AWS are summarized.

The goal of Serverless is to focus on writing the code that delivers business value and offload everything else to your trusted partners (like Cloud providers or SaaS vendors). You want to iterate quickly and today’s code quickly becomes tomorrow’s technical debt. In this talk we will show why Serverless adoption increases the developer productivity and how to measure it. We will also go through AWS Serverless architectures where you only glue together different Serverless managed services relying solely on configuration, minimizing the amount of the code written.

The goal of Serverless is to focus on writing the code that delivers business value and offload everything else to your trusted partners (like Cloud providers or SaaS vendors). You want to iterate quickly and today’s code quickly becomes tomorrow’s technical debt. In this talk we will show why Serverless adoption increases the developer productivity and how to measure it. We will also go through AWS Serverless architectures where you only glue together different Serverless managed services relying solely on configuration, minimizing the amount of the code written.

The purpose of Serverless is to focus on writing the code that delivers business value and offload undifferentiated heavy lifting to the Cloud providers or SaaS vendors of your choice. Today’s code quickly becomes tomorrow’s technical debt even if you meet the perfect decision. The less you own, the better it is from the maintainability point of view. In this talk I will go through examples of the various Serverless architectures on AWS where you glue together different Serverless managed services relying mostly on configuration, significantly reducing the amount of the code written to perform the task. Own less, build more!

The purpose of Serverless is to focus on writing the code that delivers business value and offload undifferentiated heavy lifting to the Cloud providers or SaaS vendors of your choice. Today’s code quickly becomes tomorrow’s technical debt even if you meet the perfect decision. The less you own, the better it is from the maintainability point of view. In this talk I will go through examples of the various Serverless architectures on AWS where you glue together different Serverless managed services relying mostly on configuration, significantly reducing the amount of the code written to perform the task. Own less, build more!

The purpose of Serverless is to focus on writing the code that delivers business value and offload undifferentiated heavy lifting to the Cloud providers or SaaS vendors of your choice. Today’s code quickly becomes tomorrow’s technical debt even if you meet the perfect decision. The less you own, the better it is from the maintainability point of view. In this talk I will go through examples of the various Serverless architectures on AWS where you glue together different Serverless managed services relying mostly on configuration, significantly reducing the amount of the code written to perform the task. Own less, build more!

TCO of Serverless application. How Serverless helps us to be productive, write less code and implement evolutionary architectures. How to measure productivity to see you're on track with Serverless

The purpose of Serverless is to focus on writing the code that delivers business value and offload undifferentiated heavy lifting to the Cloud providers or SaaS vendors of your choice. Today’s code quickly becomes tomorrow’s technical debt even if you meet the perfect decision. The less you own, the better it is from the maintainability point of view. In this talk I will go through examples of the various Serverless architectures on AWS where you glue together different Serverless managed services relying mostly on configuration, significantly reducing the amount of the code written to perform the task. Own less, build more!

DevOps, Continuous Integration & Deployment on AWS discusses practices for software development on AWS including DevOps, continuous integration, continuous delivery, and continuous deployment. It provides an overview of AWS services that can be used at different stages of the software development lifecycle such as CodeCommit for source control, CodePipeline for release automation, and CodeDeploy for deployment. National Novel Writing Month (NaNoWriMo) maintains its websites and services on AWS to support its annual writing challenge. It migrated to AWS to improve uptime and scalability. Its future goals include porting older sites to Rails, using Amazon SES for email, load balancing with ELB, implementing auto scaling, and using services like CodeDeploy, SNS

FoundationDB is a next-generation database that aims to provide high performance transactions at massive scale through a distributed design. It addresses limitations of NoSQL databases by providing a transactional, fault-tolerant foundation using tools like the Flow programming language. FoundationDB has demonstrated high performance that exceeds other NoSQL databases, and provides ease of scaling, building abstractions, and operation through its transactional design and automated partitioning. The goal is to solve challenges of state management so developers can focus on building applications.

When we talk about prices, we often only talk about Lambda costs. In our applications, however, we rarely use only Lambda. Usually we have other building blocks like API Gateway, data sources like SNS, SQS or Kinesis. We also store our data either in S3 or in serverless databases like DynamoDB or recently in Aurora Serverless. All of these AWS services have their own pricing models to look out for. In this talk, we will draw a complete picture of the total cost of ownership in serverless applications and present a decision-making list for determining if and whether to rely on serverless paradigm in your project. In doing so, we look at the cost aspects as well as other aspects such as understanding application lifecycle, software architecture, platform limitations, organizational knowledge and plattform and tooling maturity. We will also discuss current challenges adopting serverless such as lack of high latency ephemeral storage, unsufficient network performance and missing security features.

SaaS architectures can be deployed onto AWS in a number of ways, and each optimizes for different factors from security to cost optimization. Come learn more about common deployment models used on AWS for SaaS architectures and how each of those models are tuned for customer specific needs. We will also review options and tradeoffs for common SaaS architectures, including cost optimization, resource optimization, performance optimization, and security and data isolation.

Migrations of existing enterprise applications to the cloud can be complex. There are no migration methodologies or magic bullets that enable a simple lift and shift or automated migration. Typical migration projects take a great deal of discovery work, re-architecture, and refactoring. In this session, we will share known challenges and considerations that must be accounted for when designing, planning, and executing a migration. Topics will include: scale-out and distributed architectures, geographic dispersion, leveraging existing cloud services, and logging & monitoring. In addition, this session will address how in-depth discovery efforts can be paired with configuration management, automation, and source control to minimize the risk of future technical debt. Finally, we’ll cover the business and technical factors the affect the complexity of application refactoring.

Migrations of existing enterprise applications to the cloud can be complex. There are no migration methodologies or magic bullets that enable a simple lift and shift or automated migration. Typical migration projects take a great deal of discovery work, re-architecture, and refactoring. In this session, we will share known challenges and considerations that must be accounted for when designing, planning, and executing a migration. Topics will include: scale-out and distributed architectures, geographic dispersion, leveraging existing cloud services, and logging & monitoring. In addition, this session will address how in-depth discovery efforts can be paired with configuration management, automation, and source control to minimize the risk of future technical debt. Finally, we’ll cover the business and technical factors the affect the complexity of application refactoring.

Presentazione dello speech tenuto da Carmine Spagnuolo (Postdoctoral Research Fellow - Università degli Studi di Salerno/ ACT OR) dal titolo "Technology insights: Decision Science Platform", durante il Decision Science Forum 2019, il più importante evento italiano sulla Scienza delle Decisioni.

Java Agile ALM: OTAP and DevOps in the Cloud Bas Van Oudenaarde job Technical Manager at VX Company.

The document discusses containers, microservices, and serverless applications for developers. It provides an overview of these topics, including how containers and microservices fit into the DevOps paradigm and allow for better collaboration between development and operations teams. It also discusses trends in container usage and orchestration as well as differences between platforms as a service (PaaS) and serverless applications.

Create a highly available environment to host your microservices using Node.js, Docker, Kubernetes, and Ansible.

ip.labs is the world's leading white label e-commerce software imaging company and processes millions of images every day. The workflows of our users consist of designing, saving, loading, ordering and delivering to the printing facilities the photo products like prints, photobooks, calendars, gift products among others. In this talk we'll explore the challenges of our previous solution based on ALB, EC2, EBS and EFS, our motivation and architecture behind the reimplementation of our image storage solution based on AWS Serverless services like API Gateway, Lambda, DynamoDB, SQS, SNS, EventBridge and others, benefits that we've got with this new solution but also challenges we needed to overcome and trade-offs we had to make.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.

In this talk, we’ll use a standard serverless application that uses API Gateway, Lambda, DynamoDB, SQS, Step Functions (and other AWS-managed services). We'll explore how Amazon DevOps Guru recognizes operational issues and anomalies like increased latency and error rates (timeouts, throttling, and resource limits) and integrate DevOps Guru with PagerDuty to provide even better incident management. Amazon DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. The service uses pre-trained ML models that are able to identify spikes in application requests, so it knows when to alert and when not to.

There is a misunderstanding that everything is possible with the Serverless Services in AWS. For example, the misunderstanding that your Lambda function may scale without limitations. But each AWS service (not only Serverless) has a big list of quotas that everybody needs to be aware of, understand, and take into account during the development. In this talk, I'll explain the most important quotas (in terms of scaling, but not only that) of Serverless services like API Gateway, Lambda, DynamoDB, SQS, and Aurora Serverless and how to architect your solution with these quotas in mind.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times which may heavily impact the latencies of your application. But the times change: Community and AWS as a cloud providers improve things steadily for Java developers. In this talk we look at the best practices, features and possibilities AWS offers for the Java developers to reduce the cold start times like GraalVM Native Image and AWS Lambda SnapStart based on CRaC (Coordinated Restore at Checkpoint) project.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times which may heavily impact the latencies of your application. But the times change: Community and AWS as a cloud providers improve things steadily for Java developers. In this talk we look at the best practices, features and possibilities AWS offers for the Java developers to reduce the cold start times like GraalVM Native Image and AWS Lambda SnapStart based on on FirecrackerVM snapshot and CRaC (Coordinated Restore at Checkpoint) project.

This document summarizes Amazon CodeCatalyst and DevOps Guru, which help revolutionize the DevOps lifecycle. Amazon CodeCatalyst allows developers to create serverless projects that include code, development environments, CI/CD pipelines, and issue/report tracking. DevOps Guru uses machine learning to detect operational issues in services like DynamoDB, API Gateway, and Lambda by analyzing metrics to find anomalies and reduce human intervention. It provides both reactive insights for existing issues and proactive insights to predict future problems.

In this talk we’ll use a standard Serverless application which uses of API Gateway, Lambda, DynamoDB, SQS, Step Functions (and other AWS managed services) and explore how Amazon DevOps Guru recognizes operational issues like increased latency and error rates (timeouts, throttling and resource limits) and integrate DevOps Guru with PagerDuty for providing even better incident management. Amazon DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. The service uses pre-trained ML models that are able to identify spikes in application requests, so it knows when to alert and when not to.

There is a misunderstanding, that everything is possible with the Serverless Services in AWS, for example that your Lambda function may scale without limitations . But each AWS service (not only Serverless) has a big list of quotas that everybody needs to be aware of, understand and take into account during the development. In this talk I'll explain the most important quotas of the Serverless Services like API Gateway, Lambda, DynamoDB, SQS and Aurora Serverless and how to architect your solution with these quotas in mind.