"It’s not only Lambda! Economics behind Serverless" at Serverless Architecture Conference April 2019 by Christian Bannes and Vadym Kazulkin (both ip.labs GmbH)

Discussion of Azure web apps, App Insights, "Azure Functions in the real world", ARM templates, queues, BLOB storage and more. Includes a video demo of AAD-secured Azure Function called from a SharePoint Framework (SPFx) web part with SPO cookie auth.

The document outlines the many decisions involved in installing Primavera software, including choosing an operating system, database, application server, and other configuration options. It provides an example installation path using Windows Server 2008 R2 x64, SQL Server 2008, and native user accounts. The summary recommends seeking support early in the process given the numerous options and complexity of a full Primavera installation.

This document provides an introduction to web components and discusses their benefits. It explains that web components bring a native component model to HTML, allowing for reusable UI functionality both within and across applications. The document demonstrates several types of web components, including custom elements, HTML templates, HTML imports, and shadow DOM. It also discusses browser support for web components and strategies for improving support, such as using polyfills.

A presentation I gave at SharePoint Evolutions 2015. Here, I compare SharePoint apps (now renamed "SharePoint Add-Ins" as of April 2015!) and the newer flavour of app development, Office 365 apps. It focuses primarily on the perspective of a development team implementing the app - and factors to consider when deciding between the two approaches. However, to do this we must consider end-user and administration aspects, as well as code/development. Key agenda points: - Changes in SharePoint development - Apps, 2 years on.. - SharePoint Add-Ins – a recap - Office 365 apps - Why did Microsoft introduce these? What do they promise? - Comparing SharePoint Add-Ins with Office 365 apps - For the end-user, administrator and developer - Summary

s it possible to build a Intranet with the new Modern SharePoint, what is the downs and ups for this. Or would it be better to use a solution from a vendor?

PowerShell is a task automation and configuration management framework consisting of a command-line shell and scripting language built on .NET. It provides full access to COM and WMI to perform administrative tasks on Windows systems locally and remotely. The presentation introduces PowerShell, its history and uses, basic concepts like cmdlets and the pipeline, best practices like using comment-based help and variables, and includes examples of PowerShell scripts.

This document discusses microservices, APIs, messaging, and serverless computing on Azure. It provides an overview of RPC and REST API styles, services like Service Bus, Event Grid, and Azure Functions. It also covers containerization with Docker and deploying containerized applications to Azure.

[John White, Jason Himmelstein] On premises or in the cloud, rich data visualizations are essential to most business processes an decisions. But frequently, key data may live on premises, although tools like Power BI provide a cloud based surface to view dashboards and reports. Join us for an overview of how to extend on premises data to the cloud and how to integrate cloud-based visualizations back into Microsoft SharePoint on premises. We'll be talking about a wide variety of tools including SQL Server Reporting Services, Power BI, SharePoint (both on-premises and in the cloud), Flow and PowerApps.

The document provides guidance on designing APIs for server applications. It discusses using the appropriate HTTP verbs like GET, POST, PUT, and DELETE based on the database operation. It emphasizes the importance of documentation, de-normalizing data into top-level models, versioning models over time, and implementing caching at the server through expiration and validation models. The document uses examples to illustrate best practices for API design.

[Frank Carius] Skype for Business is now Teams. This message from ignite 2017 might work for Cloud based Services in 2018. Lets summarize the current situation of voice services by microsft, the state of CCE, SIP-Trunks, Teams and telephony and how.

The document discusses PowerPivot for SharePoint. It provides details on how PowerPivot works, including how the PowerPivot workbook is rendered. It describes components like PowerPivot Services and Analysis Services. It also covers installing and configuring PowerPivot for SharePoint farms.

Most large-scale web companies have evolved their system architecture from a monolithic application and monolithic database to a set of loosely coupled micro-services. Using examples from Google, eBay, and KIXEYE, this talk outlines the pros and cons of these different stages of evolution, and makes practical suggestions about when and how other organizations should consider migrating to micro-services. It concludes with some more advanced implications of a micro-services architecture, including SLAs, cost-allocation, and vendor-customer relationships within the organization.

aka "Wisdom from the Masters, learned the Hard Way" Disclaimer: not an AWS talk. All views expressed are my own only.

The document discusses various techniques for working with SharePoint, Microsoft Teams, Azure Functions, and the Microsoft Graph from applications such as SharePoint Framework (SPFx) solutions, PowerApps, and Microsoft Flow. Specific techniques mentioned include calling a secured Azure Function from SPFx; using site designs and PnP templates to provision sites; capturing images from PowerApps and storing them in SharePoint; and using the Microsoft Graph SDK within SPFx solutions, Azure Functions, and other applications. The document provides code examples and references to documentation for implementing these techniques.

This webinar discusses five early challenges of building streaming fast data applications: 1) choosing among alternative streaming frameworks like Kafka Streams, Spark Streaming, and Flink; 2) integrating microservices with streaming services; 3) understanding operational challenges of streaming services; 4) gaining competitive advantage through machine learning on fast data; and 5) optimizing resource utilization across large clusters running many components. The webinar promotes Lightbend's Fast Data Platform as providing an easy on-ramp and complete solution for these challenges.

[Elio Struyf] We all have these daily tasks that can be automated. Like checking if the backup job of your site completed, or looking how many times a file has been accessed, etc. These kinds of tasks are great to be automated by an Azure Functions. In this session, you will get an overview of what Azure Functions can do for you. With some demos, we go step by step through the creation, debugging and deployment process of these functions.

Serverless isn’t a silver bullet. I’ll provide decision checklist to figure out whether serverless it the right approach for your application or not which consists of understanding of your: - Application lifecycle - Workloads - Platform limitations - Cost at scale - Organizational knowledge I will also discuss current challenges adopting serverless: - Lack of High Latency Ephemeral Storage - Poor Network performance - Missing Security Improvements

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.

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.

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.

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.

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!

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.

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

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

hen 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.

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!

This document provides an agenda and summaries of key points from a presentation on integrating systems using Apache Camel. The presentation discusses how Apache Camel is an open-source integration library that uses enterprise integration patterns to connect disparate systems. It highlights features of Camel including components, data formats, and testing frameworks. Customer examples are presented that demonstrate large returns on investment and cost savings from using Camel for integration projects. The presenters argue that Camel provides flexibility, reusability and rapid development of integrations.

1) The document discusses best practices for running microservices at scale, including breaking monolithic architectures into loosely coupled microservices, using the right tools for each job, securing services, focusing on organizational transformation, and automating everything. 2) Five principles for running microservices are outlined: microservices only rely on each other's public APIs, using the right tool for the job, securing services with defense-in-depth, focusing on cross-functional teams for alignment, and automating everything. 3) Examples of event-driven serverless architectures using AWS Lambda and other AWS services are provided.

The document discusses serverless computing and key investment opportunities in the space. Serverless computing refers to cloud-based event-driven computing where the cloud provider manages the infrastructure. The document outlines the benefits of serverless computing including ease of scaling, reduced costs, and increased productivity. It also discusses some challenges around vendor lock-in, lack of control, and multitenancy. The document identifies serverless monitoring, security, and infrastructure as key investment areas. It provides overviews of serverless monitoring companies IOPipe and Dashbird which provide tools to monitor and debug serverless applications.

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 document discusses the state of serverless computing on AWS. It begins by explaining what serverless computing is and how it has evolved from physical servers to virtual servers in data centers to virtual servers in the cloud. It then discusses some of the key benefits of serverless computing such as no server management, automatic scaling, and pay per use. The document outlines some common use cases for serverless applications including web apps, backends, media processing, and big data workloads. It also provides examples of large customers using AWS Lambda at scale. Finally, it discusses some of the building blocks that enable serverless applications on AWS such as Lambda, API Gateway, DynamoDB, and others.

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 document discusses various ways to compromise an Azure Active Directory (Azure AD) environment. It describes how Azure AD roles, applications, and service principals work and how their complex permission systems can be leveraged. It also explains how linking an on-premise Active Directory to Azure AD via Azure AD Connect, as well as other Azure integrations like Azure DevOps, can be exploited to escalate privileges and access cloud resources. The document emphasizes that while cloud services provide benefits, they also require users to securely configure and manage access themselves to protect their data and environments.

- PyWren-IBM is a Python framework that allows users to easily scale Python code across serverless platforms like IBM Cloud Functions without having to learn the underlying storage or function as a service APIs. - It addresses challenges like how to integrate existing applications and workflows with serverless computing, how to process large datasets without becoming a storage expert, and how to scale code without major disruptions. - The document discusses use cases for PyWren-IBM like Monte Carlo simulations, protein folding, and stock price prediction that demonstrate how it can be used for high performance computing workloads.

Join us to learn about the state of serverless computing from Dr. Tim Wagner, General Manager of AWS Lambda. Dr. Wagner 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.

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/

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.

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 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 Image play a huge role) they address Serverless challenges and enable Java for broad usage in the Serverless world. We'll also look into AWS Lambda SnapStart feature based on CRaC (Coordinated Restore at Checkpoint) project which also reduces the cold start time of Java Serverless application on AWS. We also look into the tools which help us figure out the optimal balance between Lambda memory footprint, invocation time and execution cost.

The document compares GitHub Copilot, Amazon CodeWhisperer, and ChatGPT for Java developers. It provides an overview of each tool, compares their programming language support, IDE support, and pricing. It demonstrates their abilities for general tasks, simple functions, more complex algorithms, JUnit testing, and Spring Boot web development. It concludes that while the tools provide helpful suggestions, developers are still needed to ensure correctness and efficiency. GitHub Copilot and ChatGPT benefit from OpenAI, while Amazon CodeWhisperer needs quality improvements for Java but may leverage AWS services.

Vadym Kazulkin presented on AWS Lambda SnapStart, which aims to reduce cold start times for Java functions on AWS Lambda. SnapStart works by saving snapshots of the JVM state during a "priming" invocation and restoring it on subsequent cold starts. This can reduce cold start times from seconds to milliseconds. However, SnapStart currently only supports Java 11/17 runtimes and some limitations remain around deployment time and full cold start measurement. Priming the application before taking snapshots can further reduce cold starts but with tradeoffs around additional Lambda costs and optimization time.

I will introduce two AWS services: CodeGuru and DevOps Guru. CodeGuru Reviewer uses ML and automated reasoning to automatically identify critical issues, security vulnerabilities, and hard-to-find bugs during application development. DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. It does this by having the ability to correlate and group metrics together to understand the relationships between those metrics, so it knows when to alert.

In this talk we’ll build a standard Serverless application which uses of API Gateway, Lambda and DynamoDB and explore how Amazon DevOps Guru recognizes operational issues like increased latency and error rates (timeouts and throttles) 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.