Beyond static configuration management discusses how containerization and distributed configuration management are disrupting traditional system engineering. Key developments include specialized container-centric operating systems like CoreOS, orchestration tools like Docker, Mesos, and Kubernetes, as well as configuration stores like etcd, Consul, and Zookeeper that enable dynamic configuration of distributed systems. The talk argues this represents an exciting transition period for development and operations.
This document provides an introduction and overview of Kubernetes for deploying and managing containerized applications at scale. It discusses Kubernetes' key features like self-healing, dynamic scaling, networking and efficient resource usage. It then demonstrates setting up a Kubernetes cluster on AWS and deploying a sample application using pods, deployments and services. While Kubernetes provides many benefits, the document notes it requires battle-testing to be production-ready and other topics like logging, monitoring and custom autoscaling solutions would need separate discussions.
3 years ago, Meetic chose to rebuild it's backend architecture using microservices and an event driven strategy. As we where moving along our old legacy application, testing features became gradually a pain, especially when those features rely on multiple changes across multiple components. Whatever the number of application you manage, unit testing is easy, as well as functional testing on a microservice. A good gherkin framework and a set of docker container can do the job. The real challenge is set in end-to-end testing even more when a feature can involve up to 60 different components.
To solve that issue, Meetic is building a Kubernetes strategy around testing. To do such a thing we need to :
- Be able to generate a docker container for each pull-request on any component of the stack
- Be able to create a full testing environment in the simplest way
- Be able to launch automated test on this newly created environment
- Have a clean-up process to destroy testing environment after tests To separate the various testing environment, we chose to use Kubernetes Namespaces each containing a variant of the Meetic stack. But when it comes to Kubernetes, managing multiple namespaces can be hard. Yaml configuration files need to be shared in a way that each people / automated job can access to them and modify them without impacting others.
This is typically why Meetic chose to develop it's own tool to manage namespace through a cli tool, or a REST API on which we can plug a friendly UI.
In this talk we will tell you the story of our CI/CD evolution to satisfy the need to create a docker container for each new pull request. And we will show you how to make end-to-end testing easier using Blackbeard, the tool we developed to handle the need to manage namespaces inspired by Helm.
- Play 2.0 is a web framework for Java and Scala that simplifies development by embracing HTTP rather than fighting it
- It takes a new approach to building web apps in Java by not being built on top of servlet APIs and using an asynchronous programming model
- Developing, testing, and deploying a Play app locally and to CloudFoundry involves creating a project, running it locally, and pushing the compiled code to CloudFoundry which automatically detects and supports Play apps
(Draft) Kubernetes - A Comprehensive OverviewBob Killen
Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery called pods. Its main components include a master node that manages the cluster and worker nodes that run the applications. It uses labels to organize resources and selectors to group related objects. Common concepts include pods, services for discovery/load balancing, replica controllers for scaling, and namespaces for isolation. It provides mechanisms for configuration, storage, security, and networking out of the box to ensure containers can run reliably and be easily managed at scale.
The document discusses Docker's platform and ecosystem. It describes Docker's mission to build tools for mass innovation by providing a software layer to program the internet. It outlines key components of Docker including Docker Engine, Swarm for clustering multiple Docker hosts, Compose for defining and running multi-container applications, and Docker Hub for sharing images. It also discusses the Linux container ecosystem underpinning Docker and roadmaps for continued development.
This is a presentation I held at "DevOps and Security" -meetup on 5th of April 2016 at RedHat.
Source is available at: https://github.com/jerryjj/devsec_050416
Orchestration tool roundup kubernetes vs. docker vs. heat vs. terra form vs...Nati Shalom
Video recording: https://www.youtube.com/watch?v=tGlIgUeoGz8
It’s no news that containers represent a portable unit of deployment, and OpenStack has proven an ideal environment for running container workloads. However, where it usually becomes more complex is that many times an application is often built out of multiple containers. What’s more, setting up a cluster of container images can be fairly cumbersome because you need to make one container aware of another and expose intimate details that are required for them to communicate which is not trivial especially if they’re not on the same host.
These scenarios have instigated the demand for some kind of orchestrator. The list of container orchestrators is growing fairly fast. This session will compare the different orchestation projects out there - from Heat to Kubernetes to TOSCA - and help you choose the right tool for the job.
Session link from teh summit: https://openstacksummitmay2015vancouver.sched.org/event/abd484e0dedcb9774edda1548ad47518#.VV5eh5NViko
The Operator Pattern - Managing Stateful Services in KubernetesQAware GmbH
Cloud Native Night, January 2018, Mainz: Talk by Jakob Karalus (@krallistic, IT Consultant at codecentric)
Join our Meetup: https://www.meetup.com/de-DE/Cloud-Native-Night
Abstract: While it's easy to deploy stateless application with Kubernetes, it's harder for stateful software. Since applications often require custom functionality that Kubernetes can't provide, developers want to add more specialized patterns like automatic backups, failover or rebalancing to their Kubernetes deployments. In this talk, we will look at the Operator Pattern and other possibilities to extend the functionality of Kubernetes and how to use them to operate stateful applications.
Scaling Development Environments with DockerDocker, Inc.
This document discusses using Docker to create a scalable development environment. It outlines setting up containers for different development components like the build server, production servers, and tools. Templates are used to configure container dependencies and build processes. The goal is allowing developers to run all components locally for testing and to reproduce the production environment.
Orchestrating Linux Containers while tolerating failuresDocker, Inc.
lthough containers are bringing a refreshing flexibility when deploying services in production, the management of those containers in such an environment still requires special care in order to keep the application up and running. In this regard, orchestration platforms like Docker, Kubernetes and Nomad have been trying to alleviate this responsibility, facilitating the task of deploying and maintaining the entire application stack in its desired state. This ensures that a service will be always running, tolerating machine failures, network erratic behavior or software updates and downtime. The purpose of this talk is to explain the mechanisms and architecture of the Docker Engine orchestration platform (using a framework called swarmkit) to tolerate failures of services and machines, from cluster state replication and leader-election to container re-scheduling logic when a host goes down.
This document provides an overview and demonstration of Clocker, an open source tool for managing Docker clouds and deploying composite applications on Docker. It discusses Clocker's components including its use of Brooklyn for application management and jclouds for provisioning. It also covers Clocker's features such as container placement strategies, networking using Weave, and roadmap items like support for Docker Swarm and improved networking.
Service Discovery in kubernetes is all about how services of kubernetes get discovered internally and externally. How does a single POD communicate to another POD the within the cluster and how does a user request reach to a specific POD in the cluster? These are some questions that are answered by this TOPIC.
This document discusses developing, delivering, and running Oracle ADF applications with Docker containers. It provides an overview of using containers and Docker to build application images, deploy them to Kubernetes clusters in the cloud, and set up continuous delivery pipelines for automated testing and deployment. Sample applications are packaged into Docker containers along with required dependencies. Kubernetes is used to orchestrate and manage container deployments across different environments.
Dockerizing Windows Server Applications by Ender Barillas and Taylor BrownDocker, Inc.
A session covering the container workflow from the developers inner loop, CI/CD, to deployment in a container orchestration solution. We'll cover Visual Studio Code from a Mac, Visual Studio Code from Windows with Bash and Visual Studio as an in-container local development environment targeting both Windows and Linux Containers. We'll walk through CI, Validation and CD to the Azure Container Service running Docker Swarm as one example of how you can convert your existing config as code and VM deployments to the containerized workflows startups and early adopter enterprises are using today.
From the Philly Kubernetes December 2016 Meetup.
https://www.meetup.com/Kubernetes-Philly/events/234829676/
Kubernetes accelerates technical and business innovation through rapid development and deployment of applications. Learn how to deploy, scale, and manage your applications in a containerized environments using Kubernetes.
In this 60-minute workshop, Ross Kukulinski will review fundamental Kubernetes concepts and architecture and then will show how to containerize and deploy a multi-tier web application to Kubernetes.
Topics that will be covered include:
• Working with the Kubernetes CLI (kubectl)
• Pods, Deployments, & Services
• Manual & Automated Application Scaling
• Troubleshooting and debugging
• Persistent storage
This document provides an overview of developing, building, deploying, and running microservices using containers in the cloud. It discusses microservices and containers, how to build Docker containers, deploy containers to Kubernetes clusters in the cloud (OKE, AKS, GKE), and build, deploy and test using serverless functions. It provides examples of defining microservices as Kubernetes applications, configuring pods, services, ingress, and automating builds and deployments. Serverless platforms like AWS Lambda, Azure Functions, OpenWhisk, Fn are also briefly introduced.
OpenStack is an open source cloud computing platform that provides infrastructure as a service. It abstracts compute, storage, and networking resources from physical hardware into a dashboard that manages these resources as virtual machines, object storage, and virtual networks. OpenStack uses a central dashboard and various components like Nova (compute), Glance (images), Swift (object storage), Neutron (networking), and Keystone (identity) that can work with different underlying hardware and be deployed both publicly or privately. Neutron provides network as a service and tools for building advanced virtual networks using plugins that support technologies like Open vSwitch, Linux bridges, NSX, and OpenDaylight.
This document discusses serverless computing and functions as a service. It defines serverless computing as building applications that do not require server management, instead being executed on demand in response to events. It describes how serverless platforms handle tasks like provisioning, maintenance, scaling and billing. Examples of serverless use cases include APIs, backend services, event-driven programming and processing unpredictable traffic. The document then discusses Apache OpenWhisk as an open source serverless platform and how it works.
Docker Meetup - Melbourne 2015 - Kubernetes Deep DiveKen Thompson
This document provides an overview of Kubernetes networking and storage capabilities. It begins with an agenda that includes a deep dive on Kubernetes networking and persistent volumes, as well as live demos of persistent storage and another topic. The document then discusses Kubernetes networking at the host level using pods that share IP, IPC, and disk, as well as inter-host networking solutions like OpenShift SDN. It also covers Kubernetes persistent volume claims that allow administrators to provision storage and developers to request storage that is independent of the underlying devices. The document concludes with demos of storage and another topic.
Scalable Python with Docker, Kubernetes, OpenShiftAarno Aukia
This document summarizes a presentation about scaling Python applications using Docker, Kubernetes, and OpenShift. It discusses how the speaker previously ran Python applications on virtual servers, the shortcomings of that approach, and how containerization tools address those issues. It provides an overview of Docker for building application images, Kubernetes for orchestrating containers, and OpenShift for deploying applications to production. The speaker advocates these tools to gain benefits like continuous deployment, easy scaling, and portability across infrastructures.
Achieving Cost and Resource Efficiency through Docker, OpenShift and KubernetesDean Delamont
The document discusses how adopting containerization and microservices technologies like Docker, Kubernetes, and OpenShift can help organizations achieve cost savings, resource efficiency, reduced complexity, accelerated time to market, and greater portability when deploying solutions on OpenStack. Currently, deploying applications on OpenStack using virtual machines is costly due to high resource usage from large VM sizes, installed operating systems, overprovisioned resources, and maintaining active standby instances. The presentation explores how a container-based approach addresses these issues and improves business outcomes.
A look at kubeless a serverless framework on top of kubernetes. We take a look at what serverless is and why it matters then introduce kubeless which leverages Kubernetes API resources to provide a Function as a Services solution.
OpenShift is Red Hat's container application platform that provides a full-stack platform for deploying and managing containerized applications. It is based on Docker and Kubernetes and provides additional capabilities for self-service, automation, multi-language support, and enterprise features like authentication, centralized logging, and integration with Red Hat's JBoss middleware. OpenShift handles building, deploying, and scaling applications in a clustered environment with capabilities for continuous integration/delivery, persistent storage, routing, and monitoring.
Microservices, Containers, Docker and a Cloud-Native Architecture in the Midd...Kai Wähner
Microservices are the next step after SOA: Services implement a limited set of functions. Services are developed, deployed and scaled independently. Continuous Integration and Continuous Delivery automate deployments. This way you get shorter time to results and increased flexibility. Containers improve these even more offering a very lightweight and flexible deployment option.
In the middleware world, you use concepts and tools such as an Enterprise Service Bus (ESB), Complex Event Processing (CEP), Business Process Management (BPM) or API Gateways. Many people still think about complex, heavyweight central brokers here. However, Microservices and containers are relevant not just for custom self-developed applications, but they are also a key requirement to make the middleware world more flexible, agile and automated.
This session discusses the requirements, best practices and challenges for creating a good Microservices architecture in the middleware world. A live demo with the open source PaaS framework CloudFoundry shows how technologies and frameworks such as Java, SOAP / REST Web Services, Jenkins and Docker are used to create an agile software development lifecycle to realize “Middleware Microservices”. It also discusses other modern cloud-native alternatives such as Kubernetes, Docker, Mesos, Mesosphere or Amazon ECS / AWS.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me to download the slides
Docker allows building and running applications inside lightweight containers. Some key benefits of Docker include:
- Portability - Dockerized applications are completely portable and can run on any infrastructure from development machines to production servers.
- Consistency - Docker ensures that application dependencies and environments are always the same, regardless of where the application is run.
- Efficiency - Docker containers are lightweight since they don't need virtualization layers like VMs. This allows for higher density and more efficient use of resources.
This document provides information about Linux containers and Docker. It discusses:
1) The evolution of IT from client-server models to thin apps running on any infrastructure and the challenges of ensuring consistent service interactions and deployments across environments.
2) Virtual machines and their benefits of full isolation but large disk usage, and Vagrant which allows packaging and provisioning of VMs via files.
3) Docker and how it uses Linux containers powered by namespaces and cgroups to deploy applications in lightweight portable containers that are more efficient than VMs. Examples of using Docker are provided.
Oscon 2017: Build your own container-based system with the Moby projectPatrick Chanezon
Build your own container-based system
with the Moby project
Docker Community Edition—an open source product that lets you build, ship, and run containers—is an assembly of modular components built from an upstream open source project called Moby. Moby provides a “Lego set” of dozens of components, the framework for assembling them into specialized container-based systems, and a place for all container enthusiasts to experiment and exchange ideas.
Patrick Chanezon and Mindy Preston explain how you can leverage the Moby project to assemble your own specialized container-based system, whether for IoT, cloud, or bare-metal scenarios. Patrick and Mindy explore Moby’s framework, components, and tooling, focusing on two components: LinuxKit, a toolkit to build container-based Linux subsystems that are secure, lean, and portable, and InfraKit, a toolkit for creating and managing declarative, self-healing infrastructure. Along the way, they demo how to use Moby, LinuxKit, InfraKit, and other components to quickly assemble full-blown container-based systems for several use cases and deploy them on various infrastructures.
Building Distributed Systems without Docker, Using Docker Plumbing Projects -...Patrick Chanezon
Docker provides an integrated and opinionated toolset to build, ship and run distributed applications. Over the past year, the Docker codebase has been refactored extensively to extract infrastructure plumbing components that can be used independently, following the UNIX philosophy of small tools doing one thing well: runC, containerd, swarmkit, hyperkit, vpnkit, datakit and the newly introduced InfraKit.
This talk will give an overview of these tools and how you can use them to build your own distributed systems without Docker.
Patrick Chanezon & David Chung, Docker & Phil Estes, IBM
Containers, Serverless and Functions in a nutshellEugene Fedorenko
This document provides an overview of containers, microservices, Docker, Kubernetes, serverless computing, and functions. It discusses how containers package software for distribution and are more lightweight than virtual machines. Microservices decompose monolithic applications into loosely coupled services. Docker is a popular container platform, while Kubernetes is an open source orchestration system for containers. Serverless computing focuses on writing code without managing infrastructure, using functions as units of work. Functions are stateless and triggered by events. Platforms like AWS Lambda, Azure Functions, Fn, and OpenFaaS support serverless development.
This document provides an overview and comparison of Docker, Kubernetes, OpenShift, Fabric8, and Jube container technologies. It discusses key concepts like containers, images, and Dockerfiles. It explains how Kubernetes provides horizontal scaling of Docker through replication controllers and services. OpenShift builds on Kubernetes to provide a platform as a service with routing, multi-tenancy, and a build/deploy pipeline. Fabric8 and Jube add additional functionality for developers, with tools, libraries, logging, and pure Java Kubernetes implementations respectively.
Gentle introduction to Azure ARM templates and other deployment options, both imperative and declarative, such as Terraform, Ansible, or even azcli or PowerShell.
Understanding the container landscape and it associated projectsAnthony Chow
The document discusses containers and container technologies. It provides an overview of the history and key components of containers like Docker, including namespaces, control groups, AUFS, Docker images, registries, networking solutions, security concerns and orchestration tools. It also discusses how OpenStack projects are embracing containers to provide container orchestration platforms and run OpenStack services as containers to make them more scalable and efficient. The document encourages learning more about containers to stay relevant in today's technologies.
Microservices and containers for the unitiatedKevin Lee
In this presentation I provide a high level explanation of why applications are now being developed using in a Microservice architecture. I look at how Microservice applications are typically developed and deployed using container technology and look at some of the challenges of using container technology for applications in production.
Erik Skytthe - Monitoring Mesos, Docker, Containers with Zabbix | ZabConf2016Zabbix
At DBC we are running docker and other container types in a mesos/marathon cluster environment. I will demonstrate how we collect statistics, logs etc. and monitor this environment, showing configuration examples, data flows and templates.
Some of the covered topics:
- Mesos master and agents
- Marathon Framework
- Docker engine
- Containers
- Zookeeper
- Elasticserach/ELK
LAMP Stack (Reloaded) - Infrastructure as Code with Terraform & PackerJan-Christoph Küster
Talk given at Coding Leipzig Meetup (8th August, 2018).
Why infrastructure should be managed as code (IaC), a small intro to the IaC-tooling Terraform (and Packer), and a demo that shows how to use Terraform to deploy a good old LAMP Stack into the Cloud by the push of a button (https://github.com/cloudetc/lamp-stack-for-aws).
Infrastructure as Code: Manage your Architecture with GitDanilo Poccia
This document discusses managing infrastructure as code using tools like AWS CloudFormation and AWS Elastic Beanstalk. It explains how infrastructure as code allows treating infrastructure configurations as code that can be version controlled, tested, and treated similarly to application code. Examples are provided of using templates to define cloud resources and automating provisioning and deployment of infrastructure.
[FDD 2016] Marek Śledziński - Microsoft Windows a sprawa kontenerówFuture Processing
Idea „konteneryzacji” zadomowiła się na dobre w świecie Linuxa. Ostatnie lata pokazują zmianę myślenia Microsoftu w bardzo ciekawym kierunku, tzn. do podejścia OSS i większej współpracy z innymi firmami. Nie dziwi więc, że dziura w ofercie natywnych rozwiązań w temacie kontenerów musiała zostać zapełniona. W ramach Open Container Initiative i współpracy z twórcami Dockera, Microsoft przygotował własną wersję silnika, będącego hybrydą rozwiązań z Windowsa, Hyper-V i Dockera, pozwalającego na używanie kontenerów na systemach Windows Server 2016 (Core, Nano) i Windows 10. Rozwiązania te nie są jeszcze gotowe do masowego użycia na produkcji, ale widać, że jest to droga, której Microsoft łatwo nie porzuci. Dlatego warto już dzisiaj zapoznać się z możliwościami tych rozwiązań – jak to wszystko działa, jak zacząć, co można, a z czym są jeszcze problemy.
Vert.x is a general purpose application platform for building asynchronous and reactive applications. It uses an event-driven architecture with non-blocking APIs to build distributed applications that can be composed of components written in different programming languages. Vert.x provides tools like an event bus for communication between components, as well as HTTP/TCP clients and servers that allow building reactive web and microservices applications. It aims to simplify concurrency while leveraging existing Java libraries and allowing applications to scale across multiple machines.
Microservices, Containers and Docker
This document provides an overview of microservices, containers, and Docker. It begins by defining microservices as an architectural style where applications are composed of independent, interchangeable components. It discusses benefits of the microservices style such as independent deployability, efficient scaling, and design autonomy. The document then introduces containers as a way to package applications and their dependencies to run uniformly across various environments. It compares containers to virtual machines. Finally, it describes Docker as an open source tool that automates deployment of applications into containers, providing portability and management of containers. The document concludes by discussing the need for container orchestration at scale.
Kubernetes is exploding in popularity right now and has all the buzz and cargo-culting that Docker enjoyed just a few years ago. But what even is Kubernetes? How do I run my PHP apps in it? Should I run my PHP apps in it ?
Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It was originally developed by Google based on years of experience running production workloads at scale. Kubernetes groups containers into logical units called pods and handles tasks like scheduling, health checking, scaling and rollbacks. The main components include a master node that manages the cluster and worker nodes that run application containers scheduled by the master.
Techdays SE 2016 - Micros.. err MicrocosmosMike Martin
Mike Martin is an architect at Crosspoint Solutions who works with Windows Azure and containers. Containers provide isolated runtime environments for applications with their own dependencies and share the host operating system kernel. Container engines provide lightweight virtualization and enable "deploy anywhere" approaches. Microsoft is working to integrate containers and Docker with Windows Server, Visual Studio, and Azure to provide container-based development and deployment capabilities across platforms.
Similar to fabric8 ... and Docker, Kubernetes & OpenShift (20)
In this talk, we will explore strategies to optimize the success rate of storing and retaining new information. We will discuss scientifically proven ideal learning intervals and content structures. Additionally, we will examine how to create an environment that improves our focus while you remain in the “flow”. Lastly we will also address the influence of AI on learning capabilities.
In the dynamic field of software development, this knowledge will empower you to accelerate your learning curve and support others in their learning journeys.
Explore the rapid development journey of TryBoxLang, completed in just 48 hours. This session delves into the innovative process behind creating TryBoxLang, a platform designed to showcase the capabilities of BoxLang by Ortus Solutions. Discover the challenges, strategies, and outcomes of this accelerated development effort, highlighting how TryBoxLang provides a practical introduction to BoxLang's features and benefits.
COMPSAC 2024 D&I Panel: Charting a Course for Equity: Strategies for Overcomi...Hironori Washizaki
Hironori Washizaki, "Charting a Course for Equity: Strategies for Overcoming Challenges and Promoting Inclusion in the Metaverse", IEEE COMPSAC 2024 D&I Panel, 2024.
CViewSurvey Digitech Pvt Ltd that works on a proven C.A.A.G. model.bhatinidhi2001
CViewSurvey is a SaaS-based Web & Mobile application that provides digital transformation to traditional paper surveys and feedback for customer & employee experience, field & market research that helps you evaluate your customer's as well as employee's loyalty.
With our unique C.A.A.G. Collect, Analysis, Act & Grow approach; business & industry’s can create customized surveys on web, publish on app to collect unlimited response & review AI backed real-time data analytics on mobile & tablets anytime, anywhere. Data collected when offline is securely stored in the device, which syncs to the cloud server when connected to any network.
Overview of ERP - Mechlin Technologies.pptxMitchell Marsh
This PowerPoint presentation provides a comprehensive overview of Enterprise Resource Planning (ERP) systems. It covers the fundamental concepts, benefits, and key functionalities of ERP software, illustrating how it integrates various business processes into a unified system. From finance and HR to supply chain and customer relationship management, ERP facilitates efficient data management and decision-making across organizations. Whether you're new to ERP or looking to deepen your understanding, this presentation offers valuable insights into leveraging ERP for business success.
Seamless PostgreSQL to Snowflake Data Transfer in 8 Simple StepsEstuary Flow
Unlock the full potential of your data by effortlessly migrating from PostgreSQL to Snowflake, the leading cloud data warehouse. This comprehensive guide presents an easy-to-follow 8-step process using Estuary Flow, an open-source data operations platform designed to simplify data pipelines.
Discover how to seamlessly transfer your PostgreSQL data to Snowflake, leveraging Estuary Flow's intuitive interface and powerful real-time replication capabilities. Harness the power of both platforms to create a robust data ecosystem that drives business intelligence, analytics, and data-driven decision-making.
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Don't miss out on this opportunity to unlock the full potential of your data. Read & Download this comprehensive guide now and embark on a seamless data journey from PostgreSQL to Snowflake with Estuary Flow!
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NBFC Software: Optimize Your Non-Banking Financial CompanyNBFC Softwares
NBFC Software: Optimize Your Non-Banking Financial Company
Enhance Your Financial Services with Comprehensive NBFC Software
NBFC software provides a complete solution for non-banking financial companies, streamlining banking and accounting functions to reduce operational costs. Our software is designed to meet the diverse needs of NBFCs, including investment banks, insurance companies, and hedge funds.
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9. Facts
» OS level virtualisation tool suite
» Client-Server architecture
– Server communicates via Unix- or INET-
Sockets with a REST API
» Docker commands via CLI
» Written in Go
» Current version: 1.6
10. Lightweight Container vs. VM
Containers are isolated, but
sharing the kernel and (some) files
➜ faster & lighter
11. Concepts
» Image
– Read-only filesystem layer
– Deploy & Share
– Blueprint for a container
» Container
– Read-write filesystem layer (copy-on-write)
– Instance of an image
– Has a lifecycle (start & stop)
12. Concepts
» Repository
– Collection of layered images
– often synonym for “Image”
– Has a name: registry/user/repository:tag
» Registry
– Storage for repositories
– Default: docker.io (public docker hub)
13. docker
» CLI for managing Docker
– docker <sub-command> …
ps Show all containers
images Show all images
run Create and run a container
search Seaarch for images on a registry
pull Dowmload of images
rm Remove container
rmi Remove image
15. Facts
» Open Source orchestration platform for
Docker containers
– Rewrite of Google’s internal framework “Borg”
» Declarative specification of a desired state
» Self-healing
» Service discovery
» Scheduling across hosts
» Simple replication
17. Concepts
» Pods
– Collection of one or more Docker containers
» Replication Controller
– Creates and takes care of Pods
» Services
– Proxy for a collection of Pods
» Labels
– Grouping and organisation of Objects
18. Pod
» Collection of Docker containers running
on the same host.
» Pods have a unique IP
» Containers in a Pod ….
– …. share the same IP
– …. can reach each other via local ports
– …. can share data via volumes
» Pods can have one or more Labels
19. Replication Controller
» Controls Pods selected by Labels
» Ensures that a specified number of Pod
replicas is running
» Holds Pod Templates for creating new
Pods
» Autoscaling
» Rolling Updates
21. Service
» View on a set of Pods with
single IP address and port
» Pods are selected by Label
» Services are referenced by
environment variables
» Service addresses stay
stable
– Pods come and go (with
different IPs)
23. kubectl
» CLI for managing Kubernetes
– kubectl <sub-command> …
get pods
get services
get rc
Show pods/service/replication
controllers
create Create objects
update Update objects
delete Delete objects
resize New size for a RC
25. History
» 2011: Platform-as-a-Service (PaaS) from
Red Hat
» Three variants:
– Online - Public PaaS
– Enterprise - Private PaaS
– Origin - Community PaaS
» OpenShift V3: Complete rewrite on basis
of Kubernetes
26. Features
» Adds the “Build” aspect to Kubernetes
» Developer and Operation Tools
» Application Component Libraries
» Infrastructure Services
– Registry, Router, OAuth2 Security
» Team and user isolation (multi-tenancy)
» Management UI
27. Builds
» Extension for building images
» Docker Builds
– Build images get access to enclosing Docker
daemon.
» Source-To-Image
– Assembly of new image from a builder image
and source code
– Often combined with a Webhook for automatic
builds
28. Templates
» Templates allow the specifications of
replication controller, services, …
» Parameter slots can be filled in …
– from the CLI wit osc process
– from the User Interface
» might become a Kubernetes feature in the
future
30. Deployments
» Update of a replication controller’s pod
template
– based on triggers
‣ image change
‣ configuration change
– custom deployment strategies
– rollback support
– replication scaling
31. Registry
» OpenShift provides an own Docker
registry as service
» OpenShift projects are mapped to registry
user
– e.g. for an image “fabric8/console” to be
pushed there must exist a OpenShift project
“fabric8”
32. Router
» External DNS mapping to services
– based on HAProxy
» Different types of TLS termination
– edge : TLS terminates at the router
– passthrough: TLS stream is handle through
to the service
– re-encryption: TLS terminates at the router
and is re-encrypted for the service
34. osc
» OpenShift CLI
» Extension to kubectl
process Process Templates
project Change namespace/project
get routes Show created routes
port-forward Port forwarding into pod
exec Execute process in running pod
36. fabric8
» Tools and Services for value add to
Kubernetes and OpenShift
– Management: console, logging, metrics, …
– Continous Delivery Workflow
– iPaaS: Camel route visualisation, API registry,
Messaging as a Service, …
– Tools: Kubernetes/OpenShift build integration,
Kubernetes component test support, CDI
extensions
37. History
» Fuse ESB: Open Source integration
platform by FuseSource
» Fabric: Extension for managing many
ESBs
» Red Hat acquired FuseSource in 2012
– Fuse ESB JBoss Fuse
– Fabric (closed) fabric8 (open source)
38. » fabric8 1.x is based on Zookeeper as
central view of the system
– JBoss Fuse 6.1: fabric8 1.0
– JBoss Fuse 6.2: fabric8 1.2.x
» fabric8 2.x sits on top of Kubernetes
– fabric8 1.x functionality became Jube, a pure
Java implementation of the Kubernetes API
39. Management
» Web console for Kubernetes
– Starting/Stopping of pods
– Changing Replicas
– Individual management of pods
– based on hawt.io
42. iPaas
» Console for visualising and working with
integration services
– e.g. showing the Camel routes
» API registry for a global view of all
RESTful and WebServices
» MQ provides Messaging as a Service
– based on ActiveMQ
– allows autoscaling
43. Tools
» fabric8-maven-plugin
– Creates and apply Kubernetes
descriptors out of build informations
– Creates OpenShift routes
– Deploys kubernetes.json as Maven
artefacts
44. Tools
» Arquillian extension for testing
– Provision containers to Kubernetes
– Separate namespace per test (isolation)
– Annotations for injecting Kubernetes objects
– Assertions on Kubernetes objects
» Java Libraries
– Access to Kubernetes API
– CDI injections of Kubernetes Services
– ….
45. Summary
» Docker is the perfect foundation for a
container based infrastructure
» Kubernetes is a powerful Docker
orchestration platform backed with great
momentum
» OpenShift as a PaaS adds the “Build”
dimension to Kubernetes
» Fabric8 adds services and Java tooling to
Docker, Kubernetes and OpenShift