Alternatives to Apache Mesos logo

Alternatives to Apache Mesos

Mesosphere, OpenStack, Kubernetes, Docker, and Yarn are the most popular alternatives and competitors to Apache Mesos.
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What is Apache Mesos and what are its top alternatives?

Apache Mesos is an open-source cluster manager that simplifies the management of distributed systems and resources across a cluster of machines. It enables efficient resource sharing and isolation for distributed applications. Mesos offers features such as scalability, fault tolerance, and support for various types of workloads. However, setting up and configuring Mesos can be complex, requiring expertise and effort to deploy and manage.

  1. Kubernetes: Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Key features include automatic container placement, self-healing capabilities, and extensive ecosystem support. Pros: Widely adopted, strong community support. Cons: Steeper learning curve for beginners.
  2. Docker Swarm: Docker Swarm is Docker's native clustering solution that allows users to create and manage a cluster of Docker nodes. Key features include ease of setup, integrated with Docker tools, and declarative service model. Pros: Seamless integration with Docker, simple configuration. Cons: Limited scalability compared to other solutions.
  3. Nomad: Nomad is a cluster and workload scheduler by HashiCorp that supports various types of workloads, including Docker containers, VMs, and standalone executables. Key features include ease of use, scalability, and support for multiple datacenters. Pros: Simple setup, support for heterogeneous workloads. Cons: Limited ecosystem compared to Kubernetes.
  4. Amazon ECS: Amazon Elastic Container Service (ECS) is a fully managed container orchestration service by AWS that allows users to run Docker containers on a cluster of virtual machines. Key features include seamless integration with other AWS services, scalability, and security. Pros: Deep integration with AWS services, managed service. Cons: Tightly coupled with AWS ecosystem.
  5. Rancher: Rancher is an open-source container management platform that simplifies the deployment and management of Kubernetes and Docker clusters. Key features include multi-cluster management, centralized authentication, and monitoring. Pros: User-friendly interface, support for multiple orchestration tools. Cons: Limited control compared to standalone solutions.
  6. Apache Aurora: Apache Aurora is a service scheduler by Apache that allows users to deploy and manage long-running services on a cluster of machines. Key features include automatic service scaling, fault tolerance, and horizontal scalability. Pros: Mature project, battle-tested. Cons: Limited support for different workload types compared to Kubernetes.
  7. HashiCorp Consul: HashiCorp Consul is a service mesh and service discovery tool that enables users to connect and secure distributed applications across multiple clusters. Key features include service discovery, health checking, and key-value store. Pros: Secure communication, dynamic configuration updates. Cons: Not a full-fledged cluster manager like Mesos.
  8. Apache Hadoop YARN: Apache Hadoop YARN is a resource manager that manages resources in a Hadoop cluster and schedules jobs for processing. Key features include scalability, fault tolerance, and multi-tenancy support. Pros: Mature project, widely used in big data applications. Cons: Limited support for non-Hadoop workloads.
  9. D2iQ Konvoy: D2iQ Konvoy is a Kubernetes distribution by D2iQ that simplifies the deployment, scaling, and management of Kubernetes clusters. Key features include automated operations, secure configuration, and built-in best practices. Pros: Streamlined Kubernetes operations, enterprise-grade support. Cons: Vendor-specific solution, may lock users into D2iQ's ecosystem.
  10. Rundeck: Rundeck is an open-source automation platform that allows users to run tasks on any node in a cluster, including containerized environments. Key features include job scheduling, access control, and workflow automation. Pros: Easy to use, supports various job types. Cons: Not a dedicated cluster manager like Mesos, may require additional tools for resource management.

Top Alternatives to Apache Mesos

  • Mesosphere
    Mesosphere

    Mesosphere offers a layer of software that organizes your machines, VMs, and cloud instances and lets applications draw from a single pool of intelligently- and dynamically-allocated resources, increasing efficiency and reducing operational complexity. ...

  • OpenStack
    OpenStack

    OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources throughout a datacenter, all managed through a dashboard that gives administrators control while empowering their users to provision resources through a web interface. ...

  • Kubernetes
    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • Docker
    Docker

    The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application — from legacy to what comes next — and securely run them anywhere ...

  • Yarn
    Yarn

    Yarn caches every package it downloads so it never needs to again. It also parallelizes operations to maximize resource utilization so install times are faster than ever. ...

  • Docker Swarm
    Docker Swarm

    Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself. ...

  • Cloud Foundry
    Cloud Foundry

    Cloud Foundry is an open platform as a service (PaaS) that provides a choice of clouds, developer frameworks, and application services. Cloud Foundry makes it faster and easier to build, test, deploy, and scale applications. ...

  • Hadoop
    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

Apache Mesos alternatives & related posts

Mesosphere logo

Mesosphere

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Combine your datacenter servers and cloud instances into one shared pool
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    OpenStack logo

    OpenStack

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      Shared insights
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      Hello guys

      I am confused between choosing CentOS7 or centos8 for OpenStack tripleo undercloud deployment. Which one should I use? There is another option to use OpenStack, Ubuntu, or MicroStack.

      We wanted to use this deployment to build our home cloud or private cloud infrastructure. I heard that centOS is always the best choice through a little research, but still not sure. As centos8 from Redhat is not supported for OpenStack tripleo deployments anymore, I had to upgrade to CentosStream.

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      Kubernetes logo

      Kubernetes

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      Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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      PROS OF KUBERNETES
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      • 128
        Simple and powerful
      • 106
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      • 58
        The right abstractions
      • 25
        Scale services
      • 20
        Replication controller
      • 11
        Permission managment
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        Supports autoscaling
      • 8
        Cheap
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        Simple
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        Self-healing
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        No cloud platform lock-in
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        Promotes modern/good infrascture practice
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        Open, powerful, stable
      • 5
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      • 4
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        Quick cloud setup
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        Cloud Agnostic
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        Backed by Red Hat
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        Custom and extensibility
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        Everything of CaaS
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      CONS OF KUBERNETES
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        Steep learning curve
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        Additional vendor lock-in (Docker)
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        More moving parts to secure
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      related Kubernetes posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M views

      How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

      Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

      Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

      https://eng.uber.com/distributed-tracing/

      (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

      Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

      See more
      Ashish Singh
      Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

      To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

      Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

      We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

      Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

      Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

      #BigData #AWS #DataScience #DataEngineering

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      Docker logo

      Docker

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        Standardization
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        Scalable
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        Upgrading / down­grad­ing / ap­pli­ca­tion versions
      • 88
        Security
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        Portability
      • 26
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      • 17
        Game changer
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      • 14
        Fast
      • 12
        Concurrency
      • 8
        Docker's Compose tools
      • 6
        Easy setup
      • 6
        Fast and Portable
      • 5
        Because its fun
      • 4
        Makes shipping to production very simple
      • 3
        Highly useful
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        It's dope
      • 2
        Very easy to setup integrate and build
      • 2
        HIgh Throughput
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        Package the environment with the application
      • 2
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      • 2
        Open source and highly configurable
      • 2
        Simplicity, isolation, resource effective
      • 2
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      • 2
        Its cool
      • 2
        Docker hub for the FTW
      • 2
        Super
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      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M views

      Our whole DevOps stack consists of the following tools:

      • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
      • Respectively Git as revision control system
      • SourceTree as Git GUI
      • Visual Studio Code as IDE
      • CircleCI for continuous integration (automatize development process)
      • Prettier / TSLint / ESLint as code linter
      • SonarQube as quality gate
      • Docker as container management (incl. Docker Compose for multi-container application management)
      • VirtualBox for operating system simulation tests
      • Kubernetes as cluster management for docker containers
      • Heroku for deploying in test environments
      • nginx as web server (preferably used as facade server in production environment)
      • SSLMate (using OpenSSL) for certificate management
      • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
      • PostgreSQL as preferred database system
      • Redis as preferred in-memory database/store (great for caching)

      The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

      • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
      • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
      • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
      • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
      • Scalability: All-in-one framework for distributed systems.
      • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
      See more
      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 8.7M views

      Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

      It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

      I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

      We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

      If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

      The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

      Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

      See more
      Yarn logo

      Yarn

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      Nick Parsons
      Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.9M views

      Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

      We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

      We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

      Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

      #FrameworksFullStack #Languages

      See more
      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 27 upvotes · 4.8M views

      Our whole Node.js backend stack consists of the following tools:

      • Lerna as a tool for multi package and multi repository management
      • npm as package manager
      • NestJS as Node.js framework
      • TypeScript as programming language
      • ExpressJS as web server
      • Swagger UI for visualizing and interacting with the API’s resources
      • Postman as a tool for API development
      • TypeORM as object relational mapping layer
      • JSON Web Token for access token management

      The main reason we have chosen Node.js over PHP is related to the following artifacts:

      • Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
      • Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
      • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
      • Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
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      Docker Swarm logo

      Docker Swarm

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      Yshay Yaacobi

      Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

      Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

      After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

      See more
      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M views

      Our whole DevOps stack consists of the following tools:

      • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
      • Respectively Git as revision control system
      • SourceTree as Git GUI
      • Visual Studio Code as IDE
      • CircleCI for continuous integration (automatize development process)
      • Prettier / TSLint / ESLint as code linter
      • SonarQube as quality gate
      • Docker as container management (incl. Docker Compose for multi-container application management)
      • VirtualBox for operating system simulation tests
      • Kubernetes as cluster management for docker containers
      • Heroku for deploying in test environments
      • nginx as web server (preferably used as facade server in production environment)
      • SSLMate (using OpenSSL) for certificate management
      • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
      • PostgreSQL as preferred database system
      • Redis as preferred in-memory database/store (great for caching)

      The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

      • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
      • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
      • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
      • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
      • Scalability: All-in-one framework for distributed systems.
      • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
      See more
      Cloud Foundry logo

      Cloud Foundry

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      Deploy and scale applications in seconds on your choice of private or public cloud
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      PROS OF CLOUD FOUNDRY
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        Perfectly aligned with springboot
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      CONS OF CLOUD FOUNDRY
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        Hadoop logo

        Hadoop

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          Shared insights
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          KafkaKafkaHadoopHadoop
          at

          The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

          For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

          Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

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          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 3M views

          Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

          Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

          https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

          (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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