Alternatives to Ambari logo

Alternatives to Ambari

Hue, Zookeeper, Apache Mesos, Yarn, and Kubernetes are the most popular alternatives and competitors to Ambari.
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What is Ambari and what are its top alternatives?

This project is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters. It provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs.
Ambari is a tool in the Monitoring Tools category of a tech stack.

Top Alternatives to Ambari

  • Hue

    Hue

    It is open source and lets regular users import their big data, query it, search it, visualize it and build dashboards on top of it, all from their browser. ...

  • Zookeeper

    Zookeeper

    A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications. ...

  • Apache Mesos

    Apache Mesos

    Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers. ...

  • 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. ...

  • 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. ...

  • Ansible

    Ansible

    Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use. ...

  • Apache Spark

    Apache Spark

    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...

  • Prometheus

    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

Ambari alternatives & related posts

Hue logo

Hue

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70
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An open source SQL Workbench for Data Warehouses
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70
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PROS OF HUE
    Be the first to leave a pro
    CONS OF HUE
      Be the first to leave a con

      related Hue posts

      Zookeeper logo

      Zookeeper

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      805
      42
      Because coordinating distributed systems is a Zoo
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      42
      PROS OF ZOOKEEPER
      • 11
        High performance ,easy to generate node specific config
      • 8
        Kafka support
      • 8
        Java
      • 5
        Spring Boot Support
      • 3
        Supports extensive distributed IPC
      • 2
        Supports DC/OS
      • 2
        Used in ClickHouse
      • 1
        Curator
      • 1
        Embeddable In Java Service
      • 1
        Used in Hadoop
      CONS OF ZOOKEEPER
        Be the first to leave a con

        related Zookeeper posts

        Apache Mesos logo

        Apache Mesos

        285
        385
        30
        Develop and run resource-efficient distributed systems
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        PROS OF APACHE MESOS
        • 20
          Easy scaling
        • 6
          Web UI
        • 2
          Fault-Tolerant
        • 1
          Elastic Distributed System
        • 1
          High-Available
        CONS OF APACHE MESOS
        • 1
          Not for long term
        • 1
          Depends on Zookeeper

        related Apache Mesos posts

        Docker containers on Mesos run their microservices with consistent configurations at scale, along with Aurora for long-running services and cron jobs.

        See more
        Yarn logo

        Yarn

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        A new package manager for JavaScript
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        PROS OF YARN
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          Incredibly fast
        • 21
          Easy to use
        • 12
          Open Source
        • 10
          Can install any npm package
        • 7
          Works where npm fails
        • 6
          Workspaces
        • 2
          Incomplete to run tasks
        CONS OF YARN
        • 15
          Facebook
        • 6
          Sends data to facebook
        • 3
          Should be installed separately
        • 2
          Cannot publish to registry other than npm

        related Yarn posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 25 upvotes · 2.1M 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.
        See more
        Johnny Bell

        So when starting a new project you generally have your go to tools to get your site up and running locally, and some scripts to build out a production version of your site. Create React App is great for that, however for my projects I feel as though there is to much bloat in Create React App and if I use it, then I'm tied to React, which I love but if I want to switch it up to Vue or something I want that flexibility.

        So to start everything up and running I clone my personal Webpack boilerplate - This is still in Webpack 3, and does need some updating but gets the job done for now. So given the name of the repo you may have guessed that yes I am using Webpack as my bundler I use Webpack because it is so powerful, and even though it has a steep learning curve once you get it, its amazing.

        The next thing I do is make sure my machine has Node.js configured and the right version installed then run Yarn. I decided to use Yarn because when I was building out this project npm had some shortcomings such as no .lock file. I could probably move from Yarn to npm but I don't really see any point really.

        I use Babel to transpile all of my #ES6 to #ES5 so the browser can read it, I love Babel and to be honest haven't looked up any other transpilers because Babel is amazing.

        Finally when developing I have Prettier setup to make sure all my code is clean and uniform across all my JS files, and ESLint to make sure I catch any errors or code that could be optimized.

        I'm really happy with this stack for my local env setup, and I'll probably stick with it for a while.

        See more
        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
        • 157
          Leading docker container management solution
        • 124
          Simple and powerful
        • 101
          Open source
        • 75
          Backed by google
        • 56
          The right abstractions
        • 24
          Scale services
        • 18
          Replication controller
        • 9
          Permission managment
        • 7
          Simple
        • 7
          Supports autoscaling
        • 6
          Cheap
        • 4
          Self-healing
        • 4
          Reliable
        • 4
          No cloud platform lock-in
        • 3
          Open, powerful, stable
        • 3
          Scalable
        • 3
          Quick cloud setup
        • 3
          Promotes modern/good infrascture practice
        • 2
          Backed by Red Hat
        • 2
          Runs on azure
        • 2
          Cloud Agnostic
        • 2
          Custom and extensibility
        • 2
          Captain of Container Ship
        • 2
          A self healing environment with rich metadata
        • 1
          Golang
        • 1
          Easy setup
        • 1
          Everything of CaaS
        • 1
          Sfg
        • 1
          Expandable
        • 1
          Gke
        CONS OF KUBERNETES
        • 13
          Poor workflow for development
        • 11
          Steep learning curve
        • 5
          Orchestrates only infrastructure
        • 2
          High resource requirements for on-prem clusters

        related Kubernetes posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M 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
        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
        Ansible logo

        Ansible

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        Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
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        PROS OF ANSIBLE
        • 276
          Agentless
        • 204
          Great configuration
        • 195
          Simple
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          Powerful
        • 151
          Easy to learn
        • 66
          Flexible
        • 54
          Doesn't get in the way of getting s--- done
        • 34
          Makes sense
        • 29
          Super efficient and flexible
        • 27
          Powerful
        • 11
          Dynamic Inventory
        • 8
          Backed by Red Hat
        • 7
          Works with AWS
        • 6
          Cloud Oriented
        • 6
          Easy to maintain
        • 4
          Because SSH
        • 4
          Multi language
        • 4
          Easy
        • 4
          Simple
        • 4
          Procedural or declarative, or both
        • 4
          Simple and powerful
        • 3
          Consistency
        • 3
          Vagrant provisioner
        • 2
          Fast as hell
        • 2
          Masterless
        • 2
          Well-documented
        • 2
          Merge hash to get final configuration similar to hiera
        • 2
          Debugging is simple
        • 1
          Work on windows, but difficult to manage
        • 1
          Certified Content
        CONS OF ANSIBLE
        • 5
          Dangerous
        • 5
          Hard to install
        • 3
          Bloated
        • 3
          Backward compatibility
        • 2
          Doesn't Run on Windows
        • 2
          No immutable infrastructure

        related Ansible posts

        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 4.6M 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
        Sebastian Gębski

        Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

        See more
        Apache Spark logo

        Apache Spark

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        Fast and general engine for large-scale data processing
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        PROS OF APACHE SPARK
        • 58
          Open-source
        • 48
          Fast and Flexible
        • 7
          One platform for every big data problem
        • 6
          Easy to install and to use
        • 6
          Great for distributed SQL like applications
        • 3
          Works well for most Datascience usecases
        • 2
          Machine learning libratimery, Streaming in real
        • 2
          In memory Computation
        • 0
          Interactive Query
        CONS OF APACHE SPARK
        • 3
          Speed

        related Apache Spark posts

        Eric Colson
        Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2M views

        The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

        Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

        At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

        For more info:

        #DataScience #DataStack #Data

        See more
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1M 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 )

        See more
        Prometheus logo

        Prometheus

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        An open-source service monitoring system and time series database, developed by SoundCloud
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        PROS OF PROMETHEUS
        • 45
          Powerful easy to use monitoring
        • 38
          Flexible query language
        • 32
          Dimensional data model
        • 27
          Alerts
        • 22
          Active and responsive community
        • 21
          Extensive integrations
        • 19
          Easy to setup
        • 12
          Beautiful Model and Query language
        • 7
          Easy to extend
        • 6
          Nice
        • 3
          Written in Go
        • 2
          Good for experimentation
        • 1
          Easy for monitoring
        CONS OF PROMETHEUS
        • 11
          Just for metrics
        • 6
          Needs monitoring to access metrics endpoints
        • 6
          Bad UI
        • 3
          Not easy to configure and use
        • 2
          Requires multiple applications and tools
        • 2
          Written in Go
        • 2
          Supports only active agents
        • 1
          TLS is quite difficult to understand

        related Prometheus posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.9M views

        Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

        By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

        To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

        https://eng.uber.com/m3/

        (GitHub : https://github.com/m3db/m3)

        See more
        Matt Menzenski
        Senior Software Engineering Manager at PayIt · | 12 upvotes · 76.8K views

        Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

        See more