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?

Ambari is an open-source management platform designed to facilitate the provisioning, managing, and monitoring of Apache Hadoop clusters. Its key features include easy cluster setup, centralized management, dashboard for monitoring, and customizable alerts. However, some limitations of Ambari include complex setup process and limited support for non-Apache products.

  1. Cloudera Manager: Cloudera Manager is a comprehensive management application for Apache Hadoop clusters. It offers features such as automated deployment, real-time monitoring, and integrated troubleshooting tools. Pros include extensive support for Cloudera's distribution, while the main con is the premium price for enterprise features.

  2. Hortonworks Data Platform: Hortonworks Data Platform is another popular alternative to Ambari offering a complete data management platform for big data solutions. Its key features include Apache Hadoop, Apache Spark, and Apache Hive. Pros include the open-source nature of the platform, while the con is the lack of some enterprise-level features.

  3. MapR Control System: MapR Control System provides simplified management and monitoring capabilities for MapR Data Platform deployments. It offers features like real-time performance monitoring and centralized management of clusters. Pros include high reliability and performance, while the con is the higher cost compared to other solutions.

  4. Apache NiFi: Apache NiFi is a data automation tool that can also be used for data flow management in big data environments. Key features include real-time data processing, visual data flow design, and data provenance. Pros include flexibility and scalability, while the con is the learning curve for new users.

  5. Apache Flink: Apache Flink is a distributed stream processing framework with features like low-latency processing, event time processing, and support for batch processing. Pros include excellent performance and fault tolerance, while the con is the complexity of streaming data processing.

  6. Databricks: Databricks provides a unified analytics platform built on Apache Spark that offers features like collaborative workspace, interactive notebooks, and automated cluster management. Pros include ease of use and scalability, while the con is the pricing model based on usage.

  7. Apache Airflow: Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. Key features include a rich library of operators for tasks, rich UI for task management, and easy debugging. Pros include flexibility and extensibility, while the con is the initial setup complexity.

  8. Confluent Control Center: Confluent Control Center offers monitoring and management capabilities for Apache Kafka clusters. It includes features like real-time monitoring, metrics visualization, and cluster management. Pros include seamless integration with Apache Kafka, while the main con is the limited support for other components.

  9. Rancher: Rancher is an open-source container management platform that can also be used for managing big data workloads. Key features include multi-cluster management, deployment automation, and monitoring. Pros include ease of use and flexibility, while the con is the lack of some advanced enterprise features.

  10. Presto SQL: Presto SQL is an open-source distributed SQL query engine that can be used for querying big data in real-time. It offers features like high performance, support for multiple data sources, and ANSI SQL compatibility. Pros include fast query processing and flexibility, while the con is the potential learning curve for SQL developers transitioning to Presto.

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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97
0
An open source SQL Workbench for Data Warehouses
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97
+ 1
0
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

      807
      1K
      43
      Because coordinating distributed systems is a Zoo
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      PROS OF ZOOKEEPER
      • 11
        High performance ,easy to generate node specific config
      • 8
        Java
      • 8
        Kafka support
      • 5
        Spring Boot Support
      • 3
        Supports extensive distributed IPC
      • 2
        Curator
      • 2
        Used in ClickHouse
      • 2
        Supports DC/OS
      • 1
        Used in Hadoop
      • 1
        Embeddable In Java Service
      CONS OF ZOOKEEPER
        Be the first to leave a con

        related Zookeeper posts

        Shared insights
        on
        ZookeeperZookeeperHAProxyHAProxy
        at

        Early 2013

        In early 2013, Airbnb tackled the problem of service discovery and load balancing in the context of a service oriented architecture (SOA) by building and releasing an open source tool called SmartStack. SmartStack is built on two other open source tools created by Airbnb called Nerve and Synapse.

        Nerve is a service registration daemon that performs health checks that “creates ephemeral nodes in Zookeeper which contain information about the address/port combos for a backend available to serve requests for a particular service.”

        Synapse is a transparent service discovery framework for connecting an SOA that reads the information in Zookeeper for available backends, and then uses that information to configure a local HAProxy process, which then routes requests between clients and services.

        See more
        Apache Mesos logo

        Apache Mesos

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        Develop and run resource-efficient distributed systems
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        PROS OF APACHE MESOS
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          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

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

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        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
        • 22
          Easy to use
        • 13
          Open Source
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          Can install any npm package
        • 8
          Works where npm fails
        • 7
          Workspaces
        • 3
          Incomplete to run tasks
        • 2
          Fast
        CONS OF YARN
        • 16
          Facebook
        • 7
          Sends data to facebook
        • 4
          Should be installed separately
        • 3
          Cannot publish to registry other than npm

        related Yarn posts

        Nick Parsons
        Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 4M 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.
        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
        • 164
          Leading docker container management solution
        • 128
          Simple and powerful
        • 106
          Open source
        • 76
          Backed by google
        • 58
          The right abstractions
        • 25
          Scale services
        • 20
          Replication controller
        • 11
          Permission managment
        • 9
          Supports autoscaling
        • 8
          Cheap
        • 8
          Simple
        • 6
          Self-healing
        • 5
          No cloud platform lock-in
        • 5
          Promotes modern/good infrascture practice
        • 5
          Open, powerful, stable
        • 5
          Reliable
        • 4
          Scalable
        • 4
          Quick cloud setup
        • 3
          Cloud Agnostic
        • 3
          Captain of Container Ship
        • 3
          A self healing environment with rich metadata
        • 3
          Runs on azure
        • 3
          Backed by Red Hat
        • 3
          Custom and extensibility
        • 2
          Sfg
        • 2
          Gke
        • 2
          Everything of CaaS
        • 2
          Golang
        • 2
          Easy setup
        • 2
          Expandable
        CONS OF KUBERNETES
        • 16
          Steep learning curve
        • 15
          Poor workflow for development
        • 8
          Orchestrates only infrastructure
        • 4
          High resource requirements for on-prem clusters
        • 2
          Too heavy for simple systems
        • 1
          Additional vendor lock-in (Docker)
        • 1
          More moving parts to secure
        • 1
          Additional Technology Overhead

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        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M 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
        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

        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
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          Agentless
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          Great configuration
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          Simple
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          Powerful
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          Easy to learn
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          Flexible
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          Doesn't get in the way of getting s--- done
        • 35
          Makes sense
        • 30
          Super efficient and flexible
        • 27
          Powerful
        • 11
          Dynamic Inventory
        • 9
          Backed by Red Hat
        • 7
          Works with AWS
        • 6
          Cloud Oriented
        • 6
          Easy to maintain
        • 4
          Vagrant provisioner
        • 4
          Simple and powerful
        • 4
          Multi language
        • 4
          Simple
        • 4
          Because SSH
        • 4
          Procedural or declarative, or both
        • 4
          Easy
        • 3
          Consistency
        • 2
          Well-documented
        • 2
          Masterless
        • 2
          Debugging is simple
        • 2
          Merge hash to get final configuration similar to hiera
        • 2
          Fast as hell
        • 1
          Manage any OS
        • 1
          Work on windows, but difficult to manage
        • 1
          Certified Content
        CONS OF ANSIBLE
        • 8
          Dangerous
        • 5
          Hard to install
        • 3
          Doesn't Run on Windows
        • 3
          Bloated
        • 3
          Backward compatibility
        • 2
          No immutable infrastructure

        related Ansible posts

        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 8.9M 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
        • 61
          Open-source
        • 48
          Fast and Flexible
        • 8
          One platform for every big data problem
        • 8
          Great for distributed SQL like applications
        • 6
          Easy to install and to use
        • 3
          Works well for most Datascience usecases
        • 2
          Interactive Query
        • 2
          Machine learning libratimery, Streaming in real
        • 2
          In memory Computation
        CONS OF APACHE SPARK
        • 4
          Speed

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        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 11.2M 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
        Eric Colson
        Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.1M 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

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        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
        • 47
          Powerful easy to use monitoring
        • 38
          Flexible query language
        • 32
          Dimensional data model
        • 27
          Alerts
        • 23
          Active and responsive community
        • 22
          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
        • 12
          Just for metrics
        • 6
          Bad UI
        • 6
          Needs monitoring to access metrics endpoints
        • 4
          Not easy to configure and use
        • 3
          Supports only active agents
        • 2
          Written in Go
        • 2
          TLS is quite difficult to understand
        • 2
          Requires multiple applications and tools
        • 1
          Single point of failure

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        Matt Menzenski
        Senior Software Engineering Manager at PayIt · | 16 upvotes · 1M 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
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M 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