Alternatives to Xplenty logo

Alternatives to Xplenty

Alooma, Segment, Talend, Airflow, and Stitch are the most popular alternatives and competitors to Xplenty.
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What is Xplenty and what are its top alternatives?

Xplenty is a cloud-based ETL (Extract, Transform, Load) platform that allows users to easily create data pipelines for processing and preparing data for analytics. Key features include a visual interface for building data workflows, support for various data sources and destinations, data transformation capabilities, scheduling and monitoring tools, and a scalable infrastructure. However, some limitations of Xplenty include pricing based on data volume, limited support for custom scripting, and potential performance issues with large datasets.

  1. Matillion: Matillion is an ETL tool designed for cloud data warehouses such as Amazon Redshift, Snowflake, and Google BigQuery. Key features include a drag-and-drop interface, support for various data sources, data transformation capabilities, and job scheduling. Pros include native integration with cloud data warehouses, while cons may include pricing based on usage.
  2. Talend: Talend is an open-source ETL tool that offers a comprehensive set of features for data integration and management. Key features include a graphical interface, support for various data sources and destinations, data transformation capabilities, and scheduling tools. Pros include a robust feature set and community support, while cons may include a steeper learning curve for beginners.
  3. Stitch Data: Stitch Data is a cloud-based ETL tool that specializes in data pipeline automation. Key features include support for over 100 data sources, automated schema changes, and data replication. Pros include ease of use and scalability, while cons may include limited data transformation capabilities.
  4. Alooma: Alooma is a data integration platform that offers real-time data streaming and ETL capabilities. Key features include support for various data sources, data transformation options, and automatic schema creation. Pros include real-time data processing capabilities, while cons may include pricing based on usage.
  5. Singer: Singer is an open-source ETL framework that allows users to create data pipelines using modular, reusable components called "Taps" and "Targets". Key features include extensibility, community-supported connectors, and data transformation capabilities. Pros include flexibility and open-source nature, while cons may include the need for technical expertise to set up and maintain pipelines.
  6. FiveTran: FiveTran is a cloud-based ETL tool that focuses on automating data integration processes. Key features include pre-built connectors for various data sources, data transformation options, and zero maintenance. Pros include ease of use and scalability, while cons may include limited customization options.
  7. CData Sync: CData Sync is an ETL and data replication tool that offers connectivity to over 200 data sources with SQL queries. Key features include data transformation capabilities, support for various data destinations, and scheduling options. Pros include a wide range of data source connectors, while cons may include pricing based on data source count.
  8. Panoply: Panoply is a cloud data warehouse platform that includes built-in ETL capabilities for seamless data integration. Key features include automated data pipeline creation, support for various data sources, data transformation options, and real-time data processing. Pros include ease of use and scalability, while cons may include pricing based on usage.
  9. Blendo: Blendo is a cloud-based data integration platform that focuses on simplifying ETL processes. Key features include support for various data sources, data transformation options, and real-time data loading. Pros include ease of use and pre-built data connectors, while cons may include limited customization options.
  10. Hevo Data: Hevo Data is a no-code data pipeline platform that offers real-time data integration and transformation. Key features include support for various data sources, automated schema mapping, and real-time data ingestion. Pros include ease of use and real-time capabilities, while cons may include limited data transformation options.

Top Alternatives to Xplenty

  • Alooma
    Alooma

    Get the power of big data in minutes with Alooma and Amazon Redshift. Simply build your pipelines and map your events using Alooma’s friendly mapping interface. Query, analyze, visualize, and predict now. ...

  • Segment
    Segment

    Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch. ...

  • Talend
    Talend

    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms. ...

  • Airflow
    Airflow

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...

  • Stitch
    Stitch

    Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company. ...

  • Matillion
    Matillion

    It is a modern, browser-based UI, with powerful, push-down ETL/ELT functionality. With a fast setup, you are up and running in minutes. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

Xplenty alternatives & related posts

Alooma logo

Alooma

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Integrate any data source like databases, applications, and any API - with your own Amazon Redshift
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      Segment logo

      Segment

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      PROS OF SEGMENT
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        Easy to scale and maintain 3rd party services
      • 49
        One API
      • 39
        Simple
      • 25
        Multiple integrations
      • 19
        Cleanest API
      • 10
        Easy
      • 9
        Free
      • 8
        Mixpanel Integration
      • 7
        Segment SQL
      • 6
        Flexible
      • 4
        Google Analytics Integration
      • 2
        Salesforce Integration
      • 2
        SQL Access
      • 2
        Clean Integration with Application
      • 1
        Own all your tracking data
      • 1
        Quick setup
      • 1
        Clearbit integration
      • 1
        Beautiful UI
      • 1
        Integrates with Apptimize
      • 1
        Escort
      • 1
        Woopra Integration
      CONS OF SEGMENT
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        Not clear which events/options are integration-specific
      • 1
        Limitations with integration-specific configurations
      • 1
        Client-side events are separated from server-side

      related Segment posts

      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 3.1M views

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Robert Zuber

      Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

      We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

      See more
      Talend logo

      Talend

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      247
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      A single, unified suite for all integration needs
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      PROS OF TALEND
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        CONS OF TALEND
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          Shared insights
          on
          TalendTalendSnapLogicSnapLogic

          SnapLogic Vs Talend: Which one to choose when you have a lot of transformation logic to be used huge volume of data load on everyday basis.

          . better monitor & support . better performance . easy coding

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          Airflow logo

          Airflow

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          A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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          PROS OF AIRFLOW
          • 51
            Features
          • 14
            Task Dependency Management
          • 12
            Beautiful UI
          • 12
            Cluster of workers
          • 10
            Extensibility
          • 6
            Open source
          • 5
            Complex workflows
          • 5
            Python
          • 3
            Good api
          • 3
            Apache project
          • 3
            Custom operators
          • 2
            Dashboard
          CONS OF AIRFLOW
          • 2
            Observability is not great when the DAGs exceed 250
          • 2
            Running it on kubernetes cluster relatively complex
          • 2
            Open source - provides minimum or no support
          • 1
            Logical separation of DAGs is not straight forward

          related Airflow posts

          Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

          Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

          There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

          Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

          Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

          Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

          See more

          We are a young start-up with 2 developers and a team in India looking to choose our next ETL tool. We have a few processes in Azure Data Factory but are looking to switch to a better platform. We were debating Trifacta and Airflow. Or even staying with Azure Data Factory. The use case will be to feed data to front-end APIs.

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          Stitch logo

          Stitch

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          All your data. In your data warehouse. In minutes.
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          PROS OF STITCH
          • 8
            3 minutes to set up
          • 4
            Super simple, great support
          CONS OF STITCH
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            related Stitch posts

            Ankit Sobti

            Looker , Stitch , Amazon Redshift , dbt

            We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

            For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

            See more
            Cyril Duchon-Doris

            Hello, For security and strategic reasons, we are migrating our apps from AWS/Google to a cloud provider with more security certifications and fewer functionalities, named Outscale. So far we have been using Google BigQuery as our data warehouse with ELT workflows (using Stitch and dbt ) and we need to migrate our data ecosystem to this new cloud provider.

            We are setting up a Kubernetes cluster in our new cloud provider for our apps. Regarding the data warehouse, it's not clear if there are advantages/inconvenients about setting it up on kubernetes (apart from having to create node groups and tolerations with more ram/cpu). Also, we are not sure what's the best Open source or on-premise tool to use. The main requirement is that data must remain in the secure cluster, and no external entity (especially US) can have access to it. We have a dev cluster/environment and a production cluster/environment on this cloud.

            Regarding the actual DWH usage - Today we have ~1.5TB in BigQuery in production. We're going to run our initial rests with ~50-100GB of data for our test cluster - Most of our data comes from other databases, so in most cases, we already have replicated sources somewhere, and there are only a handful of collections whose source is directly in the DWH (such as snapshots, some external data we've fetched at some point, google analytics, etc) and needs appropriate level of replication - We are a team of 30-ish people, we do not have critical needs regarding analytics speed, and we do not need real time. We rebuild our DBT models 2-3 times a day and this usually proves enough

            Apart from postgreSQL, I haven't really found open-source or on-premise alternatives for setting up a data warehouse, and running transformations with DBT. There is also the question of data ingestion, I've selected Airbyte and @meltano and I have troubles understanding if one of the 2 is better but Airbytes seems to have a bigger community.

            What do you suggest regarding the data warehouse, and the ELT workflows ? - Kubernetes or not kubernetes ? - Postgresql or something else ? if postgre, what are the important configs you'd have in mind ? - Airbyte/DBT or something else.

            See more
            Matillion logo

            Matillion

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            An ETL Tool for BigData
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            PROS OF MATILLION
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                JavaScript logo

                JavaScript

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                PROS OF JAVASCRIPT
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                  Can be used on frontend/backend
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                  It's everywhere
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                  Lots of great frameworks
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                  Fast
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                  Light weight
                • 425
                  Flexible
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                  You can't get a device today that doesn't run js
                • 286
                  Non-blocking i/o
                • 237
                  Ubiquitousness
                • 191
                  Expressive
                • 55
                  Extended functionality to web pages
                • 49
                  Relatively easy language
                • 46
                  Executed on the client side
                • 30
                  Relatively fast to the end user
                • 25
                  Pure Javascript
                • 21
                  Functional programming
                • 15
                  Async
                • 13
                  Full-stack
                • 12
                  Setup is easy
                • 12
                  Future Language of The Web
                • 12
                  Its everywhere
                • 11
                  Because I love functions
                • 11
                  JavaScript is the New PHP
                • 10
                  Like it or not, JS is part of the web standard
                • 9
                  Expansive community
                • 9
                  Everyone use it
                • 9
                  Can be used in backend, frontend and DB
                • 9
                  Easy
                • 8
                  Most Popular Language in the World
                • 8
                  Powerful
                • 8
                  Can be used both as frontend and backend as well
                • 8
                  For the good parts
                • 8
                  No need to use PHP
                • 8
                  Easy to hire developers
                • 7
                  Agile, packages simple to use
                • 7
                  Love-hate relationship
                • 7
                  Photoshop has 3 JS runtimes built in
                • 7
                  Evolution of C
                • 7
                  It's fun
                • 7
                  Hard not to use
                • 7
                  Versitile
                • 7
                  Its fun and fast
                • 7
                  Nice
                • 7
                  Popularized Class-Less Architecture & Lambdas
                • 7
                  Supports lambdas and closures
                • 6
                  It let's me use Babel & Typescript
                • 6
                  Can be used on frontend/backend/Mobile/create PRO Ui
                • 6
                  1.6K Can be used on frontend/backend
                • 6
                  Client side JS uses the visitors CPU to save Server Res
                • 6
                  Easy to make something
                • 5
                  Clojurescript
                • 5
                  Promise relationship
                • 5
                  Stockholm Syndrome
                • 5
                  Function expressions are useful for callbacks
                • 5
                  Scope manipulation
                • 5
                  Everywhere
                • 5
                  Client processing
                • 5
                  What to add
                • 4
                  Because it is so simple and lightweight
                • 4
                  Only Programming language on browser
                • 1
                  Test
                • 1
                  Hard to learn
                • 1
                  Test2
                • 1
                  Not the best
                • 1
                  Easy to understand
                • 1
                  Subskill #4
                • 1
                  Easy to learn
                • 0
                  Hard 彤
                CONS OF JAVASCRIPT
                • 22
                  A constant moving target, too much churn
                • 20
                  Horribly inconsistent
                • 15
                  Javascript is the New PHP
                • 9
                  No ability to monitor memory utilitization
                • 8
                  Shows Zero output in case of ANY error
                • 7
                  Thinks strange results are better than errors
                • 6
                  Can be ugly
                • 3
                  No GitHub
                • 2
                  Slow

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                Zach Holman

                Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

                But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

                But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

                Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

                See more
                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.2M 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

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                Git logo

                Git

                290.3K
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                6.6K
                Fast, scalable, distributed revision control system
                290.3K
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                PROS OF GIT
                • 1.4K
                  Distributed version control system
                • 1.1K
                  Efficient branching and merging
                • 959
                  Fast
                • 845
                  Open source
                • 726
                  Better than svn
                • 368
                  Great command-line application
                • 306
                  Simple
                • 291
                  Free
                • 232
                  Easy to use
                • 222
                  Does not require server
                • 27
                  Distributed
                • 22
                  Small & Fast
                • 18
                  Feature based workflow
                • 15
                  Staging Area
                • 13
                  Most wide-spread VSC
                • 11
                  Role-based codelines
                • 11
                  Disposable Experimentation
                • 7
                  Frictionless Context Switching
                • 6
                  Data Assurance
                • 5
                  Efficient
                • 4
                  Just awesome
                • 3
                  Github integration
                • 3
                  Easy branching and merging
                • 2
                  Compatible
                • 2
                  Flexible
                • 2
                  Possible to lose history and commits
                • 1
                  Rebase supported natively; reflog; access to plumbing
                • 1
                  Light
                • 1
                  Team Integration
                • 1
                  Fast, scalable, distributed revision control system
                • 1
                  Easy
                • 1
                  Flexible, easy, Safe, and fast
                • 1
                  CLI is great, but the GUI tools are awesome
                • 1
                  It's what you do
                • 0
                  Phinx
                CONS OF GIT
                • 16
                  Hard to learn
                • 11
                  Inconsistent command line interface
                • 9
                  Easy to lose uncommitted work
                • 7
                  Worst documentation ever possibly made
                • 5
                  Awful merge handling
                • 3
                  Unexistent preventive security flows
                • 3
                  Rebase hell
                • 2
                  When --force is disabled, cannot rebase
                • 2
                  Ironically even die-hard supporters screw up badly
                • 1
                  Doesn't scale for big data

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                Simon Reymann
                Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M 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 · 8.3M 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.

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