Alternatives to AWS Glue logo

Alternatives to AWS Glue

AWS Data Pipeline, Airflow, Apache Spark, Talend, and Alooma are the most popular alternatives and competitors to AWS Glue.
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What is AWS Glue and what are its top alternatives?

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
AWS Glue is a tool in the Big Data Tools category of a tech stack.

Top Alternatives to AWS Glue

  • AWS Data Pipeline
    AWS Data Pipeline

    AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Databricks
    Databricks

    Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications. ...

  • 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. ...

AWS Glue alternatives & related posts

AWS Data Pipeline logo

AWS Data Pipeline

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Process and move data between different AWS compute and storage services
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PROS OF AWS DATA PIPELINE
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    Easy to create DAG and execute it
CONS OF AWS DATA PIPELINE
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    related AWS Data Pipeline posts

    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
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      Features
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      Task Dependency Management
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      Beautiful UI
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      Cluster of workers
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      Extensibility
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      Open source
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      Complex workflows
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      Python
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      Good api
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      Apache project
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      Custom operators
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      Dashboard
    CONS OF AIRFLOW
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      Observability is not great when the DAGs exceed 250
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      Running it on kubernetes cluster relatively complex
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      Open source - provides minimum or no support
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      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.

    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
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      Open-source
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      Fast and Flexible
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      One platform for every big data problem
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      Great for distributed SQL like applications
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      Easy to install and to use
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      Works well for most Datascience usecases
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      Interactive Query
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      Machine learning libratimery, Streaming in real
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      In memory Computation
    CONS OF APACHE SPARK
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      Speed

    related Apache Spark posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M 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

    See more
    Talend logo

    Talend

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    A single, unified suite for all integration needs
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    PROS OF TALEND
      Be the first to leave a pro
      CONS OF TALEND
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        related Talend posts

        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

        See more
        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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        PROS OF ALOOMA
          Be the first to leave a pro
          CONS OF ALOOMA
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            related Alooma posts

            Databricks logo

            Databricks

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            A unified analytics platform, powered by Apache Spark
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            PROS OF DATABRICKS
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              Best Performances on large datasets
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              True lakehouse architecture
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              Scalability
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              Databricks doesn't get access to your data
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              Usage Based Billing
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              Security
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              Data stays in your cloud account
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              Multicloud
            CONS OF DATABRICKS
              Be the first to leave a con

              related Databricks posts

              Jan Vlnas
              Developer Advocate at Superface · | 5 upvotes · 429.2K views

              From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

              I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

              Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

              See more
              Vamshi Krishna
              Data Engineer at Tata Consultancy Services · | 4 upvotes · 245.3K views

              I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

              See more
              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
              • 896
                Fast
              • 745
                Light weight
              • 425
                Flexible
              • 392
                You can't get a device today that doesn't run js
              • 286
                Non-blocking i/o
              • 236
                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
                Its everywhere
              • 12
                Future Language of The Web
              • 11
                JavaScript is the New PHP
              • 11
                Because I love functions
              • 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
                Easy to hire developers
              • 8
                No need to use PHP
              • 8
                For the good parts
              • 8
                Can be used both as frontend and backend as well
              • 8
                Powerful
              • 8
                Most Popular Language in the World
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                Popularized Class-Less Architecture & Lambdas
              • 7
                It's fun
              • 7
                Nice
              • 7
                Versitile
              • 7
                Hard not to use
              • 7
                Its fun and fast
              • 7
                Agile, packages simple to use
              • 7
                Supports lambdas and closures
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                Love-hate relationship
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                Photoshop has 3 JS runtimes built in
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                Evolution of C
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                1.6K Can be used on frontend/backend
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                Client side JS uses the visitors CPU to save Server Res
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                It let's me use Babel & Typescript
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                Easy to make something
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                Can be used on frontend/backend/Mobile/create PRO Ui
              • 5
                Promise relationship
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                Stockholm Syndrome
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                Function expressions are useful for callbacks
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                Scope manipulation
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                Everywhere
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                Client processing
              • 5
                Clojurescript
              • 5
                What to add
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                Because it is so simple and lightweight
              • 4
                Only Programming language on browser
              • 1
                Test2
              • 1
                Easy to learn
              • 1
                Easy to understand
              • 1
                Not the best
              • 1
                Hard to learn
              • 1
                Subskill #4
              • 1
                Test
              • 0
                Hard 彤
              CONS OF JAVASCRIPT
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                A constant moving target, too much churn
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                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

              related JavaScript posts

              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 · 10.1M 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
              Git logo

              Git

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              Fast, scalable, distributed revision control system
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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
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                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

              related Git posts

              Simon Reymann
              Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.2M 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.

              See more