Alternatives to Apache Drill logo

Alternatives to Apache Drill

Presto, Apache Spark, Apache Calcite, Apache Impala, and Druid are the most popular alternatives and competitors to Apache Drill.
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What is Apache Drill and what are its top alternatives?

Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
Apache Drill is a tool in the Database Tools category of a tech stack.

Top Alternatives to Apache Drill

  • Presto
    Presto

    Distributed SQL Query Engine for Big Data

  • 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. ...

  • Apache Calcite
    Apache Calcite

    It is an open source framework for building databases and data management systems. It includes a SQL parser, an API for building expressions in relational algebra, and a query planning engine ...

  • Apache Impala
    Apache Impala

    Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time. ...

  • Druid
    Druid

    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations. ...

  • 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. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

Apache Drill alternatives & related posts

Presto logo

Presto

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1K
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Distributed SQL Query Engine for Big Data
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1K
+ 1
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PROS OF PRESTO
  • 18
    Works directly on files in s3 (no ETL)
  • 13
    Open-source
  • 12
    Join multiple databases
  • 10
    Scalable
  • 7
    Gets ready in minutes
  • 6
    MPP
CONS OF PRESTO
    Be the first to leave a con

    related Presto posts

    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
    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
    Apache Spark logo

    Apache Spark

    2.9K
    3.5K
    140
    Fast and general engine for large-scale data processing
    2.9K
    3.5K
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    140
    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

    related Apache Spark posts

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

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

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

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

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

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

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

    See more
    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
    Apache Calcite logo

    Apache Calcite

    11
    29
    0
    A dynamic data management framework
    11
    29
    + 1
    0
    PROS OF APACHE CALCITE
      Be the first to leave a pro
      CONS OF APACHE CALCITE
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        related Apache Calcite posts

        Apache Impala logo

        Apache Impala

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        Real-time Query for Hadoop
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        PROS OF APACHE IMPALA
        • 11
          Super fast
        • 1
          Massively Parallel Processing
        • 1
          Load Balancing
        • 1
          Replication
        • 1
          Scalability
        • 1
          Distributed
        • 1
          High Performance
        • 1
          Open Sourse
        CONS OF APACHE IMPALA
          Be the first to leave a con

          related Apache Impala posts

          I have been working on a Java application to demonstrate the latency for the select/insert/update operations on KUDU storage using Apache Kudu API - Java based client. I have a few queries about using Apache Kudu API

          1. Do we have JDBC wrapper to use Apache Kudu API for getting connection to Kudu masters with connection pool mechanism and all DB operations?

          2. Does Apache KuduAPI supports order by, group by, and aggregate functions? if yes, how to implement these functions using Kudu APIs.

          3. How can we add kudu predicates to Kudu update operation? if yes, how?

          4. Does Apache Kudu API supports batch insertion (execute the Kudu Insert for multiple rows at one go instead of row by row)? (like Kudusession.apply(List);)

          5. Does Apache Kudu API support join on tables?

          6. which tool is preferred over others (Apache Impala /Kudu API) for read and update/insert DB operations?

          See more
          Druid logo

          Druid

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          Fast column-oriented distributed data store
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          32
          PROS OF DRUID
          • 15
            Real Time Aggregations
          • 6
            Batch and Real-Time Ingestion
          • 5
            OLAP
          • 3
            OLAP + OLTP
          • 2
            Combining stream and historical analytics
          • 1
            OLTP
          CONS OF DRUID
          • 3
            Limited sql support
          • 2
            Joins are not supported well
          • 1
            Complexity

          related Druid posts

          Shared insights
          on
          DruidDruidMongoDBMongoDB

          My background is in Data analytics in the telecom domain. Have to build the database for analyzing large volumes of CDR data so far the data are maintained in a file server and the application queries data from the files. It's consuming a lot of resources queries are taking time so now I am asked to come up with the approach. I planned to rewrite the app, so which database needs to be used. I am confused between MongoDB and Druid.

          So please do advise me on picking from these two and why?

          See more

          My process is like this: I would get data once a month, either from Google BigQuery or as parquet files from Azure Blob Storage. I have a script that does some cleaning and then stores the result as partitioned parquet files because the following process cannot handle loading all data to memory.

          The next process is making a heavy computation in a parallel fashion (per partition), and storing 3 intermediate versions as parquet files: two used for statistics, and the third will be filtered and create the final files.

          I make a report based on the two files in Jupyter notebook and convert it to HTML.

          • Everything is done with vanilla python and Pandas.
          • sometimes I may get a different format of data
          • cloud service is Microsoft Azure.

          What I'm considering is the following:

          Get the data with Kafka or with native python, do the first processing, and store data in Druid, the second processing will be done with Apache Spark getting data from apache druid.

          the intermediate states can be stored in druid too. and visualization would be with apache superset.

          See more
          JavaScript logo

          JavaScript

          352.5K
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          Lightweight, interpreted, object-oriented language with first-class functions
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          PROS OF JAVASCRIPT
          • 1.7K
            Can be used on frontend/backend
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            It's everywhere
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            Lots of great frameworks
          • 897
            Fast
          • 745
            Light weight
          • 425
            Flexible
          • 392
            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

          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 · 10.9M views

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

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

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

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

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

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

          See more
          Git logo

          Git

          291.4K
          174.9K
          6.6K
          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
          • 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

          related Git posts

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

          Our whole DevOps stack consists of the following tools:

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

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

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

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

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

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

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

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

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

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

          See more
          GitHub logo

          GitHub

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          PROS OF GITHUB
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            Easy setup
          • 504
            Issue tracker
          • 486
            Great community
          • 483
            Remote team collaboration
          • 451
            Great way to share
          • 442
            Pull request and features planning
          • 147
            Just works
          • 132
            Integrated in many tools
          • 121
            Free Public Repos
          • 116
            Github Gists
          • 112
            Github pages
          • 83
            Easy to find repos
          • 62
            Open source
          • 60
            It's free
          • 60
            Easy to find projects
          • 56
            Network effect
          • 49
            Extensive API
          • 43
            Organizations
          • 42
            Branching
          • 34
            Developer Profiles
          • 32
            Git Powered Wikis
          • 30
            Great for collaboration
          • 24
            It's fun
          • 23
            Clean interface and good integrations
          • 22
            Community SDK involvement
          • 20
            Learn from others source code
          • 16
            Because: Git
          • 14
            It integrates directly with Azure
          • 10
            Standard in Open Source collab
          • 10
            Newsfeed
          • 8
            It integrates directly with Hipchat
          • 8
            Fast
          • 8
            Beautiful user experience
          • 7
            Easy to discover new code libraries
          • 6
            Smooth integration
          • 6
            Cloud SCM
          • 6
            Nice API
          • 6
            Graphs
          • 6
            Integrations
          • 6
            It's awesome
          • 5
            Quick Onboarding
          • 5
            Reliable
          • 5
            Remarkable uptime
          • 5
            CI Integration
          • 5
            Hands down best online Git service available
          • 4
            Uses GIT
          • 4
            Version Control
          • 4
            Simple but powerful
          • 4
            Unlimited Public Repos at no cost
          • 4
            Free HTML hosting
          • 4
            Security options
          • 4
            Loved by developers
          • 4
            Easy to use and collaborate with others
          • 3
            Ci
          • 3
            IAM
          • 3
            Nice to use
          • 3
            Easy deployment via SSH
          • 2
            Easy to use
          • 2
            Leads the copycats
          • 2
            All in one development service
          • 2
            Free private repos
          • 2
            Free HTML hostings
          • 2
            Easy and efficient maintainance of the projects
          • 2
            Beautiful
          • 2
            Easy source control and everything is backed up
          • 2
            IAM integration
          • 2
            Very Easy to Use
          • 2
            Good tools support
          • 2
            Issues tracker
          • 2
            Never dethroned
          • 2
            Self Hosted
          • 1
            Dasf
          • 1
            Profound
          CONS OF GITHUB
          • 54
            Owned by micrcosoft
          • 38
            Expensive for lone developers that want private repos
          • 15
            Relatively slow product/feature release cadence
          • 10
            API scoping could be better
          • 9
            Only 3 collaborators for private repos
          • 4
            Limited featureset for issue management
          • 3
            Does not have a graph for showing history like git lens
          • 2
            GitHub Packages does not support SNAPSHOT versions
          • 1
            No multilingual interface
          • 1
            Takes a long time to commit
          • 1
            Expensive

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